# Deep Generative Multimedia Children’s Literature

Matthew L. Olson

###### Abstract

Artistic work leveraging Machine Learning techniques is an increasingly
popular endeavour for those with a creative lean. However, most work is done
in a single domain: text, images, music, etc. In this work, I design a system
for a machine learning created multimedia experience, specifically in the
genre of children’s literature. We detail the process for exclusively using
publicly available pretrained deep neural network based models, I present
multiple examples of the work my system creates, and I explore the problems
associated in this area of creative work.

Figure 1: A diagram of my system architecture. An input prompt such as “A
children’s story about a boy and a horse:” results in a video of a read story
with kids music. By leveraging a variety of publicly available Deep Learning
models, I can create stories (GPT-3), images from text (Stable Diffusion), 3d
effects (3d Ken Burns), voice from text (Mimic3), and kids music (JukeBox).
The output video can be viewed at %“footnotesize–%Shared ̵attributes ̵across
̵distinct ̵and ̵independently ̵trained ̵GANs. ̵In ̵(a), ̵the ̵top ̵and ̵bottom
̵show ̵aligned ̵attribute ̵of ̵“texttt–hair-bangs˝ ̵and ̵“texttt–hair-color˝
̵respectively. ̵In ̵(b), ̵the ̵proposed ̵method ̵is ̵able ̵to ̵find ̵an
̵unexpected ̵“texttt–pencil-sketch˝ ̵attribute ̵direction ̵in
̵CelebA.˝https://youtu.be/YNvgHxaotQI

##  1 Introduction

The popularity in Deep Learning (DL) based creative ventures continues to grow
without any signs of slowing down. Unpredictable to many a decade ago, the
achievements of DL models in a variety of creative domains are spectacular in
their own right.

In this work, I combine multiple publicly available DL models to create a
fully automated system in the generation of multimedia entertainment. The
framework I propose is general enough for any genre of entertainment, but I
focus on the task of children’s video literature production.

A single DL model can produce amazing results for one domain: creating
artistic faces (Karras et al. 2020), music (Dhariwal et al. 2020), images from
text (Ramesh et al. 2021), and complete short stories (Brown et al. 2020).
However, each of these models only functions in one or two domains. By
combining a multitude of models, I create a system for generating children’s
video literature. However, this newfound creative achievement is not without
major societal implications.

Internet videos targeted towards children gained much attention a few years
ago when advertisers saw the bizarre and terrifying children videos on the
internet (Maheshwari 2017). While the policies of video content providing
websites significantly changed, the underlying problem of video content being
unsettling and novel still plagues user-content video applications. This work
focuses on the creation of child-friendly content, but in order to shed light
on the ease at which unscrupulous actors could create their own videos, I also
explore how creating a fully automated system targeted for children will tend
to result in disturbing content.

The paper is structured as follows. Section $2$ provides a detailed
explanation of the DL models used in my system. Section $3$ explores the
results generated by my carefully constructed framework to produce child
friendly content, as well as the unintended creation of unfriendly content.
And I end with a discussion in section $4$ with an analysis of ethical
implications of my work, where I detail why I explore friendly and unfriendly
content. Examples of the full videos my system created can be seen on my
dedicated YouTube channel for this project at
https://www.youtube.com/channel/UCnfcG7aaqHtldpzEWvqaIXQ (no children were
shown these videos in the making of this work).

##  2 Methods

Here I describe the steps for combining publicly available pretrained DL
models; a diagram depicting the process is shown in figure 1.

### Language Model

The central model of this system is GPT-3 (Brown et al. 2020) 111While model
weights for GPT-3 are not available for download, an API is available for a
nominal cost (less than $\$0.10$ per story). And a free version, OPT (Zhang et
al. 2022), does exist for those with compute power to run it.. GPT-3 is an
auto-regressive large language model. Given a series of tokens
$T=t_{1},t_{2},...,t_{n}$, GPT-3 is trained to maximize the next token
$P(t_{n+1}|T)$ which is the probability of token $t_{n+1}$ occurring after
tokens $T=t_{1},t_{2},...,t_{n}$. By using an extremely large transformer-
based deep neural network with 175 billion parameters, along with half a
trillion tokens to train on, GPT-3 is able to convincingly generate text (of
length $m$) $t_{n+1},...,t_{n+m}$ given a prompt $T$ by iteratively appending
$t_{n+i}$ to $T$.

