#  ChemReasoner: Heuristic Search over a Large Language Model’s Knowledge
Space using Quantum-Chemical Feedback

Henry W. Sprueill  Carl Edwards  Khushbu Agarwal  Mariefel V. Olarte  Udishnu
Sanyal  Conrad Johnston  Hongbin Liu  Heng Ji  Sutanay Choudhury

###### Abstract

The discovery of new catalysts is essential for the design of new and more
efficient chemical processes in order to transition to a sustainable future.
We introduce an AI-guided computational screening framework unifying
linguistic reasoning with quantum-chemistry based feedback from 3D atomistic
representations. Our approach formulates catalyst discovery as an uncertain
environment where an agent actively searches for highly effective catalysts
via the iterative combination of large language model (LLM)-derived hypotheses
and atomistic graph neural network (GNN)-derived feedback. Identified
catalysts in intermediate search steps undergo structural evaluation based on
spatial orientation, reaction pathways, and stability. Scoring functions based
on adsorption energies and barriers steer the exploration in the LLM’s
knowledge space toward energetically favorable, high-efficiency catalysts. We
introduce planning methods that automatically guide the exploration without
human input, providing competitive performance against expert-enumerated
chemical descriptor-based implementations. By integrating language-guided
reasoning with computational chemistry feedback, our work pioneers AI-
accelerated, trustworthy catalyst discovery.

Machine Learning, ICML

mOCarl[#1]

##  1 Introduction

The discovery of new catalysts requires one to identify the optimal
combination of chemical descriptors (or properties), which are often only
empirically understood. Chemists actively reason to mentally search through
reactants, catalysts, and operating conditions that enable more energy-
efficient chemical conversions. As proposed by seminal work on descriptor-
based catalyst search (Nørskov et al., 2011), linking microscopic surface
properties to macroscopic catalytic performance via chemical descriptors is
key to accelerating such hypothesis generation.

Large Language Models (LLMs) (Wei et al., 2022; Ouyang et al., 2022; Taylor et
al., 2022; Lai et al., 2023; OpenAI, 2023) now bring within reach the
realization of such data-driven autonomous search to accelerate scientific
discovery. Our work aims to enhance natural language reasoning capabilities
with quantum-chemical feedback to discover optimal catalysts for target
reactions.

Figure 1: ChemReasoner successively “thinks” in terms of different constraints
and factors, which are based on scientific principles and narrow down the set
of possible candidates. It accomplishes that by prompting a language model
with different combinations of chemical descriptors, yielding a tree-
structured space of queries and potential candidates, and returns the optimal
answer via efficient exploration of the search-space. ChemReasoner uses
automated planning, based on previous reasoning, to initiate the exploration
and guides it via a reward obtained from the exploration process to prune
unpromising actions. We currently use “adsorption energy”, a key measure of
reactivity as the reward function.

The Challenge However, reasoning about complex catalytic processes requires
compositionality across multiple modalities, extending beyond the capabilities
of existing language models. These include scientific concepts from literature
and property prediction with 3D atomistic configurations. Determining the best
catalyst is a multi-step process requiring reasoning about multiple
macroscopic properties. The first step involves identifying an optimal set of
chemical descriptors (e.g. “resistance to poisoning”, “porosity”) which are
relevant to the reaction in question. Formally, given a set of these important
descriptors $\mathbf{P},|\mathbf{P}|=n$ , we want to identify the optimal
subset $\mathbf{R\subset\mathbf{P}},|\mathbf{R}|=r$ to consider when
suggesting new catalysts. This yields $P^{n}_{r}=\frac{n!}{(n-r)!}$ possible
permutations to reason over. The amount of reasoning required scales very
quickly, necessitating an external reasoner: an LLM. Using its knowledge of
scientific concepts, the LLM both proposes important properties and selects
the best possible catalysts (from all possible catalysts) that have these
properties. Pruning this large space of candidate catalysts requires reasoning
about the complex microscopic interactions that occur between atomistic
structures in 3D space based on macroscopic properties. Further, while simple
reactions can be assessed via adsorption energies of 3D chemical structures,
complex reactions demand consideration of multi-step reaction pathways and
selectivity (Unsleber et al., 2023).

Technical approach To solve this challenge, we propose a framework that
combines LLM-driven heuristic search for catalyst discovery with structure-
based scoring from atomistic graph neural networks (GNNs) trained from quantum
chemistry simulations for guidance (Fig. 1). This framework formulates
catalyst discovery as an uncertain environment where an agent (the LLM)
pursues energetically favorable catalysts based on computational chemistry
feedback. In each search step, the agent plans its actions(Huang et al., 2022;
Hao et al., 2023) by 1) automatically identifying the optimal set of
properties to consider, 2) generating new search prompts based on the
identified properties, and 3) executing the prompts using sophisticated
instruction following (Ouyang et al., 2022). Catalyst candidates identified in
each step of the search are transformed into 3D atomistic representations of
the catalyst-adsorbate structure (Zitnick et al., 2020). These representations
enable evaluation via structural evaluation – including spatial orientation,
energy barriers overreaction pathways, and stability – yielding a reward for
catalyst suitability. This reward drives the LLM towards catalysts which
enable reactions with minimal external energy, a crucial step for developing
environmentally friendly industrial processes.

In this work, we make the following key contributions:

  1. 1.

We introduce ChemReasoner: a novel hypothesis generation and testing framework
unifying heuristic search over an LLM’s knowledge-space with quantum
chemistry-guided feedback. This enables natural language-based reasoning for
catalyst discovery with stronger domain guarantees obtained from computational
chemistry methods.

  2. 2.

We demonstrate the decisive impact of planning methods in automatically
navigating chemical search spaces over SOTA LLM-based implementation. Our LLM-
planned approach with zero human input (ChemReasoner-Planner) surpasses search
guided by expert-selected chemical descriptors (ChemReasoner-Expert) for two
out of three categories in our evaluation benchmark.

  3. 3.

Third, and uniquely, we establish domain-grounding of language models via
quantum chemical property feedback. We go beyond screening catalysts on
adsorption energies alone and propose a methodology to reason in terms of
reaction pathways and energy barriers.

Our work pioneers an AI-guided approach to computational catalyst screening
and discovery. To facilitate community adoption and advancement of this
extremely interdisciplinary and compute-intensive research, we commit to
releasing all our datasets. This includes query benchmarks from catalysis
experts, multi-modal provenance trails, over 700,000+ atomistic trajectory
computations, and validation from density functional theory calculations.

##  2 Background and Related Work

###  2.1 Catalysis

Catalysts accelerate chemical reactions by lowering reaction barriers, without
being consumed in the process. Heterogeneous catalysis, wherein the catalyst
is in a different phase than the reactants and products, is widely used in
industrial chemical processes (Dumesic et al., 2008). Developing novel
heterogeneous catalysts with high activity and selectivity is essential to
design energy efficient chemical process that paves a way towards
sustainability (Zitnick et al., 2020; Hu & Yip, 2021; Mukhtar et al., 2022).

In heterogeneous catalysis, gases or liquids interact with a solid catalyst
surface to enable a reaction (Greeley et al., 2002). This overall process
consists of three elementary steps: 1) Adsorption - reactant molecules bind to
the catalyst surface; 2) Surface reaction - adsorbed molecules react with each
other to generate product(s); 3) Desorption - product(s) molecules desorb from
the catalyst surface.

Adsorption energy of a reactant, reflecting its binding strength on a specific
catalyst surface, is often identified as one of the key criterion for the
activity of that catalyst. Fundamentally, adsorption energy of a reactant can
be tuned by changing microscopic properties such as surface structure (crystal
facet) or electronic configuration. Within catalysis, an overarching goal is
to link these microscopic descriptors to macroscopic catalytic performance
metrics to allow computation descriptor-based catalyst search (Nørskov et al.,
2011). However, modeling complex catalyst surface reactions requires going
beyond just adsorption descriptors (Xi et al., 2002). Interactions between
reaction intermediates and competition between multiple possible reaction
pathways must be considered as well (Kattel et al., 2017; Schwaller et al.,
2019; Chen & Jung, 2022; Unsleber et al., 2023). With so many interactions in
mind, we pursue a goal of developing models to reason compositionally about
descriptors, structures, and pathways to generate high-quality hypotheses for
potential catalysts, applied to the production of sustainable fuels.

###  2.2 LLMs for Chemistry

LLMs for chemistry can be divided into two categories: domain-specific models
and adapted general-purpose models. Multimodal, domain-specific molecule-
language models have recently emerged to target a number of problems in the
chemistry domain (Edwards et al., 2021; Vall et al., 2021; Zeng et al., 2022;
Xu & Wang, 2022; Su et al., 2022; Edwards et al., 2022a). For brevity, we
discuss them further in Appendix D. On the other hand, general-domain LLMs
such as GPT-4 (OpenAI, 2023) have been adopted in an agent-based approach to
interface with chemistry-specific tools, allowing information gathering and
hypothesis generation (Boiko et al., 2023; Bran et al., 2023). While this work
is exciting, it differs from our approach, where we employ an overarching,
domain-specific reward function to probe the LLM’s knowledge, enabling
stronger, simulation-backed guarantees about complex scientific reasoning. We
note that our method builds upon Sprueill et al. (2023). Unlike their
approach, which uses LLM-computed rewards, we integrate true computational
chemistry-based rewards to guide the model. Further, we integrate a context-
aware planner into our search algorithm to automatically guide the search.

##  3 System and Methods

Figure 2: Illustration of ChemReasoner search process (best viewed in color):
The initial question generates base candidates, which are iteratively refined
by adding an optimal set of constraints to the query and producing a new set
of actions (or prompts) to explore the LLMs internal knowledge space. The
optimal action set is chosen by 1) sampling from expert specified action
space, or 2) automated generated by a planner component as illustrated in
Figure 3. We describe the resultant structure shown as the “search tree” and
each node in the tree represents a set of 3-tuple of (question, answer,
reward). We refer to the initial query as a “root node.” Figure 3: Planner-
guided search action generation (best viewed in color): Given a query state
defined by a question (shown in top-left) and the set of corresponding
answers, the LLM is used as an optimizer to generate a “plan” for the next
query. The LLM performs internal reasoning as shown in orange boxes. It
accounts for the complete context from root query up to the current query node
and generates a “query plan” with the attributes “catalyst type”, “inclusion
criteria”, “exclusion criteria” and “relationship to current candidate list”.
Figure 4: Illustration of planner guided heuristic search (best viewed in
color) described in section 3.2 below. Note the systematic expansion of the
query plan in the orange boxes (left column). The middle column shows
illustration of 3D atomistic structures generated from chemical symbols. Each
3D structure is processed by a reward function that involves geometry
relaxation and potentially deriving approximations of energy barriers in
reaction pathways (right column).

This section elaborates algorithmic components of the architecture described
in Algorithm 1. It can be broadly divided into two components: 1) LLM-planned
and guided heuristic search over chemical space and 2) quantum-chemical
feedback from graph neural network (GNN) models trained from density
functional theory (DFT) simulations.

###  3.1 Heuristic Search

The goal of heuristic search is to answer a user-specified natural language
query by systematically exploring candidates from different regions in
chemical space. This is accomplished by taking the original query (or prompt)
and corresponding LLM answers, and systematically applying different screening
criteria to iteratively contextualize the LLM prompts and answers into a
narrowed region of the chemical space. This process is illustrated in Figure
2. In this paper, we employ the beam search method (Rubin & Reddy, 1977) for
heuristic search.

Require: LLM, initial prompt $P_{0}$, number of children to generate $N$,
number of children to keep $M$, target depth $d$
Initialize tree $T$ with nodes $P$ and edges $(P,a_{j})$, scalar $\gamma$,
stored values $p(P,a_{j})$, and reward function $R$.
$root(T)\leftarrow P_{0}$
$P_{\mathrm{curr}}\leftarrow[root(T)]$
$P_{\mathrm{next}}\leftarrow[]$
for  _$t=1,\dots,d$_ do

for  _$P_{i}\in P_{\mathrm{curr}}$_ do

$\mathcal{A}_{i},p=\mathcal{P}(P_{i},p)$$\rhd$ Get action set and priors
for
_$a_{j}\in\mathop{\mathrm{arg\,top}\,N}_{a_{k}\in\mathcal{A}_{i}}\left(p(a_{j})\right)$_
do

$P_{j}^{*}\leftarrow a_{j}(P_{i})$ $\rhd$ Apply action $a_{j}$
$T$.append$(P_{j}^{*})$
$P_{\mathrm{next}}$.append$P_{j}^{*}$

end for

$P_{\mathrm{curr}}\leftarrow\mathop{\mathrm{arg\,top}\,M}_{P_{j}\in
P_{\mathrm{next}}}(R(P_{j}))$ $\rhd$Calculate reward of LLM answer and
downselect

end for

end for

return $\arg\max_{P_{j}\in T}(R(P_{j}))$

Algorithm 1 Description of the ChemReasoner framework.

Formally, our goal is to search through chemical descriptors to design the
optimal prompt, which leads the LLM to return the best candidate catalysts for
a catalysis query. Starting with a general prompt $P_{0}$, we use a set of
actions to modify the prompt to improve the LLM output with respect to a
reward function, $R$. Notably, ChemReasoner-Planner generates its own action
space $\mathcal{A}$ (Figure 3).

Definitions: We define the Search Tree as a hierarchical tree consisting of
(prompt, answer, and reward) nodes. Each node in this tree represents a state
in the search space (henceforth referred to as search state or query state).
Nodes are linked if an action $a\in\mathcal{A}$ modifies the prompt of one
into the other. We denote a path from root to leaf node a Reasoning Pathway.

Following Sprueill et al. (2023), each LLM prompt has an inner structured
representation that consists of 1) a natural language question, 2) an
inclusion-exclusion list that includes or excludes specific chemical
descriptors for target catalysts and 3) a relational operator that describes
how the search can be shifted from the previous query’s candidate catalysts to
a different region in chemical space. We employ three relational operators: 1)
similar-to, 2) subclass-of and 3) dissimilar-to. Each LLM answer represents a
set of candidate catalysts, and each candidate is scored using a reward
function. Starting the search with the root node, the search algorithm
progresses by expanding each node into a set of children nodes with actions
$a$. Each layer of the search tree is pruned based on the reward of the nodes.
Finally, when maximum search depth is achieved, we select the node with the
highest reward as the overall answer to the initial prompt.

###  3.2 Planner-guided Search

Our planner component is responsible for systematically expanding the search
by contextually determining viable actions. Specifically, the planner
conditions action selection on the full sequence of previous queries and
catalysts discoveries traced from the root of the search tree. This contextual
grounding enables automatically constraining subsequent search directions in a
scientifically coherent way.

Consider any node in the search tree where the planner generates the next
query to execute (shown via orange box on top left in Figure 4). Next, the LLM
executes the query and retrieves a set of candidate catalysts (such as Cu, Pd
etc.). Each of these candidates are then transformed into a 3D atomistic
representation and evaluated by a reward function described in sections 3.3
and 3.4. All candidates at a given depth in the search tree are collected and
only a subset are chosen for expanding in the next iteration. The process
repeats iteratively (shown via rows in above diagram) until the maximum tree
depth is reached.

Overall, by leveraging language models to contextually expand search,
ChemReasoner-Planner balances exploration with producing interpretable,
scientifically-grounded reasoning paths. See section E for a complete trace of
a planner guided search.

Figure 5:  Illustration of reaction pathways corresponding to CO2 conversion
to methanol for two different catalysts (red and blue bars shows the energy of
different intermediates). The energy barrier (difference between lower energy
state and higher energy state described as hill climbing in the text)
associated with these catalysts is shown by red and blue arrow.

###  3.3 Reward via Structure Optimization and Energy Prediction

Each reward function returns a real number as a measure of the catalyst’s
goodness (higher is better) for a given input question. In this work, we
implement two reward functions with different levels of complexity.

Adsorption energy-based reward: This function returns the adsorption energy of
the most stable binding configuration of the catalyst as the reward. The
computation begins with translating the symbolic representation of the
catalysts (such as “Platinum”) and adsorbates (e.g., “CO”) into a 3D atomistic
structure (Fig. 4 right). The stability and energy of a catalyst’s atomic
structure directly impacts its catalytic activity and selectivity. Therefore,
we compute the most stable configuration for the catalyst-adsorbate pair and
use its adsorption energy as a measure of the reward (Section A). The
optimization process, also known as the relaxation process, iteratively
relaxes the atomic positions of the 3D structure until an energy minimum is
found. A GNN (Gasteiger et al., 2021) is then used to calculate the adsorption
energy from this state.

Reaction pathway-based reward: This function measures the goodness of a
catalyst considering multiple reaction pathways and intermediate steps. We
initially obtained reaction pathways from the LLM. Given each reaction
pathway, it computes the energy functions for every intermediate step. Figure
5 shows two instances of the same reaction pathway for two different
catalysts. As the figure shows, proceeding from one reaction step to another
requires different amounts of energy which depends on the catalyst.
Intuitively, moving from a lower-energy state to higher-one can be viewed as a
“hill-climbing” in the energy landscape (indicated by the red and blue arrow
for two different catalysts) and we formulate a function that would assign the
highest reward to pathways with the smallest hills to climb (Eq. 1).
$\mathrm{ads}_{t}$ is the intermediate at step $t$ of the reaction and
$E_{\mathrm{ads}_{t}}$ is energy of adsorption energy of $\mathrm{ads}_{t}$ on
some catalyst. The overall reaction-based reward function for the top-$k$
catalysts is therefore,

$r(c)=-\min_{p\in\mathrm{Paths}}\left(\max_{\mathrm{ads}_{t}\in
p}\left(E_{\mathrm{ads}_{t}}-E_{\mathrm{ads}_{t-1}}\right)\right).$ (1)

##  4 Experiments

| OpenCatalyst | BioFuels |  CO2-Conversion
---|---|---|---
| GPT-4 | GPT-3.5 | GPT-4 | GPT-3.5 | GPT-4 | GPT-3.5
Chain-of-Thought | 0.37 | 0.66 | 2.08 | 2.10 | -0.62 | -0.54
Self Consistency | 0.73 | 0.76 | 2.08 | 2.12 | -0.54 | -0.36
ChemReasoner-Expert | 1.90 | 2.11 | 3.90 | 3.79 | 0.45 | 0.78
ChemReasoner-Planner | 2.36 | 2.16 | 4.15 | 3.29 | 0.01 | 0.49
Table 1: Final reward values of best recommended catalyst for each search
variant. Our ChemReasoner methods both significantly outperforms the GPT-4
baseline. Larger numbers are better and reflect GNN-predicted adsorption or
reaction pathway-based rewards.

We conduct experiments to evaluate if a system using LLM-guided heuristic
search with quantum-chemistry feedback can discover more novel and effective
catalysts than state-of-the-art LLMs alone. Our experiments focus on three key
research questions for enabling such a system.

  1. RQ1.

Quantification of performance improvement: Does heuristic-search guided by
quantum-chemical feedback produce better catalyst candidates over querying
state-of-the-art LLMs?

  2. RQ2.

Characterization of key components: What are the key parameters that control
the computational complexity-system performance trade-off?

  3. RQ3.

LLM Hypothesis testing: How do we verify hypotheses generated by ChemReasoner
using domain knowledge? Which areas need further attention to make
ChemReasoner’s computational screening more accurate and interpretable?

###  4.1 Experimental setup

Dataset We conduct our experiments on an augmented version of a chemistry-
focused reasoning query benchmark originally proposed in (Sprueill et al.,
2023) containing 145 queries split between 3 general categories: OpenCatalyst,
BioFuels, and CO2-Fuel. We adopt queries from Sprueill et al. (2023) for the
first two categories and further enhance the dataset with the CO2-Fuel subset.
OpenCatalyst is compiled from the set of adsorbates from the Open Catalyst
Project 2020 dataset (Chanussot et al., 2010; Zitnick et al., 2020); it
requires suggesting catalysts for the strong adsorption of each adsorbate (86
queries). Second, BioFuels is targeted at catalyst discovery for biofuel
development (39 queries). These queries have been modified to target metallic
catalysts, which is necessary for our reward calculation. Finally, we
specifically target the conversion of CO2 to methanol and ethanol (20
queries), a platform molecule, which can be used as feedstocks to produce
fuels and chemicals for achieving a net-zero objective (Ling et al., 2023;
Mondal & Yadav, 2021). See section B for details on queries in the dataset.

System Implementation LLMs in our experimental setup included OpenAI GPT-3.5
and GPT-4. Although we initially benchmarked with LLama2 (Touvron et al.,
2023), we found that its instruction-following capabilities in this domain
were too limited to allow evaluation. For our GNN reward model, the GemNet-dT
model (Gasteiger et al., 2021) from the OpenCatalysis project was utilized.
Runtime configurations and inference scaling performance for each of these
models are provided in section C.6. Inferences for both OpenAI models were
executed in parallel using asynchronous execution features. The GNN inferences
were run on a single GPU on a DGX2/V100 and A100 systems.

##  5 Towards Explainable Reasoning from Chemical Feedback

###  5.1 Comparing Reasoning Approaches

We evaluate two different variations of ChemReasoner. ChemReasoner-Expert is
an implementation in which the action space is defined by catalysis experts.
These actions (relation-operators and descriptors) are:

  1. 1.

Inclusion criteria: high activity, high selectivity, low cost, novelty, low
toxicity, high binding energy, high conversion, high availability.

  2. 2.

Exclusion criteria: low activity, low stability, low selectivity, low binding
energy, high cost, high toxicity, low dispersion, low porosity, high scarcity,
low conversion.

  3. 3.

Catalyst type: metallic catalysts, monometallic catalysts, bimetallic
catalysts, trimetallic catalysts.

  4. 4.

Relationship to previous candidate set: include elements that are different
from, include elements similar to, introduce new elements to, include elements
from.

These actions are sampled with uniform probability, without using the same
criteria twice. On the contrary, ChemReasoner-Planner uses LLM-suggested
actions for expanding the search space which do not require any expert
specification.

Better LLM translates into Search Efficiency: As Table 1 shows, both
implementations of ChemReasoner significantly outperforms the GPT-4 baseline.
This is also visualized by Figure 7 (section C.4). Specifically, ChemReasoner-
Planner coupled with GPT-4 performs best for the OpenCatalysis and Biofuels
query categories, whereas ChemReasoner-Expert performs best for CO2-Conversion
queries. As noted in Section 5.4 and shown in Table 2, the top-1 prediction of
ChemReasoner-Expert has high similarity with the current commercial catalyst
for methanol synthesis (Etim et al., 2020). We also computed the average depth
of the node containing the best answer for both variants of ChemReasoner
(Table 4). Lower average depth would indicate that our system is finding the
best results more efficiently, and we observe that using GPT-4 leads to a
reduction of 11.28% in the average search depth. The impact is more pronounced
for ChemReasoner-Expert than ChemReasoner-Planner, which already obtains
performance boost through the algorithmic contribution of planning.