To generate an initial children’s story $G$, I prompt GPT-3 with $T_{G}=$ “The
following is a children’s story about a $X$ and a $Y$ :” , where $X$ and $Y$
selected from a list of boy, girl, or some animal. GPT-3 is very effective at
creating a simple and cohesive story that follow a logical flow.

Let us define $T_{Di}$ as a re-prompt for GPT-3 with the entire story $G$,
followed by the text: “The story has pictures accompanying it. From the
sentence $S_{i}$, here is what the picture looks like: ”, where $S_{i}$ is one
of the sentences from the story. The output of GPT-3, given $T_{Di}$ is then a
single description of an image associated with $S_{i}$, which can more
formally be written as $D_{i}=GPT\text{-}3(G,T_{Di})$. I then save the output
$D_{i}$ for of each of these sentence descriptions to be used for the text-to-
image model.

### Text-to-Image Model

Diffusion models are particularly interesting as they have been combined with
language models to produce conditional image generation. By pretraining a
language and vision encoder to jointly maximize the similarity of text and
image embedding pairs, the language encoder learns a rich feature space which
can be repurposed for text-conditioned image generation. Stable Diffusion
(Rombach et al. 2021) is the model of choice for text-to-image generation as
it is trained heavily on artistic images and is freely available to download.
Stable Diffusion is composed of a text encoder and a denoising autoencoder
such that for a given input image description $D$, it creates a text-
conditioned image $I=StableDiffusion(D)$. As Stable Diffusion is trained on a
large dataset, LAION (Desai et al. 2021), of $5$ billion aesthetically
pleasing text-image pairs from the internet, and it can generate a wide
variety of artistic images given a prompt.

### Static Video Enhancement

While static images for a video is a good replication of a physical storybook,
I include another publicly available model to create a 3d Ken Burns Effects
(Niklaus et al. 2019). By combining a depth prediction DL model along with a
context-aware synthesis network, this recent state-of-the-art model can take
any static picture $I$, separate different elements from each other (like
foreground objects and background objects), and create a zoom/pan/rotation
video around those objects as if the image were 3d. By applying this model to
the StableDiffusion images, I am able to create much more dynamic frames for
the videos $K=3d-KenBurns(I)$, adding an extra degree of freedom for each
frame. An example of the effect applied to a scene is shown in figure 2.

Figure 2:  An example of the 3d ken burns effect applied to one of the video
scenes. The foreground is zoomed in, while the background contents (clouds in
this example) are left unchanged.

### Text-to-Speech Model

To finish off the storytelling, I use the Mimic3 text-to-speech DL model
(Gesling 2022). Mimic3 is based off VITS (Kim, Kong, and Son 2021), which is a
non-autoregressive model that directly predicts speech waveforms $W=Mimic3(G)$
from input text (i.e., a generated story $G$). Mimic3 is an ideal piece of
software as it has many choices for what voice to use, each of which is a good
replica of a human voice. Mimic3 has many customization options, such as
speech speed, which enables dynamic and easy application to the story. Other
non-DL text-to-speech methods sound much more robotic and take away from the
overall feel of the videos.

### Music Models

The emotional valence of video scenes are significantly impacted by
accompanying music (Ansani et al. 2020). The choice of music for my proposed
multimedia is crucial for setting the tone. There are two major choices for
selecting music: models that create raw sounds (wav files) or models that
create notes (midi files). In this work, I investigate two DL models: JukeBox
(Dhariwal et al. 2020) to create wav files and Perceiver-AR (Hawthorne et al.
2022) to create midi files.

JukeBox leverages a VQ-VAE (Van Den Oord, Vinyals et al. 2017) DL model to
compress segments of a wav file into a significantly smaller latent
representation, where it then auto-regressively predicts waveform segments (in
the latent space) in order to create music with lyrics. While this model takes
many hours to create one minute of music $\mathbf{M}=JukeBox()$, it has a wide
variety of artists and genres to choose from including upbeat children’s music
artists.

Lyrical Removal Model One drawback of the JukeBox model is that it has no
explicit option to disable lyrics. I use Vocal-Remover (tsurumeso 2022) a
which is based off of MMDenseNet (Takahashi, Goswami, and Mitsufuji 2018),
which takes an input song and returns two audio tracks: the singing and the
instruments. And while this model does not perform perfectly with generated
music, it does de-emphasize the singing in a given song. I therefore wrap
JukeBox with this model to significantly soften the singing in my generated
tracks.