LLM’s alignment with Reward function is key: The strong performance of the
ChemReasoner-Expert on CO2-Conversion queries is noteworthy, especially
considering it is based on GPT-3.5-turbo. We hypothesize that the performance
is related to a complex reward function. For queries associated with
adsorption energy based reward function (OpenCatalyst and Biofuels), even if
the query does not explicitly mention adsorption energy as a target factor,
the LLM’s notion of a good catalyst (generally one with low cost, high
selectivity etc.) typically aligns with lower adsorption energy (higher
reward) profiles. Therefore, the planner effectively uses the LLM as an
optimizing function for searching towards energetically favorable catalysts.
However, LLMs notion of a good catalyst may not directly align or strongly
correlate with the complex reaction pathway-based reward function associated
with CO2 conversion. In general, it suggests that fine-tuning the LLM using a
methodology similar to RLHF (Ouyang et al., 2022) may be a promising path for
downstream tasks with complex reward functions.

###  5.2 Performance characterization of Key Components

ChemReasoner performs a significantly large number of LLM and GNN inferences
that influence both its performance and throughput. The following factors
control the computational complexity of the overall execution.

LLM Inference and Tree Search The runtime of ChemReasoner is $O(N_{tree})$,
where $N_{tree}$ is the number of nodes in the search tree. $N_{tree}$
increases exponentially with the maximum depth (set to 5) and the branching
factor for expanding each node in the search tree, denoted as $N_{actions}$
(set to 8). We use a beam width (set to 6) parameter to control the number of
nodes that are expanded in each iteration of the search. ChemReasoner performs
LLM inferences for each node in the search tree to 1) plan actions, 2) suggest
candidate catalysts, and 3) transform LLM answers into atomistic structures.
It is important to note the strong intra-node and inter-node dependencies
between these LLM queries, as the output from one query is fed into the next
ones. This dependency exists both within a node and to its children in the
search tree. Therefore, robustness of LLM instruction-following behavior,
specifically the ability to return answers in a consistent format, becomes a
critical factor towards an effective system. Overall, ChemReasoner-Expert and
ChemReasoner-Planner executes $2N_{tree}$ and $3N_{tree}$ LLM inferences
respectively.

Requirements for LLM Scaling and Robustness Therefore, to execute the entire
benchmark of 145 queries with $N_{max}=300$ requires execution of
28,000-42,000 LLM inferences, which makes the the scalability and throughput
of a generative LLM a critical factor for successful experimentation. By
default, we batch LLM inferences in sizes of 48 and execute the calls
asynchronously for scalability. These two factors, namely the robustness of
instruction-following and high-throughput execution of batched queries
restricted our experiments to only GPT-3.5 and GPT-4 models. Our experiments
with LLama2 were both affected by frequent spurious answers as well as by the
relatively low throughput on A100 systems. We recognize the importance of both
development and benchmarking on open-source models and plan to pursue it as
future work.

GNN Inference and Reward Estimation Executing GNN inferences plays an equally
critical role for ChemReasoner. A single execution of the adsorption energy-
based reward function requires multiple inferences. Given a string-based
representation of a catalyst, we first initialize a 3D atomistic
representation and oversample the structural conformation to generate
$N_{structs}$ samples. Next, we execute the relaxation process for each of
these $N_{structs}$ structures (set to 16 by default) for a maximum specified
iteration limit of $N_{relax}$ (set to 64 by default). Therefore, each
execution of each adsorption energy-based reward function requires
$N_{structs}*N_{relax}$ GNN inferences. We group the GNN inferences using a
batch size of 40 for scalability. For reaction pathway-based reward
estimation, we perform this computation for every intermediate state in each
pathway. Therefore, with $N_{pathway}$ denoting the number of pathways and
$N_{rstep}$ denoting the average number of intermediate steps per pathway, a
single reward estimation involves
$N_{structs}*N_{relax}*N_{pathway}*N_{rstep}$ inferences. Given our CO2-fuel
conversion queries which involve 2 reaction pathways comprising 4-5 steps, we
execute 9,216 GNN inferences for every reaction-pathway based reward.

###  5.3 Considering DFT-Based Rewards

A critical component for a reasoning system is accurate feedback. For this
reason, the use of DFT-based rewards would be the default choice. However, due
to their significant computational cost, we elected to employ GNNs (Zitnick et
al., 2020). We evaluated our top GNN results using DFT simulations; overall,
we found that DFT calculation-based rewards diverge in some cases
significantly from GNN predictions (Table 3). Here, we briefly examine the
divergence between SOTA GNN models and ground truth DFT simulations.

One limitation of the GNN likely arises from the conversion of text-based
catalyst recommendations from the LLM to 3D atomistic structures for the GNN
calculation. Our structure generation method is limited to pre-defined lattice
structures, (e.g., face centered cubic (FCC), body centered cubic (FCC), and
hexagonal close packed (HCP)). While these lattice structures effectively
describe mono-metallic structures, multimetallic compounds may exhibit more
complex lattice structures. Thus, our assumption of simpler structures may be
limiting. Furthermore, our multimetallic bulk structures were generated by
randomly placing elements throughout the bulk, creating additional complexity.

While the GNN can predict energy for any given structures, the challenges
mentioned above may lead to erroneous DFT calculations, which makes validation
of our predicted catalysts difficult. Therefore, future research should
explore novel methods to convert textual representations of catalysts into
realistic 3D atomistic structures.

###  5.4 CO_2 Hydrogenation: A Case Study

To evaluate the real-world efficacy of ChemReasoner, we performed a literature
evaluation of the top-5 predicted metallic catalysts for the conversion of
CO_2 to methanol. These predictions from GPT-4, ChemReasoner-Expert, and
ChemReasoner-Planner models are shown in Table 2. We find that the
ChemReasoner-Expert predictions include elements that make up the current
commercial catalysts for methanol production Cu/ZnO/Al_2O_3 (Etim et al.,
2020). Cu, ZnO, and Cu-Zn sites were considered to generate the active sites
for high methanol selectivity (Etim et al., 2020; Tisseraud et al., 2015).

On the other hand, ChemReasoner-Planner predicted bimetallic precious metal
alloys, which are well-known to catalyze hydrogenation reactions (Tawalbeh et
al., 2023). The ensemble effect (Wang et al., 2019) in Pd-Au electrocatalysts
was considered as key factor in achieving high activity for CO_2 reduction to
methanol since Pd, Pt, Rh and Ru alone can shift the selectivity to methane
(CH_4) by uncontrolled hydrogenation of CO_2 (Pakhare & Spivey, 2014).
Alloying allows for improved methanol selectivity compared to the performance
of single metal catalysts (Tawalbeh et al., 2023; Etim et al., 2020; Tisseraud
et al., 2015) that were predicted by GPT-4 and some by ChemReasoner-Expert.
Since both ChemReasoner-Expert and ChemReasoner-Planner predicted alloys, we
consider their recommendations to have higher chances of being successful
catalysts.

##  6 Conclusion

We introduce a multi-modal framework unifying linguistic reasoning enabled by
generative LLMs with atomistic structure based rewards, built on principles in
catalysis and quantum chemistry. We demonstrate that such integration can
enable the recommendations of novel catalyst structures that are critical for
developing energy efficient chemical conversion processes and combat climate
change. Our methodology is generic and can be applied to other scientific
applications in biology and chemistry. Carrying strong quantitative and
qualitative validation with domain guided approaches, and extending support
towards recommendation of novel structures that are not covered in the
original training datasets remain major candidates for future work.