Perceiver-AR is the other option for creating music by modeling the physical
process of playing an instrument as encoded by a midi file. Through training
on publicly available classical music, any general auto-regressive DL model
can easily be adapted for this type of data and the Perceiver-AR is a recent
high quality DL model for such predictive modeling of music
$\mathbf{M}=Perceiver\text{-}AR()$ by leveraging large contexts for
prediction. The music produced by this model takes only seconds to create and
sounds better than a randomly played piano– and when it comes to ease of use
goes for publicly available music generation models, the Perceiver-AR is
significantly easier to setup to create music when compared to JukeBox. I
include its use in my framework and explore the consequences of its use below.

An algorithmic description of the entire multimedia children’s literature
generation is described in algorithm 1.

Algorithm 1 Psuedocode for Multimedia Generation

$\mathrm{Generate\\_Video}(X,Y)$

$G=GPT\text{-}3(T_{G}(X,Y))$, Create story from prompt

$\mathbf{K}=[]$, Initialize empty list of scenes

for Sentence $S_{i}$ in $G$ do

$D_{i}=GPT\text{-}3(T_{Di}(S_{i}))$, Image description

$I_{i}=StableDiffusion(D_{i})$, Image

$K_{i}=3dKenBurns(I_{i})$, Dynamic scene

Append $K_{i}$ to $\mathbf{K}$

end for

$W=Mimic3(G)$, Generate speech

$M=JukeBox()$, Generate music

$V=FFmpeg(\mathbf{K},W,M)$, Concatenate into video

Return $V$

Figure 3:  Examples from two of our stories, where we hand-picked the frames
for each scene. These images are generally cute and follow a consistent
narrative flow for the given video. Figure 4:  Examples from using
StableDiffusion in a fully automated way, where images are often unsettling
and would need significant refinement in order to be ready for a children’s
media setting.

### Detailed Implementation

GPT-3 is very straightforward to use with their web-based interface. I use
default parameters with the “text-davinci-002” model. This model is the most
powerful and consistently provides excellent stories and image descriptions.

Mimic3 has many options for speaker’s voice, language, and speed. I use the
free US English “VCTK” model, with a 2.1x speed scaling to slow the speech
down.

JukeBox is trained on thousands of artists with many different genres. I
select two popular children’s music artists (The Wiggles and Raffi) and
randomly sample a minute of unconditional music. The output wav file is then
run through Vocal-Remover.

Perciever-AR needs to have its midi outputs converted into mp3 files, for
which I use the window’s operating system’s default sound font file, along
with VLC (VideoLan 2006) to perform the conversion.

Stable Diffusion still requires some engineering of the input in order to
generate interesting outputs that correspond to the desired image. For each
sentence description of the story, I append a few words such as “extremely
detailed, textured, high detail, 4k” in order to produce high resolution
outputs, along with other major themes like “Funko Pop”. Stable Diffusion can
easily be run multiple times per input to create many different images to
select from, in order to find images that are not unsettling, I generate $100$
different examples for every image description and pick one that I find cute
and interesting. I also investigate a fully automated setup where only a
single instance for each description is generated, finding it to have serious
issues.

3d Ken Burns Effects provides an easy to use github implementation. I use the
default zoom/unzoom, though other effects can easily be applied to my frames.

All output files from each of these above models are concatenated and combined
using FFmpeg (Tomar 2006).

##  3 Results

Here I present two videos generated by my system, which can be seen on my
YouTube channel. Example frames from both videos are shown in figure 3; while
the style is not consistent scene to scene, these images seem appropriate for
their context within the story.

The first video, https://youtu.be/YNvgHxaotQI is about a boy and a horse. The
images are cohesive: showing a horse, boy, or farmer in the various scenes.
The generated song by Raffi fits well, filling in pauses between sentences.
The second video, https://youtu.be/PaivgKpS5Bo is about a girl, a dog and a
squirrel. Again, the scenes a overall consistent and the musical accompaniment
of The Wiggles enhances the tone of the video. The story itself even contains
a moral about boundaries. Overall, these videos are cute and something I could
imagine a child watching on YouTube (though I do reiterate that no children
were shown these videos at any point during the production of this work).

### Easy production, alarming results

On the other hand, in my efforts to create end-to-end fully automated
children’s multimedia literature, I stumbled upon content that is the
antithesis of my goals: moderately disturbing videos. Two aspects contribute
to this problem: the music and uncurated images. The music produced Perceiver-
AR takes only seconds to create and seems like a decent choice as it sounds
better than a randomly played piano, however, it has the terrible result of
sounding like it belongs in a horror film. Additionally, Stable Diffusion has
significant quality issues when generating faces. Examples of this generation
can be seen in figure 4.

We now present two additional videos from my YouTube channel, which have been
described by viewers as ”mildly terrifying”. The first video,
https://youtu.be/AyeOWBPduM8 , uses the same story a boy and a horse. The
video shows strange artefacts around horse generation and cannot accurately
represent faces. The second video https://youtu.be/sXEIMki9fZI uses the same
story about a girl, a dog and a squirrel. This video is a bit more
frightening, with the images being more artistic and the animals much less
defined than the horses of the previous video.

#### Deep Generative Multimedia Horror Literature?

Lastly, I present an alternative approach in the following video
https://youtu.be/JCe7KkVT3Ds. Seeing how scary the music created by Perceiver-
AR is, I prompted GPT-3 with ”demon” and ”cat”, to get horror-based video,
rather than a video for children. This story is significantly longer and not
written in the third-person perspective. The first-person nature of the story
means each frame has less characters to work with and makes more images that
do not match the story. The long run-time of the story also shows the
importance of the 3d ken burns effect, as using only the default zoom/unzoom
becomes tedious by the end of this story. I hoped this direction of multimedia
production would be more fruitful, but I leave multi-genre multimedia
production for future work.

##  4 Discussion

### Ethical Implications

We did not intend to create a children’s video generator that produced
disturbing content, the unsettling videos shown above were an unintended
consequence of blindly building a fully automated system. While I could have
crafted a version of this work with no mention as to these unfortunate videos,
I believe it is my responsibility to disclose its accidental production.

We understand that some may find such a demonstration to be unacceptable, that
belief (in my opinion) is similar to security through obscurity. It is better
to plainly show how even those with no ill intentions can accidentally
generate this type of content. Our hope is that by including these examples,
the creative machine learning community will be more aware of the
unintentional side effects of publicly releasing their models. Furthermore, I
hope to bring this content to light for parents, as the responsibility for
children’s video content consumption should fall on informed parents who are
aware of the dangers in user generated video content.

### Conclusion

In this work, I proposed an system for creating children’s multimedia
literature. I showed that by combining a variety of pretrained Deep Learning
models, full video stories are relatively easy to automatically generate. I
discussed the practical application of each DL model and how each fit into the
overall system. Lastly, I presented multiple different videos, analyzed their
contents, and showed how my system can create good and bad children’s videos.

## References

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# Non-polaritonic effects in cavity-modified photochemistry

Philip A<EMAIL_ADDRESS>Wai Jue Tan1, Vasyl G. Kravets2,
Alexander N. Grigorenko2 and William L<EMAIL_ADDRESS>

1Department of Physics and Astronomy, University of Exeter,
Exeter, EX4 4QL, United Kingdom
2School of Physics and Astronomy, University of Manchester,
Manchester, M13 9PL, United Kingdom

###### Abstract

Strong coupling of molecules to vacuum fields has been widely reported to lead
to modified chemical properties such as reaction rates. However, some recent
attempts to reproduce infrared strong coupling results have not been
successful, suggesting that factors other than strong coupling may sometimes
be involved. Here we re-examine the first of these vacuum-modified chemistry
experiments in which changes to a molecular photoisomerisation process in the
UV-vis spectral range were attributed to strong coupling of the molecules to
visible light. We observed significant variations in photoisomerisation rates
consistent with the original work; however, we found no evidence that these
changes need to be attributed to strong coupling. Instead, we suggest that the
photoisomerisation rates involved are most strongly influenced by the
absorption of ultraviolet radiation in the cavity. Our results indicate that
care must be taken to rule out non-polaritonic effects before invoking strong
coupling to explain any changes of chemical properties arising in cavity-based
experiments.