## References

  * Artz et al. (2018) Artz, J., Muüller, T. E., Thenert, K., Kleinekorte, J., Meys, R., Sternberg, A., Bardow, A., and Leitner, W.  Sustainable conversion of carbon dioxide: an integrated review of catalysis and life cycle assessment.  _Chemical reviews_ , 118(2):434–504, 2018.
  * Boiko et al. (2023) Boiko, D. A., MacKnight, R., and Gomes, G.  Emergent autonomous scientific research capabilities of large language models.  _arXiv preprint arXiv:2304.05332_ , 2023.
  * Bran et al. (2023) Bran, A. M., Cox, S., White, A. D., and Schwaller, P.  Chemcrow: Augmenting large-language models with chemistry tools.  _arXiv preprint arXiv:2304.05376_ , 2023.
  * Canakci & Van Gerpen (1999) Canakci, M. and Van Gerpen, J.  Biodiesel production viaacid catalysis.  _Transactions of the ASAE_ , 42(5):1203–1210, 1999.
  * Cao et al. (2023) Cao, H., Liu, Z., Lu, X., Yao, Y., and Li, Y.  Instructmol: Multi-modal integration for building a versatile and reliable molecular assistant in drug discovery.  _arXiv preprint arXiv:2311.16208_ , 2023.
  * Chanussot et al. (2010) Chanussot, L., Das, A., Goyal, S., Lavril, T., Shuaibi, M., Riviere, M., Tran, K., Heras-Domingo, J., Ho, C., Hu, W., et al.  The open catalyst 2020 (oc20) dataset and community challenges. arxiv.  _arXiv_ , 2010.
  * Chanussot* et al. (2021) Chanussot*, L., Das*, A., Goyal*, S., Lavril*, T., Shuaibi*, M., Riviere, M., Tran, K., Heras-Domingo, J., Ho, C., Hu, W., Palizhati, A., Sriram, A., Wood, B., Yoon, J., Parikh, D., Zitnick, C. L., and Ulissi, Z.  Open catalyst 2020 (oc20) dataset and community challenges.  _ACS Catalysis_ , 2021.  doi: 10.1021/acscatal.0c04525.
  * Chen & Jung (2022) Chen, S. and Jung, Y.  A generalized-template-based graph neural network for accurate organic reactivity prediction.  _Nature Machine Intelligence_ , 4(9):772–780, 2022.
  * Cheng et al. (2023) Cheng, A. H., Cai, A., Miret, S., Malkomes, G., Phielipp, M., and Aspuru-Guzik, A.  Group selfies: a robust fragment-based molecular string representation.  _Digital Discovery_ , 2023.
  * Chithrananda et al. (2020) Chithrananda, S., Grand, G., and Ramsundar, B.  Chemberta: Large-scale self-supervised pretraining for molecular property prediction.  _arXiv preprint arXiv:2010.09885_ , 2020.
  * Christofidellis et al. (2023) Christofidellis, D., Giannone, G., Born, J., Winther, O., Laino, T., and Manica, M.  Unifying molecular and textual representations via multi-task language modelling.  _arXiv preprint arXiv:2301.12586_ , 2023.
  * Dal Corso (2014) Dal Corso, A.  Pseudopotentials periodic table: From h to pu.  _Computational Materials Science_ , 95:337–350, 2014.
  * Daza & Kuhn (2016) Daza, Y. A. and Kuhn, J. N.  Co 2 conversion by reverse water gas shift catalysis: comparison of catalysts, mechanisms and their consequences for co 2 conversion to liquid fuels.  _RSC advances_ , 6(55):49675–49691, 2016.
  * Dumesic et al. (2008) Dumesic, J. A., Huber, G. W., and Boudart, M.  Principles of heterogeneous catalysis.  _Handbook of Heterogeneous Catalysis: Online_ , 2008.
  * Edwards et al. (2021) Edwards, C., Zhai, C., and Ji, H.  Text2Mol: Cross-modal molecule retrieval with natural language queries.  In _Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing_ , pp. 595–607, Online and Punta Cana, Dominican Republic, November 2021. Association for Computational Linguistics.  URL https://aclanthology.org/2021.emnlp-main.47.
  * Edwards et al. (2022a) Edwards, C., Lai, T., Ros, K., Honke, G., Cho, K., and Ji, H.  Translation between molecules and natural language.  In _Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing_ , pp. 375–413, Abu Dhabi, United Arab Emirates, December 2022a. Association for Computational Linguistics.  doi: 10.18653/v1/2022.emnlp-main.26.  URL https://aclanthology.org/2022.emnlp-main.26.
  * Edwards et al. (2022b) Edwards, C., Lai, T., Ros, K., Honke, G., Cho, K., and Ji, H.  Translation between molecules and natural language.  In _Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing_ , pp. 375–413, Abu Dhabi, United Arab Emirates, December 2022b. Association for Computational Linguistics.  URL https://aclanthology.org/2022.emnlp-main.26.
  * Etim et al. (2020) Etim, U. J., Song, Y., and Zhong, Z.  Improving the cu/zno-based catalysts for carbon dioxide hydrogenation to methanol, and the use of methanol as a renewable energy storage media.  _Frontiers in Energy Research_ , 8:545431, 2020.
  * Fabian et al. (2020) Fabian, B., Edlich, T., Gaspar, H., Segler, M., Meyers, J., Fiscato, M., and Ahmed, M.  Molecular representation learning with language models and domain-relevant auxiliary tasks.  _arXiv preprint arXiv:2011.13230_ , 2020.
  * Fang et al. (2023) Fang, Y., Liang, X., Zhang, N., Liu, K., Huang, R., Chen, Z., Fan, X., and Chen, H.  Mol-instructions: A large-scale biomolecular instruction dataset for large language models.  _arXiv preprint arXiv:2306.08018_ , 2023.
  * Gasteiger et al. (2021) Gasteiger, J., Becker, F., and Günnemann, S.  Gemnet: Universal directional graph neural networks for molecules.  _Advances in Neural Information Processing Systems_ , 34:6790–6802, 2021.
  * Giannozzi et al. (2009) Giannozzi, P., Baroni, S., Bonini, N., Calandra, M., Car, R., Cavazzoni, C., Ceresoli, D., Chiarotti, G. L., Cococcioni, M., Dabo, I., et al.  Quantum espresso: a modular and open-source software project for quantum simulations of materials.  _Journal of physics: Condensed matter_ , 21(39):395502, 2009.
  * Giannozzi et al. (2017) Giannozzi, P., Andreussi, O., Brumme, T., Bunau, O., Nardelli, M. B., Calandra, M., Car, R., Cavazzoni, C., Ceresoli, D., Cococcioni, M., et al.  Advanced capabilities for materials modelling with quantum espresso.  _Journal of physics: Condensed matter_ , 29(46):465901, 2017.
  * Greeley et al. (2002) Greeley, J., Nørskov, J. K., and Mavrikakis, M.  Electronic structure and catalysis on metal surfaces.  _Annual review of physical chemistry_ , 53(1):319–348, 2002.
  * Grimme et al. (2010) Grimme, S., Antony, J., Ehrlich, S., and Krieg, H.  A consistent and accurate ab initio parametrization of density functional dispersion correction (dft-d) for the 94 elements h-pu.  _The Journal of chemical physics_ , 132(15), 2010.
  * Guo et al. (2023) Guo, T., Guo, K., Nan, B., Liang, Z., Guo, Z., Chawla, N. V., Wiest, O., and Zhang, X.  What can large language models do in chemistry? a comprehensive benchmark on eight tasks.  _arXiv preprint arXiv:2305.18365_ , 2023.
  * Hao et al. (2023) Hao, S., Gu, Y., Ma, H., Hong, J. J., Wang, Z., Wang, D. Z., and Hu, Z.  Reasoning with language model is planning with world model.  _arXiv preprint arXiv:2305.14992_ , 2023.
  * Hu & Yip (2021) Hu, X. and Yip, A. C.  Heterogeneous catalysis: enabling a sustainable future, 2021.
  * Huang et al. (2022) Huang, W., Xia, F., Xiao, T., Chan, H., Liang, J., Florence, P., Zeng, A., Tompson, J., Mordatch, I., Chebotar, Y., et al.  Inner monologue: Embodied reasoning through planning with language models.  _arXiv preprint arXiv:2207.05608_ , 2022.
  * Huber et al. (2020) Huber, S. P., Zoupanos, S., Uhrin, M., Talirz, L., Kahle, L., Häuselmann, R., Gresch, D., Müller, T., Yakutovich, A. V., Andersen, C. W., et al.  Aiida 1.0, a scalable computational infrastructure for automated reproducible workflows and data provenance.  _Scientific data_ , 7(1):300, 2020.
  * Kattel et al. (2017) Kattel, S., Liu, P., and Chen, J. G.  Tuning selectivity of co2 hydrogenation reactions at the metal/oxide interface.  _Journal of the American Chemical Society_ , 139(29):9739–9754, 2017.
  * Krenn et al. (2020) Krenn, M., Häse, F., Nigam, A., Friederich, P., and Aspuru-Guzik, A.  Self-referencing embedded strings (selfies): A 100% robust molecular string representation.  _Machine Learning: Science and Technology_ , 1(4):045024, 2020.
  * Lai et al. (2023) Lai, T. M., Zhai, C., and Ji, H.  Knowledge-enhanced biomedical language models.  In _Journal of Biomedical Informatics_ , 2023.
  * Larsen et al. (2017) Larsen, A. H., Mortensen, J. J., Blomqvist, J., Castelli, I. E., Christensen, R., Dułak, M., Friis, J., Groves, M. N., Hammer, B., Hargus, C., Hermes, E. D., Jennings, P. C., Jensen, P. B., Kermode, J., Kitchin, J. R., Kolsbjerg, E. L., Kubal, J., Kaasbjerg, K., Lysgaard, S., Maronsson, J. B., Maxson, T., Olsen, T., Pastewka, L., Peterson, A., Rostgaard, C., Schiøtz, J., Schütt, O., Strange, M., Thygesen, K. S., Vegge, T., Vilhelmsen, L., Walter, M., Zeng, Z., and Jacobsen, K. W.  The atomic simulation environment—a python library for working with atoms.  _Journal of Physics: Condensed Matter_ , 29(27):273002, 2017.  URL http://stacks.iop.org/0953-8984/29/i=27/a=273002.
  * Ling et al. (2023) Ling, G. Z. S., Foo, J. J., Tan, X.-Q., and Ong, W.-J.  Transition into net-zero carbon community from fossil fuels: Life cycle assessment of light-driven co2 conversion to methanol using graphitic carbon nitride.  _ACS Sustainable Chemistry & Engineering_, 11(14):5547–5558, 2023.
  * Liu et al. (2022) Liu, S., Nie, W., Wang, C., Lu, J., Qiao, Z., Liu, L., Tang, J., Xiao, C., and Anandkumar, A.  Multi-modal molecule structure-text model for text-based retrieval and editing.  _arXiv preprint arXiv:2212.10789_ , 2022.
  * Liu et al. (2023a) Liu, S., Wang, J., Yang, Y., Wang, C., Liu, L., Guo, H., and Xiao, C.  Chatgpt-powered conversational drug editing using retrieval and domain feedback.  _arXiv preprint arXiv:2305.18090_ , 2023a.
  * Liu et al. (2023b) Liu, S., Zhu, Y., Lu, J., Xu, Z., Nie, W., Gitter, A., Xiao, C., Tang, J., Guo, H., and Anandkumar, A.  A text-guided protein design framework.  _arXiv preprint arXiv:2302.04611_ , 2023b.
  * Liu et al. (2023c) Liu, Z., Zhang, W., Xia, Y., Wu, L., Xie, S., Qin, T., Zhang, M., and Liu, T.-Y.  Molxpt: Wrapping molecules with text for generative pre-training.  _arXiv preprint arXiv:2305.10688_ , 2023c.
  * Mondal & Yadav (2021) Mondal, U. and Yadav, G. D.  Methanol economy and net zero emissions: critical analysis of catalytic processes, reactors and technologies.  _Green Chemistry_ , 23(21):8361–8405, 2021.
  * Mukhtar et al. (2022) Mukhtar, A., Saqib, S., Lin, H., Shah, M. U. H., Ullah, S., Younas, M., Rezakazemi, M., Ibrahim, M., Mahmood, A., Asif, S., et al.  Current status and challenges in the heterogeneous catalysis for biodiesel production.  _Renewable and Sustainable Energy Reviews_ , 157:112012, 2022.
  * Nørskov et al. (2011) Nørskov, J. K., Abild-Pedersen, F., Studt, F., and Bligaard, T.  Density functional theory in surface chemistry and catalysis.  _Proceedings of the National Academy of Sciences_ , 108(3):937–943, 2011.
  * NVIDIA Corporation (2022) NVIDIA Corporation.  Megamolbart v0.2, 2022.  URL https://catalog.ngc.nvidia.com/orgs/nvidia/teams/clara/models/megamolbart_0_2.
  * OpenAI (2023) OpenAI.  Gpt-4 technical report, 2023.
  * Ouyang et al. (2022) Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al.  Training language models to follow instructions with human feedback.  _Advances in Neural Information Processing Systems_ , 35:27730–27744, 2022.
  * Pakhare & Spivey (2014) Pakhare, D. and Spivey, J.  A review of dry (co2) reforming of methane over noble metal catalysts.  _Chem. Soc. Rev._ , 43:7813–7837, 2014.  doi: 10.1039/C3CS60395D.  URL http://dx.doi.org/10.1039/C3CS60395D.
  * Paszke et al. (2019) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S.  Pytorch: An imperative style, high-performance deep learning library.  In _Advances in Neural Information Processing Systems 32_ , pp. 8024–8035. Curran Associates, Inc., 2019.  URL http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf.
  * Perdew et al. (1996) Perdew, J. P., Burke, K., and Wang, Y.  Generalized gradient approximation for the exchange-correlation hole of a many-electron system.  _Physical review B_ , 54(23):16533, 1996.
  * Rubin & Reddy (1977) Rubin, S. M. and Reddy, R.  The locus model of search and its use in image interpretation.  _Cambridge, Massachusetts_ , pp. 590–595, 1977.
  * Schwaller et al. (2019) Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C. A., Bekas, C., and Lee, A. A.  Molecular transformer: a model for uncertainty-calibrated chemical reaction prediction.  _ACS central science_ , 5(9):1572–1583, 2019\.
  * Schwaller et al. (2021) Schwaller, P., Probst, D., Vaucher, A. C., Nair, V. H., Kreutter, D., Laino, T., and Reymond, J.-L.  Mapping the space of chemical reactions using attention-based neural networks.  _Nature Machine Intelligence_ , 3(2):144–152, 2021.
  * Seidl et al. (2023) Seidl, P., Vall, A., Hochreiter, S., and Klambauer, G.  Enhancing activity prediction models in drug discovery with the ability to understand human language.  _arXiv preprint arXiv:2303.03363_ , 2023.
  * Sprueill et al. (2023) Sprueill, H. W., Edwards, C., Olarte, M. V., Sanyal, U., Ji, H., and Choudhury, S.  Monte carlo thought search: Large language model querying for complex scientific reasoning in catalyst design.  _arXiv preprint arXiv:2310.14420_ , 2023.
  * Stolarczyk et al. (2018) Stolarczyk, J. K., Bhattacharyya, S., Polavarapu, L., and Feldmann, J.  Challenges and prospects in solar water splitting and co2 reduction with inorganic and hybrid nanostructures.  _ACS Catalysis_ , 8(4):3602–3635, 2018.
  * Su et al. (2022) Su, B., Du, D., Yang, Z., Zhou, Y., Li, J., Rao, A., Sun, H., Lu, Z., and Wen, J.-R.  A molecular multimodal foundation model associating molecule graphs with natural language.  _arXiv preprint arXiv:2209.05481_ , 2022.
  * Tawalbeh et al. (2023) Tawalbeh, M., Javed, R. M. N., Al-Othman, A., Almomani, F., and Ajith, S.  Unlocking the potential of CO_2 hydrogenation into valuable products using noble metal catalysts: A comprehensive review.  _Environmental Technology & Innovation_, 31:103217, 2023\.  ISSN 2352-1864.  doi: https://doi.org/10.1016/j.eti.2023.103217.  URL https://www.sciencedirect.com/science/article/pii/S2352186423002134.
  * Taylor et al. (2022) Taylor, R., Kardas, M., Cucurull, G., Scialom, T., Hartshorn, A., Saravia, E., Poulton, A., Kerkez, V., and Stojnic, R.  Galactica: A large language model for science.  _arXiv preprint arXiv:2211.09085_ , 2022.
  * Tisseraud et al. (2015) Tisseraud, C., Comminges, C., Belin, T., Ahouari, H., Soualah, A., Pouilloux, Y., and Le Valant, A.  The cu–zno synergy in methanol synthesis from CO2, part 2: Origin of the methanol and co selectivities explained by experimental studies and a sphere contact quantification model in randomly packed binary mixtures on cu–zno coprecipitate catalysts.  _Journal of Catalysis_ , 330:533–544, 2015.  ISSN 0021-9517.  doi: https://doi.org/10.1016/j.jcat.2015.04.035.  URL https://www.sciencedirect.com/science/article/pii/S0021951715001396.
  * Touvron et al. (2023) Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.  Llama 2: Open foundation and fine-tuned chat models.  _arXiv preprint arXiv:2307.09288_ , 2023.
  * Tysinger et al. (2023) Tysinger, E. P., Rai, B. K., and Sinitskiy, A. V.  Can we quickly learn to “translate” bioactive molecules with transformer models?  _Journal of Chemical Information and Modeling_ , 63(6):1734–1744, 2023.
  * Unsleber et al. (2023) Unsleber, J. P., Liu, H., Talirz, L., Weymuth, T., Mörchen, M., Grofe, A., Wecker, D., Stein, C. J., Panyala, A., Peng, B., et al.  High-throughput ab initio reaction mechanism exploration in the cloud with automated multi-reference validation.  _The Journal of Chemical Physics_ , 158(8), 2023.
  * Vall et al. (2021) Vall, A., Hochreiter, S., and Klambauer, G.  Bioassayclr: Prediction of biological activity for novel bioassays based on rich textual descriptions.  In _ELLIS ML4Molecules workshop_ , 2021.
  * Vaucher et al. (2021) Vaucher, A. C., Schwaller, P., Geluykens, J., Nair, V. H., Iuliano, A., and Laino, T.  Inferring experimental procedures from text-based representations of chemical reactions.  _Nature communications_ , 12(1):2573, 2021.
  * Wang et al. (2019) Wang, Y., Cao, L., Libretto, N. J., Li, X., Li, C., Wan, Y., He, C., Lee, J., Gregg, J., Zong, H., Su, D., Miller, J. T., Mueller, T., and Wang, C.  Ensemble effect in bimetallic electrocatalysts for co2 reduction.  _Journal of the American Chemical Society_ , 141(42):16635–16642, 2019.  doi: 10.1021/jacs.9b05766.  URL https://doi.org/10.1021/jacs.9b05766.  PMID: 31509393.
  * Wei et al. (2022) Wei, J., Wang, X., Schuurmans, D., Bosma, M., Chi, E., Le, Q., and Zhou, D.  Chain of thought prompting elicits reasoning in large language models.  _arXiv preprint arXiv:2201.11903_ , 2022.
  * Weininger (1988) Weininger, D.  Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules.  _Journal of chemical information and computer sciences_ , 28(1):31–36, 1988.
  * Weininger et al. (1989) Weininger, D., Weininger, A., and Weininger, J. L.  Smiles. 2. algorithm for generation of unique smiles notation.  _Journal of chemical information and computer sciences_ , 29(2):97–101, 1989.
  * Xi et al. (2002) Xi, J., Wang, Z., Wang, W., and Lu, G.  In-situ xps study for reaction mechanism of methanol decomposition over cu-ni/zn catalyst.  _ACTA PHYSICOCHIMICA SINICA_ , 18(1):82–86, 2002\.
  * Xu & Wang (2022) Xu, H. and Wang, S.  Protranslator: zero-shot protein function prediction using textual description.  In _Research in Computational Molecular Biology: 26th Annual International Conference, RECOMB 2022, San Diego, CA, USA, May 22–25, 2022, Proceedings_ , pp. 279–294. Springer, 2022.
  * Xu et al. (2023) Xu, M., Yuan, X., Miret, S., and Tang, J.  Protst: Multi-modality learning of protein sequences and biomedical texts.  _arXiv preprint arXiv:2301.12040_ , 2023.
  * Xu & Carter (2018) Xu, S. and Carter, E. A.  Theoretical insights into heterogeneous (photo) electrochemical co2 reduction.  _Chemical reviews_ , 119(11):6631–6669, 2018\.
  * Ye et al. (2023) Ye, G., Cai, X., Lai, H., Wang, X., Huang, J., Wang, L., Liu, W., and Zeng, X.  Drugassist: A large language model for molecule optimization.  _arXiv preprint arXiv:2401.10334_ , 2023.
  * Zeng et al. (2022) Zeng, Z., Yao, Y., Liu, Z., and Sun, M.  A deep-learning system bridging molecule structure and biomedical text with comprehension comparable to human professionals.  _Nature communications_ , 13(1):862, 2022.
  * Zhang et al. (2023) Zhang, X., Wang, L., Helwig, J., Luo, Y., Fu, C., Xie, Y., Liu, M., Lin, Y., Xu, Z., Yan, K., et al.  Artificial intelligence for science in quantum, atomistic, and continuum systems.  _arXiv preprint arXiv:2307.08423_ , 2023.
  * Zhao et al. (2023a) Zhao, H., Liu, S., Ma, C., Xu, H., Fu, J., Deng, Z.-H., Kong, L., and Liu, Q.  Gimlet: A unified graph-text model for instruction-based molecule zero-shot learning.  _bioRxiv_ , pp. 2023–05, 2023a.
  * Zhao et al. (2023b) Zhao, W., Zhou, D., Cao, B., Zhang, K., and Chen, J.  Adversarial modality alignment network for cross-modal molecule retrieval.  _IEEE Transactions on Artificial Intelligence_ , 2023b.
  * Zhao et al. (2024) Zhao, Z., Ma, D., Chen, L., Sun, L., Li, Z., Xu, H., Zhu, Z., Zhu, S., Fan, S., Shen, G., et al.  Chemdfm: Dialogue foundation model for chemistry.  _arXiv preprint arXiv:2401.14818_ , 2024.
  * Zitnick et al. (2020) Zitnick, C. L., Chanussot, L., Das, A., Goyal, S., Heras-Domingo, J., Ho, C., Hu, W., Lavril, T., Palizhati, A., Riviere, M., et al.  An introduction to electrocatalyst design using machine learning for renewable energy storage.  _arXiv preprint arXiv:2010.09435_ , 2020.

##  Appendix A GNN Driven Adsorption Energy Calculation

Here, we detail our methodology for GNN-driven adsorption energy calculations.
Given a linguistic catalyst recommendation from the LLM, an adsorption energy
is produced in a three step process, catalyst generation, adsorbate+catalyst
sampling, relaxation.

First, the linguistic representation of a catalyst from the language model
must be translated into a formate which is computationally digestible. For
instance, a simple catalyst, like pure Copper Zinc (CuZn), can be expressed in
several different ways:

  * •

Copper-Zinc

  * •

Copper alloyed with Zinc

  * •

CuZn

  * •

Copper/Zinc-oxide (catlyst not supported by our method)

  * •

Cu-Zn.

Attempting to capture all possible disambiguation of catlyst is intractable
for large tree searches, especially with less-well behaved models such as
GPT-3.5-turbo. It also becomes more difficult to decide which catalysts to
penalize as containing non-metals, such as Copper/Zinc-oxide. Therefore, we
prompt the LLM to parse out the correct chemical symbols with the LLM into
digestible lists of chemical symbols. For instance, the above phrases would be
parsed into:

  * •

[“Cu”, “Zn”]

  * •

[“Cu”, “Zn”]

  * •

[“Cu”, “Zn”]

  * •

[“Cu”, “Zn”, “O”] (catlyst not supported by our method)

  * •

[“Cu”, “Zn”].

These examples are much easier to parse out, since we expect to see only
chemical symbols. Additionally, the “O” can easily be parsed out to return a
penalty value. With these lists of chemical symbols we can use a set of rules
to generate the associated 3D structures.

In the second step, the 3D structure of the catalyst is inferred from known
reference structures, as specified by the Atomic Simulation Environment (ASE)
package (Larsen et al., 2017). For the purposes of this paper, we restrict our
focus only to catalyst whose reference structures are in face-centered cubic
(FCC), body-centered cubic (BCC), or hexagonal close-packed (HCP) lattice
structures. Catalysts with other reference structures are skipped and assigned
a negative reward value. However, many important metals for catalysis have
these three lattice structures. If the recommended catalyst is a bimetallic or
trimetallic catalyst (composed of two or three elements), we randomly
introduce atoms from the secondary and tertiary elements into the structure.
For bimetallic compounds, AB, we assume a 2:1 ratio of A to B, while
trimetallic catalysts have a 1:1:1 ratio. Catalysts with more than 3 elements
return a penalty value. The lattice structure is always inferred by the first
species listed by the LLM. For instance CuZn would have the structure of
copper, with some atoms randomly replaced with Zn in a 2:1 ratio. To promote
the stability of the catalysts, we sample 16 possible configurations and chose
only the structure with the lowest energy, as determined by the GNN, to move
on to the rest of the GNN calculation.

The third step of the process is the sampling of adsorbate+catalyst
configurations. the goal is to sample different locations and orientations for
an adsorbate to bind to the surface of the catalyst, to ensure optimal binding
is observed. We determine binding sites, use the placement sites specified by
ASE for each of the three lattice configuration. Then, for an adsorbate, which
we denote generically as, *XYZ, we place the atom marked by the * at the
binding location at a height 1.87 Åabove the surface. Then, to sample
orientations of the adsorbate, we rotate the molecule a random angle up to 15
degrees in the x-axis (axis parallel to the surface), then a random angle up
to 360 degrees around the z axis (axis perpendicular to the surface). Finally,
since some adsorbate molecules may bind differently to certain elements in
multimetallic catalysts, we randomly replace the binding site of the catalyst
with each of the available metallic elements, with equal probability. This
gives fair change of the adsorbate binding to any of the given catalyst
species. We take 16 initial adsorbate+catalyst configurations and relax each
of them with the GNN to compute adsorption energies.

The fourth and final step is the the GNN driven relaxation. Given the set of
atomic coordinates and atomic numbers of a structure, the GemNet-dT model
(Gasteiger et al., 2021) returns an approximation to that structure’s
adsorption energy and forces, $E_{\mathrm{ads}}$ and
$\mathbf{F_{\mathrm{max}}}$. Starting with the sampled adsorbate+catalyst
configurations, the structures are relaxed in-batches of 40, using the L-BFGS
from Pytorch (Chanussot* et al., 2021; Paszke et al., 2019). We stop the
relaxation terminates when after 64 stems or when the magnitude of the maximum
single atom force is below the early stopping threshold,
$\mathbf{F_{\mathrm{max}}}<0.05$ eV/Å. Then, the minimum adsorption energy of
the 16 samples is returned as the final adsorption energy for the
adsorbate+catalyst configuration.

These four steps constitute the computational backbone of the reward
calculation. For reaction based queries, the reward function is derived from
these energies according to Equation 1.

##  Appendix B Dataset Design

We propose two task datasets related to catalyst design: the first is derived
from the Open Catalyst (OC) Project (Zitnick et al., 2020) and the second
consists of complex reasoning queries designed by catalysis experts. Our
multi-disciplinary team involves researchers who actively work on designing
new catalysts for bio-fuels development.

###  B.1 Prompt Design

To apply ChemReasoner to catalyst discovery, we define use a prompt template
which incorporates and overall query for the LLM to answer and we use the
actions defined in 5.1 to modify the fields of the templates to reason about
the queries. Here, we discuss the s general prompt template, which is used for
ChemReasoner, CoT, and self-consistency, before diving into the construction
of queries within the datasets.

Given a query, we use a prompt template which allows the systematic adjustment
of an LLM’s context for answering the query, essentially creating a reasoning
process. The reasoning has four changeable variables, the “catalyst type”,
“inclusion criteria”, “exclusion criteria”, and “candidate_list_statement”.
Here, we describe each of these variables in detail.

First, the “catalyst type” is the broad category of catalysts to suggest (i.e.
metallic catalysts, bimetallic catalysts, transition metal catalysts). Second,
we provide a list of ‘inclusion criteria” and “exclusion criteria” to provide
additional context for which properties are more/less useful for suggesting a
catalyst that answers the original query. Such examples could be “include
catalysts with low cost, high conversion, high Lewis-acidity” or “exclude
catalysts which degrade quickly”. While the variables provide context to the
LLM, in ChemReasoner, we additionally include, in the prompt, the suggested
catalysts from the previous answer as additional context. This motivates final
variable, “candidate list type,” where the prompt LLM is instructed how to use
the previous candidates in its evaluation. For instance, it can produce
candidates that are “similar to..”, “different from…”, or “have stronger
affinity for oxygen than…” the previous candidates.

The overall prompt template is as follows:

{query}

{include_statement}{exclude_statement} Provide scientific explanations for
each of the {catalyst_label}. Finally, return a python list named final_answer
which contains the top-5 {catalyst_label}. {candidate_list_statement}

Take a deep breath and let’s think step-by-step. Remember, you need to return
a python list named final_answer!

The query in the prompt template is determined by our three datasets:
OpenCatalyst, BioFuels,

###  B.2 OpenCatalyst Dataset

The Open Catalyst dataset (Zitnick et al., 2020) is an extensive collection of
(DFT) calculations for various adsorbate+catalyst, intended for training
surrogate models for computational chemistry simulations related to catalysis.
Here, we utilize this dataset to benchmark the ability of the ChemReasonerto
produce catalyst recommendations for the set of adsorbates given in the Open
Catalyst 2020 dataset.

Each query consists of a single adsorbate from the Open Catalyst Project
dataset (i.e. *OHCH3, where * denotes the catalyst binding site). So, each
prompt template takes the form Generate a list of top-5 {catalyst_label} for
the adsorption of {adsorbate}, where catalyst_label begins with ‘metallic
catalysts’.

###  B.3 BioFuels Dataset

The BioFuels dataset targets more complex adsorbates related to (a) the
production of sustainable fuels from organic materials and (b), the reverse
water gas shift reaction. The reactions and adsorbates targeted here are
crucial to the production synthetic bio fuels with greater
selectivity.(Canakci & Van Gerpen, 1999; Daza & Kuhn, 2016; Kattel et al.,
2017; Artz et al., 2018; Stolarczyk et al., 2018; Xu & Carter, 2018; Mukhtar
et al., 2022)

For the queries in (a), we benchmark the ability of ChemReasonerto recommend
catalysts for the adsorption of specific adsorbates related to bio-fuel
production. Such adsorbates are phenol(-ate), anisole, furfurol, methanol,
methyl, ethanol, acetic acide, and acetate. Additionally, we apply a criterion
of interest to ChemReasoner, such as high/low selectivity, conversion, binding
energy. Finally, we provide additional context by specifying a type of
reaction we are interested in, hydrodeoxygenation or hydrogenation. The
template for these queries is Generate a list of top-5 {catalyst_label} can
bind {adsorbate} in the {reaction_type} reaction with {additional_constraint},
where catalyst_label begins with ‘metallic catalysts’ while reaction_type,
adsorbate, and additional_constraint take the values described above.

For the queries in (b), we benchmark the ability of ChemReasonerto recommend
catalysts for the production of CO through the RWGS reaction,

$\displaystyle\ce{CO2+H2<=>[cat]CO+H2O}.$

There are several query variations for this reaction. First, we ask
ChemReasonerto recommend catalysts that strongly bind the reactants (CO2 and
H2) and weakly bind the product (CO). The remainder of the questions relate to
catalysts which demonstrate high convergence for the reaction. Then, we change
the type of catalyst requested (metallic catalyst, bimetallic catalyst,
bimetallic catalyst with noble metal and base metal). Finally, we optionally
include an additional criterion for low cost catalysts. The final query
template is Generate a list of top-5 {catalyst_label} {additional_criterion}
for {reaction_question}.

###  B.4 CO_2-Conversion Dataset

Finally, we propose a set of 20 queries to benchmark ChemReasoner’s ability to
recommend catalysts for more complex reactions, the conversion of CO2 to
methanol and ethanol (X-anol). Compared to the RWGS reaction, these reactions
consist of pathways with different chemical intermediates. The language model
will have to consider how the structure of the catalyst promotes the
progression of the reaction of several chemical intermediates. Thus, queries
involving the CO2 to X-anol conversion reactions use our reaction pathway
based reward.

These query templates echo the same structure as the RWGS templates, Generate
a list of top-5 {catalyst_label} {additional_criterion} for
{reaction_question}.

Top-5 Catalysts | GPT-4 |  ChemReasoner-Expert |  ChemReasoner-Planner
---|---|---|---
1 | Pd | Cu-Zn | Pd-Au
2 | Cu | Fe | Pt-Ru
3 | Ru | Ni | Ru-Au
4 | Rh | Co | Rh-Pd
5 | Pt | Cu-Cr | Pt-Au
Table 2: Final catalyst recommendations from ChemReasoner and GPT-4 (root of
the tree) for the CO2$\rightarrow$ methanol conversion reactions.

##  Appendix C Search Analysis

###  C.1 Parameter selection

Given the compute intensive nature of each ChemReasoner query, we carried out
hyperparameter sweeps to determine the optimal number of actions for search
expansion ($N_{actions}$), as well as the maximum number of relaxation steps
($N_{relax}$). Figure 8 shows how increasing $N_{actions}$ impacts the
distribution of rewards, and we set it to a default value of 8 for all
experiments. Figure 6 shows convergence of the GNN for finding the optimal
atomistic structure configuratio over 300 iterations for 40 random initial
adsorbate+catalsyst configurations. Relaxations are terminated early if
$|\mathbf{F_{\mathrm{max}}}|<0.05$ ev/Å. At each step in the relaxation
process, the difference between the current adsorption energy and the final
adsorption energy is reported. The dashed gray line depicts the iterations
cutoff for reward calculations in ChemReasoner, 64 steps. Most iterations are
nearly converged by 64 steps.

###  C.2 Quantitative Validation with DFT

Figure 6: GNN energy versus relaxation iteration for 40 randomly chosen
adsorbate+catalyst configurations. The difference between the current and
final GNN energies are displayed to highlight the time-to-convergence for each
relaxation. The majority of structures are nearly fully relaxed by 64
iterations.

To validate the adsorption energies being predicted by the GNN, density
functional theory (DFT) calculations were performed for the top scoring
candidates. These calculations were carried out using the open-source _Quantum
ESPRESSO_ package (Giannozzi et al., 2009, 2017) which implements DFT using a
plane-wave basis set. Core electrons were replaced by pseudopotentials of
projector augmented-wave (PAW) type from the _pslibrary_ (Dal Corso, 2014).
The _PBE_ exchange-correlation functional was used (Perdew et al., 1996), and
van der Waals effects were included using the DFT-D3 approach (Grimme et al.,
2010). All of the calculations were managed and orchestrated using the AiiDA
automation framework (Huber et al., 2020).

While the GNN predicts the adsorption energy directly, each DFT computation of
the adsorption energy requires three calculations of the total energy:

$E_{adsorption}^{DFT}=E_{system}^{DFT}-(E_{slab}^{DFT}+E_{adsorbate}^{DFT})$
(2)

where $E_{system}^{DFT}$ is the DFT total energy for the complete system,
$E_{slab}^{DFT}$ is the DFT total energy for the bare slab without any
adsorbate, and $E_{slab}^{DFT}$ total DFT energy of the adsorbate molecule
alone.

To compute each of these energies, relaxations were initiated from the final
relaxed coordinates coming from the GNN, removing the adsorbate or the slab if
necessary. For the both the GNN relaxations and the DFT relaxations, the same
atoms in the bulk layers were fixed, while those in the surface layer were
allowed to relaxed. The cell vectors from the GNN structures already included
sufficient vacuum above the slab and so were used directly for the
corresponding DFT calculations.

Surface | Pathway | Adsorbate | Adsorption Energy (eV)
---|---|---|---
|  |  | GNN | DFT
CuZn | Methanol |  CO2 | $0.384$ | $-0.066$
|  | CHOH | $0.552$ | $5.951$
|  | OCHO | $0.577$ | $5.836$
|  |  OHCH3 | $-1.160$ | $2.699$
CuAlZn | Methanol |  CO2 | $0.265$ | $6.816$
|  | CHOH | $0.609$ | $-1.824$
|  | OCHO | $-0.125$ | $2.820$
|  |  OHCH3 | $-1.589$ | $-5.615$
Table 3: Comparison of adsorption energies predicted by the GNN to those
computed with DFT, for the methanol pathway.

###  C.3 Analysis of Search Depth

Beyond analyzing the final reward, it is also valuable to understand which of
the ChemReasonersearch methods discover their best recommended catalysts with
fewer computations. One aspect of of this search is the depth at which the
maximum reward node is found. As shown in Table 4, GPT-4 based searches find
their maximum reward node in fewer nodes, on average than, GPT-3.5-turbo based
searches, in each dataset. Then, ChemReasoner-Expert shows superior
performance in the OpenCatalyst and BioFuels datasets.

Table 4: Comparison of the average search depth of best node in each of the search trees. Method | Category | GPT-3.5-turbo | GPT-4 | Search depth reduction (%)
---|---|---|---|---
ChemReasoner-Expert | OpenCatalyst | 3.87 | 3.45 | 10.97
ChemReasoner-Expert | BioFuels | 3.71 | 3.50 | 5.76
ChemReasoner-Expert |  CO2-Fuel | 4.30 | 3.30 | 23.25
ChemReasoner-Planner | OpenCatalyst | 3.79 | 3.67 | 3.08
ChemReasoner-Planner | BioFuels | 3.5 | 3.21 | 8.16
ChemReasoner-Planner |  CO2-Fuel | 4.25 | 3.55 | 16.47

###  C.4 Distribution of Reward Functions

Figure 7: Distribution of simulation rewards for GPT-4 vs the two ChemReasoner
strategies. ChemReasoner consistently recommends better catalyst candidates
across different simulations.

###  C.5 Ablation on Actions Taken

To further investigate ChemReasoner’s performance, we perform an ablation over
the number of actions suggested from the planning prompt. While it may be
unpredictable how many actions the LLM may propose in its plan, we can limit
the number of actions, by randomly down-selecting actions, determining the
ideal number of actions for ChemReasoner-Planner. In Figure 8, we show box-
and-whisker plots of the best rewards for ChemReasoner-Planner evaluated over
our datasets for various maximum numbers of actions (10 was used in the main
text). The final rewards are steadily increasing for max number of actions
2-6, while 8 and 10 are offer the highest rewards overall.

Figure 8: Impact of maximum number of actions on candidate search. Maximum
actions if the most actions (e.g., “exclude low selectivity”) that the LLM is
allowed to consider at once when it is reasoning over catalysts.

###  C.6 Scaling of LLM and GNN inference

Both GPT-4 and GPT-3.5-turbo were run using the Python OpenAI Azure interface.
Our runtime was dominated by per-deployment rates limits, which are 40K
tokens/minute and 240 requests/minute for GPT-4 and 300k tokens/minute and
1800 requests/minute for GPT-3.5-turbo. Prompts at each level in the heuristic
search tree are executed asynchronously and in parallel.

Our implementation of the GNN reward function can perform an L-BFGS step in
approximately 0.5s with a batch size of 40 adsorbate+catalyst configurations
on a single GPU on a DGX2/V100 and on a single A100 40GB.

##  Appendix D Multi-modal models for Chemistry

Recent advances in NLP have produced strong results in the chemistry domain by
training LLMs (Fabian et al., 2020; Chithrananda et al., 2020; Vaucher et al.,
2021; Schwaller et al., 2021; NVIDIA Corporation, 2022; Tysinger et al., 2023)
on molecular sequence representations (Weininger, 1988; Weininger et al.,
1989; Krenn et al., 2020; Cheng et al., 2023). Beyond this, multi-modal text-
molecule models (Edwards et al., 2021; Vall et al., 2021; Zeng et al., 2022;
Xu & Wang, 2022; Su et al., 2022; Seidl et al., 2023; Xu et al., 2023; Zhao et
al., 2023b; Liu et al., 2023b) have been proposed to enable high-level control
over molecular design (Zhang et al., 2023). This line of work largely focuses
on cross-modal retrieval (Edwards et al., 2021; Zeng et al., 2022),
translation (Edwards et al., 2022b; Liu et al., 2023c; Christofidellis et al.,
2023), and editing (Liu et al., 2022). Building on this trend, recent work has
also emerged to enable this behavior via instruction-following (Zhao et al.,
2023a; Fang et al., 2023; Guo et al., 2023) and dialogue-based systems for
molecules (Liu et al., 2023a; Cao et al., 2023; Ye et al., 2023; Zhao et al.,
2024).

##  Appendix E Example of Automated Planner-driven Execution

In this example, we demonstrate the reasoning pathway followed by
ChemReasoner-Planner from the root node in a search tree to the best answer.

We provide the question and then present the internal reasoning generated by
the LLM to determine the next search criteria. We interleave each planning
phase with the question and answers.

Question: Generate a list of top-5 catalyst-label that exhibit high activity
for the CO2 to methanol conversion reaction. Provide scientific explanations
and return a list of top 5 answers and their explanations as a list of pairs.
Let’s think step-by-step.

### Level 0: Answer from User-specified Query

1\. Copper (Cu): Cu-based catalysts are the most widely used in the CO2 to
methanol conversion process, mainly due to their high catalytic activity and
selectivity towards methanol. The reaction mechanism involves the initial
dissociation of CO2 on the Cu surface into CO and O, followed by the
hydrogenation of CO to form methanol.

2\. Zinc (Zn): Zinc is commonly used in conjunction with copper (Cu/Zn) for
the CO2 to methanol conversion. Zinc does not directly participate in the
catalysis but enhances the dispersion of copper particles, which increases the
number of active sites and thereby the catalytic activity.

3\. Palladium (Pd): Palladium can catalyze the CO2 to methanol conversion
reaction via a formate pathway. The CO2 molecule adsorbs on the palladium
surface and is reduced to formate, which subsequently gets hydrogenated to
produce methanol.

4\. Ruthenium (Ru): Ruthenium-based catalysts exhibit high activity for the
CO2 to methanol conversion reaction. The CO2 molecule adsorbs on the Ru
surface and undergoes dissociation into CO and O, which are then hydrogenated
to form methanol.

5\. Nickel (Ni): Nickel-based catalysts can efficiently catalyze the CO2 to
methanol conversion via a CO pathway. The CO2 molecule initially dissociates
into CO and O on the Ni surface, followed by the hydrogenation of CO to form
methanol.

### Level 1: Internal monologue of Planner

To address the root question, we need to modify the search state to find
metallic catalysts that are effective in the CO2 to methanol conversion
reaction. Given the existing state, we need to add inclusion criteria that
align with this requirement.

Let’s consider the following factors:

1) Catalyst Type: The root prompt asks for metallic catalysts, so we don’t
need to change the type of catalyst. However, to narrow down our search, we
could specify a subclass of metallic catalysts that are known to be effective
in this reaction. For example, transition metal catalysts such as copper,
zinc, and palladium are known to be active in CO2 to methanol conversion.

2) Inclusion Criteria: Since we’re looking for catalysts that exhibit high
activity for the CO2 to methanol conversion reaction, we should include this
as a criteria. We might also want to include catalysts that are stable under
reaction conditions and have a high selectivity towards methanol.

3) Exclusion Criteria: Since we’re looking for metallic catalysts, we can
exclude non-metallic catalysts. Additionally, we might want to exclude
catalysts that degrade quickly or are toxic.

4) Relationship to Candidate List: We’re looking for catalysts that are
similar to those in our candidate list. This means they should have similar
properties and performance in the CO2 to methanol conversion reaction.

Here are my suggestions:

\- Catalyst Type: [”transition metal catalysts”] \- Inclusion Criteria: [”high
activity in CO2 to methanol conversion”, ”stability under reaction
conditions”, ”high selectivity towards methanol”] \- Exclusion Criteria:
[”non-metallic catalysts”, ”quickly degrading catalysts”, ”toxic catalysts”]
\- Relationship to Candidate List: [”similar to”]

This way, we can find catalysts that are not only similar to those in our
candidate list, but also meet the specific requirements of the root question.

### Level 1: Answer from Planner-generated Query

Based on the requirement of high activity for the CO2 to methanol conversion
reaction, the following metallic catalysts are recommended:

1\. **Rhodium (Rh)**: Rhodium-based catalysts have shown excellent performance
in CO2 hydrogenation to methanol. The high activity of Rh is attributed to its
ability to dissociate the C-O bond in CO2, which is a key step in the
conversion process. Additionally, Rh has an appropriate binding energy for
CO2, allowing for efficient adsorption and desorption of the reactant and
product.

2\. **Platinum (Pt)**: Platinum is known for its high catalytic activity in
various reactions. In the case of CO2 to methanol conversion, Pt can activate
the CO2 molecule by weakening the C-O bond, facilitating the subsequent
hydrogenation steps.

3\. **Iridium (Ir)**: Iridium catalysts are effective in CO2 hydrogenation to
methanol due to their high resistance to CO poisoning, a common issue in these
reactions. Furthermore, Ir has a suitable binding energy for CO2, which
enhances the reaction rate by promoting the adsorption and desorption
processes.

4\. **Gold (Au)**: Gold catalysts have recently been found to be effective in
CO2 to methanol conversion. The high activity of Au is associated with its
ability to activate CO2 by facilitating the breaking of the C-O bond. Also, Au
has a high resistance to CO poisoning.

5\. **Silver (Ag)**: Silver is another promising catalyst for CO2 to methanol
conversion. The strong interaction between Ag and CO2 ensures efficient
activation of the CO2 molecule, leading to high reaction rates. Moreover, Ag
is known for its resistance to sintering, which maintains the catalyst’s
stability and longevity.

### Level 2: Internal monologue of Planner

Based on the current search state, answer and the root question, it is clear
that we are looking for metallic catalysts that exhibit high activity for the
CO2 to methanol conversion reaction. The inclusion criterion is commercial
viability, and there are no exclusion criteria. The recommended catalysts
should be similar to the ones provided in the candidate list.

From the current answer, we see that the recommended catalysts have properties
such as high resistance to CO poisoning, efficient activation of CO2, ability
to dissociate the C-O bond in CO2, suitable binding energy for CO2, high
resistance to sintering, etc.

Based on these, here are some possible actions that we can take:

\- **Catalyst Type:** We can look for other types of metallic catalysts that
have not been included in the current list. For instance, we can search for
”transition metal catalysts” or ”noble metal catalysts”, since these
categories often exhibit high catalytic activities and have similar properties
to the catalysts in the current list.

\- **Inclusion Criteria:** We can add new inclusion criteria based on the
properties of the recommended catalysts in the current answer. For example, we
can include ”high resistance to CO poisoning” or ”ability to dissociate the
C-O bond in CO2”.

\- **Exclusion Criteria:** Since all the recommended catalysts in the current
answer seem to have desirable properties, it may not be necessary to add any
exclusion criteria at this point. However, if we want to narrow down our
search, we could consider excluding catalysts that have ”low resistance to
sintering” or ”poor binding energy for CO2”.

\- **Relationship to Candidate List:** The relationship to the candidate list
can remain as ”similar to”, as we are looking for catalysts that have similar
properties to the ones in the current list.

”catalyst-type”: [”transition metal catalysts”, ”noble metal catalysts”],
”inclusion-criteria”: [”high resistance to CO poisoning”, ”ability to
dissociate the C-O bond in CO2”], ”exclusion-criteria”: [”low resistance to
sintering”, ”poor binding energy for CO2”], ”relationship-to-candidate-list”:
[”similar to”]

### Level 2: Answer from Planner-generated Query

Sure, here is the list of top-5 metallic catalysts that exhibit high activity
for the CO2 to methanol conversion reaction with properties of commercial
viability and high resistance to CO poisoning:

1\. Copper (Cu): Copper-based catalysts, particularly when combined with zinc
oxide and alumina (Cu/ZnO/Al2O3), are the most widely used in the industrial
production of methanol from CO2. Copper has a high catalytic activity and
selectivity towards methanol production. The resistance to CO poisoning is
improved by the presence of ZnO, which can absorb CO2 and present it to the
copper sites in a more reactive form.

2\. Palladium (Pd): Palladium catalysts, particularly when supported on carbon
(Pd/C), show high activity for CO2 reduction to methanol. Pd has the ability
to dissociate CO2 into CO and O, facilitating the subsequent hydrogenation of
CO to methanol. The carbon support provides resistance to CO poisoning by
facilitating the desorption of CO from the Pd surface.

3\. Ruthenium (Ru): Ruthenium catalysts, especially when supported on carbon
(Ru/C), show good activity and selectivity for CO2 reduction to methanol. Ru
has a high CO dissociation energy, which helps resist CO poisoning. The carbon
support helps disperse the Ru particles to maximize the catalytic surface
area.

4\. Nickel (Ni): Nickel catalysts, particularly when supported on alumina
(Ni/Al2O3), have been shown to catalyze the CO2 to methanol reaction. Nickel
has a lower CO binding energy than other metals, which helps resist CO
poisoning. The alumina support helps disperse the Ni particles and stabilize
the catalyst.

5\. Cobalt (Co): Cobalt catalysts, particularly when supported on ceria
(Co/CeO2), can catalyze the CO2 to methanol reaction. Cobalt has a relatively
low CO binding energy, which helps resist CO poisoning. The ceria support can
enhance the CO2 adsorption and activation, improving the catalytic activity.

Comparing to the initial list, these metals are generally cheaper and more
abundant, making them more commercially viable. They also show a good
resistance to CO poisoning due to their lower CO binding energies and/or the
use of specific supports.

### Level 3: Internal monologue of Planner

Based on the $searchState, $rootPrompt, $currentQuestion, and $currentAnswer,
here are some possible action suggestions:

1) ”catalyst-type”: The current catalyst type is ”metallic catalysts”. The
$currentAnswer provides a list of metallic catalysts that are commercially
viable and have high resistance to CO poisoning. Based on this, we could
further specify the type of metallic catalysts to explore. For example, we
could look into ”transition metal catalysts” or ”noble metal catalysts”. These
categories are subclasses of metallic catalysts and could potentially offer
new candidates that have not been considered yet.

2) ”inclusion-criteria”: The current inclusion criteria are ”commercial
viability” and ”high resistance to CO poisoning”. The $currentAnswer explains
that these properties are achieved through the choice of metal and the use of
specific supports. This suggests that we could add criteria related to these
factors. For instance, we could add ”use of specific supports to enhance
catalytic activity and resistance to CO poisoning” as a new inclusion
criteria.

3) ”exclusion-criteria”: The current exclusion criteria is an empty list.
Based on the $currentAnswer, all the suggested catalysts are not only
commercially viable and resistant to CO poisoning, but also have good
catalytic activity for the CO2 to methanol conversion reaction. Therefore, we
could add ”low catalytic activity for the CO2 to methanol conversion reaction”
as an exclusion criterion.

4) ”relationship-to-candidate-list”: The current relationship to the candidate
list is ”similar to”. Since the $currentAnswer provides a new list of
catalysts that are different from the initial list, we could change the
relationship to ”different from”.

”catalyst-type”: [”transition metal catalysts”, ”noble metal catalysts”],
”inclusion-criteria”: [”use of specific supports to enhance catalytic activity
and resistance to CO poisoning”], ”exclusion-criteria”: [”low catalytic
activity for the CO2 to methanol conversion reaction”], ”relationship-to-
candidate-list”: [”different from”]

These changes would allow us to explore new categories of metallic catalysts
while focusing on the important properties and excluding those with low
activity for the CO2 to methanol conversion reaction.

### Level 3: Answer from Planner-generated Query

Let’s begin by analyzing the original list of catalysts:

1\. Copper (Cu): Commercially viable and shows high resistance to CO
poisoning. It has been extensively used in industry for the conversion of CO2
to methanol due to its high catalytic activity. Cu-based catalysts, especially
when combined with zinc oxide and alumina, exhibit remarkable performance.

2\. Palladium (Pd): While Pd-based catalysts show high catalytic activity for
many reactions, they are known to be susceptible to CO poisoning, which could
limit their effectiveness in the CO2 to methanol conversion.

3\. Ruthenium (Ru): Ru-based catalysts exhibit high catalytic activity and
resistance to CO poisoning. However, their high cost and limited availability
may hinder their commercial viability.

4\. Nickel (Ni): Ni-based catalysts are commercially viable due to their
relatively low cost. However, they exhibit moderate activity for the CO2 to
methanol conversion and could be susceptible to CO poisoning.

5\. Cobalt (Co): Co-based catalysts are less active for the CO2 to methanol
conversion compared to the other catalysts listed. Also, they are susceptible
to CO poisoning.

Based on the above analysis, let’s create a new list of metallic catalysts
that meet the specified requirements:

1\. Rhodium (Rh): Rh-based catalysts show high catalytic activity for the CO2
to methanol conversion and have high resistance to CO poisoning. However,
their high cost may limit their commercial viability.

2\. Silver (Ag): Ag-based catalysts exhibit high catalytic activity for the
CO2 to methanol conversion, and are resistant to CO poisoning. They are also
commercially viable due to their relative abundance and lower cost compared to
noble metals.

3\. Iron (Fe): Fe-based catalysts are commercially viable due to their low
cost and abundance. They show moderate to high activity for the CO2 to
methanol conversion and have good resistance to CO poisoning.

4\. Platinum (Pt): Pt-based catalysts have high catalytic activity and
resistance to CO poisoning. However, their high cost may limit their
commercial viability.

5\. Gold (Au): Au-based catalysts show high catalytic activity for the CO2 to
methanol conversion and have high resistance to CO poisoning. However, their
high cost may limit their commercial viability.

This list includes catalysts that exhibit high catalytic activity for the CO2
to methanol conversion and high resistance to CO poisoning. However, the
commercial viability of some of these catalysts may be limited due to their
high cost.

### Level 4: Internal monologue of Planner

From the $currentAnswer, it seems that we have already achieved the intent of
the $rootPrompt. We have a list of top-5 metallic catalysts that exhibit high
activity for the CO2 to methanol conversion reaction, and have given
scientific explanations for each of the metallic catalysts. The list has also
been compared to the initial list of catalysts.

However, if we wanted to refine the search further, we could consider the
following actions:

1) ”Change the type of catalyst to search for”: We could limit our search to
only noble metallic catalysts. Noble metals like Rhodium, Platinum, and Gold
have already been identified as having high catalytic activity and resistance
to CO poisoning. However, their commercial viability may be limited due to
their high cost. Including less costly noble metals like Silver might provide
a good balance between catalytic performance and commercial viability.

2) ”Add a new inclusion criteria”: We could add ”relative abundance” as a new
inclusion criteria. This would ensure that we only consider catalysts that are
not only commercially viable and resistant to CO poisoning, but also readily
available.

3) ”Add a new exclusion criteria”: We could add ”high cost” as a new exclusion
criteria. This would help us exclude catalysts that are not commercially
viable, despite their high catalytic activity and resistance to CO poisoning.

4) ”Change the relationship to the candidate list”: Given that we already have
a list of suitable catalysts, we could modify the relationship to be
”different from”. This would help us identify new and potentially better
catalysts.

Here’s how we could represent these suggested actions:

”catalyst-type”: [”noble metallic catalysts”], ”inclusion-criteria”:
[”relative abundance”], ”exclusion-criteria”: [”high cost”], ”relationship-to-
candidate-list”: [”different from”]

These suggestions would help us refine our search and potentially identify new
metallic catalysts that are commercially viable, resistant to CO poisoning,
readily available, and not excessively costly.

### Level 4: Answer from Planner-generated Query

The list provided contains both noble (Rh, Ag, Pt, Au) and non-noble (Fe)
metallic catalysts. For the CO2 to methanol conversion, a noble metallic
catalyst is desirable for its high resistance to CO poisoning which is
important as CO is an intermediate in this reaction. Iron, however, is not a
noble metal and does not have this property.

1\. Palladium (Pd): Palladium is a noble metal that can be used for CO2 to
methanol conversion. It has high resistance to CO poisoning. It is also
commercially viable since it is widely used in the industry for hydrogenation
reactions. Moreover, palladium can dissociate CO2 into CO and O, which is
beneficial for methanol synthesis.

2\. Ruthenium (Ru): Ruthenium is another noble metal that can be used for this
conversion. It shows high resistance to CO poisoning and is commercially
viable. Ruthenium can adsorb CO2 and hydrogen simultaneously, facilitating the
conversion to methanol.

3\. Rhodium (Rh): Rhodium is a noble metal with high catalytic activity for
the CO2 to methanol conversion. It is resistant to CO poisoning and is
commercially viable. It has an excellent ability to dissociate CO2 and
hydrogen, facilitating the conversion to methanol.

4\. Platinum (Pt): Platinum is a noble metal that is highly resistant to CO
poisoning. It is commercially viable and is often used in catalysis due to its
high reactivity. Platinum can dissociate CO2 into CO and O, which is
beneficial for methanol synthesis.

5\. Gold (Au): Gold is a noble metal that is resistant to CO poisoning. It is
commercially viable and is known to catalyze the CO2 to methanol conversion,
especially when used as nanoparticles. Gold can adsorb and activate CO2,
facilitating its conversion to methanol.

Therefore, the updated list of noble metallic catalysts suitable for the CO2
to methanol conversion, considering the given criteria, is [’Palladium (Pd)’,
’Ruthenium (Ru)’, ’Rhodium (Rh)’, ’Platinum (Pt)’, ’Gold (Au)’].

### Level 5: Internal monologue of Planner

Looking at the $rootPrompt, the task is to generate a list of top-5 metallic
catalysts. The $currentAnswer provides a list of noble metallic catalysts.
However, the $rootPrompt does not restrict the type of metallic catalysts to
noble ones. Therefore, one possible action is to expand the search to include
non-noble metallic catalysts.

Furthermore, the $currentAnswer includes scientific explanations for each
catalyst. These explanations can be used to refine the inclusion and exclusion
criteria. For example, the ability to dissociate CO2 into CO and O is a
desirable property mentioned multiple times in the explanations. Therefore,
this can be added as a new inclusion criterion. On the other hand, low
catalytic activity for the CO2 to methanol conversion reaction is the current
exclusion criterion. We can add another exclusion criterion based on the
explanations provided. For example, we can exclude catalysts that cannot
adsorb and activate CO2, as this property is also mentioned as being
beneficial for the conversion to methanol.

The relationship to the candidate list can be changed to ”different from” to
find new metallic catalysts that are not in the current list.

Here are the suggestions based on the above analysis:

”catalyst-type”: [”non-noble metallic catalysts”], ”inclusion-criteria”:
[”ability to dissociate CO2 into CO and O”, ”ability to adsorb and activate
CO2”], ”exclusion-criteria”: [”cannot adsorb and activate CO2”],
”relationship-to-candidate-list”: [”different from”]

These changes will help in finding new metallic catalysts that are not noble
metals but still have high catalytic activity for the CO2 to methanol
conversion reaction. This is in line with the intent of the $rootPrompt.

### Level 5: Answer from Planner-generated Query

The CO2 to methanol conversion reaction is a crucial process in catalysis and
has been extensively studied. Here are my top-5 recommendations for noble
metallic catalysts that exhibit high activity for this reaction, along with
the scientific explanations:

1\. **Palladium-Gold (Pd-Au) Alloy**: This alloy catalyst has shown excellent
catalytic performance for CO2 reduction to methanol. The Pd-Au alloy,
especially when supported on titanium dioxide (TiO2), provides a synergistic
effect where Pd facilitates CO2 activation and dissociation, while Au helps in
reducing the CO intermediate to methanol. The alloy structure also enhances
the resistance to CO poisoning.

2\. **Platinum-Ruthenium (Pt-Ru) Alloy**: Pt-Ru alloy, when supported on
carbon (C), exhibits high activity and selectivity for CO2 to methanol
conversion. The bifunctional mechanism involves CO2 activation and
dissociation on Ru sites and subsequent hydrogenation to methanol on Pt sites.

3\. **Ruthenium-Gold (Ru-Au) Alloy**: The Ru-Au alloy is another excellent
choice for this reaction. Ru helps in dissociating CO2 into CO and O, while Au
aids in the reduction of CO to methanol. The alloy structure also enhances the
resistance to CO poisoning.

4\. **Rhodium-Palladium (Rh-Pd) Alloy**: The Rh-Pd alloy, when supported on
alumina (Al2O3), exhibits high activity for CO2 to methanol conversion. Rh
facilitates CO2 dissociation, while Pd helps in reducing the CO intermediate
to methanol. The alloy structure enhances the resistance to CO poisoning.

5\. **Platinum-Gold (Pt-Au) Alloy**: The Pt-Au alloy, when supported on carbon
(C), shows high activity and selectivity for the CO2 to methanol conversion.
The bifunctional mechanism involves CO2 activation and dissociation on Au
sites and subsequent hydrogenation to methanol on Pt sites.