Strong light-matter coupling occurs when the rate of photon exchange between
an ensemble of molecular resonators and a confined electromagnetic mode
exceeds losses to the environment [1, 2]. This leads to the formation of two
polariton modes, separated by an energy $\hbar\Omega$, that inherit properties
from both the light and matter states [3, 4]. Strong coupling has been
investigated in fields such as lasing [5], Bose-Einstein condensation [6] and
singular optics [7]. Perhaps the most exciting area of study is polaritonic
(or vacuum-modified) chemistry [8, 9, 10, 1, 11, 12]: if, as hoped, strong
coupling changes the energy levels of molecules and their coherent
interaction, can it modify chemical processes? It has been reported that
strong coupling of electronic transitions in molecules to visible light can
suppress photochemical processes [13, 14, 15, 16, 17]. More attention has been
given to the potential of strong coupling of vibrational modes in molecules to
infrared radiation, known as vibrational strong coupling (VSC) [18, 19, 20].
VSC has been reported to modify chemical processes including reaction rates
[21, 22], enzyme activity [23, 24] and reaction pathways [25, 26]. While the
idea of modifying chemical processes using light is not a new one [27, 28, 29,
30], polaritonic chemistry is especially exciting because the light involved
is not introduced by for example a laser beam, rather it is strong coupling to
vacuum electromagnetic fields that is thought to be sufficient to modify
chemical processes, even in the absence of incident light [13].

The claims of polaritonic chemistry are compelling, but controversy remains
[31, 32]; a full understanding has yet to be developed. Two recent attempts to
reproduce VSC experiments were unsuccessful [33, 34] and some theoretical
works suggest that VSC may not produce measurable changes to the chemical
processes they considered [35, 36]. Many VSC experiments have been performed
on molecules in solution in a flow cell that doubles as an optical microcavity
[11]; and it has been suggested that this experimental design could give rise
to false positives, for example due to the diffusion of reactants or products,
temperature shifts or slight changes in cavity thickness [31].

A major challenge in polaritonic chemistry is the design of suitable control
experiments. Strong coupling requires molecules to be placed in a structure
(such as a planar microcavity) that possesses an electromagnetic mode tuned to
match the energy of the molecular resonance of interest. It has also been
reported that VSC vacuum-modified chemistry requires the molecular resonance
to coincide with the cavity’s normal-incidence resonance (i.e. zero-detuning
at normal incidence), even if the experiment is probed with light at an
oblique angle [21, 25, 1]. This is a challenging idea [37], that is gaining
some theoretical support [38], and is one that we explore for strong coupling
to visible light later in this work. Determining the effect of strong coupling
on chemical processes requires one to compare this ‘on-resonance’ structure
with one where no strong coupling is present: either an ‘off-resonance’ cavity
[23, 39] (where the optical resonance does not match the molecular resonance
of interest) or a ‘cavity-free’ [15, 24] structure (where any confined
electromagnetic modes should be of negligible strength). Some studies use a
combination of ‘off-resonance’ and ‘cavity-free’ structures [13, 21, 25, 26].
Both control approaches have limitations. An appropriate ‘off-resonance’
cavity control requires the optical resonance to be fully detuned from the
molecular resonance and must not inadvertently have a higher- or lower-order
cavity mode tuned to match some other molecular mode which could influence
chemical processes. Meanwhile, all so-called ‘cavity-free’ structures (such as
a planar microcavity that lacks a top mirror [13]) will still support some
manner of confined electromagnetic mode [40], many of which can lead to strong
coupling [41, 42]. Furthermore, changing the structure will modify the
intensity of light incident on the molecules, making direct comparison between
the results from an ‘on-resonance’ cavity and a ‘cavity-free’ structure
difficult, especially when studying photochemical processes [13, 15, 17]. To
be persuasive, polaritonic chemistry experiments might best compare the
results from a series of structures ranging from ones where strong coupling is
present to ones where no strong coupling is present. This could be achieved by
systematically increasing the thickness of a cavity (ideally covering a large,
continuous range of thicknesses where strong coupling to different cavity
modes is observed) or by studying cavities with different levels of field
confinement (for example, by making cavities with mirrors of different
thicknesses). If strong coupling does influence the chemical process, one
could then look for a correlation, for example by plotting reaction rate in a
cavity against the cavity mode detuning. Very few experiments in the
literature fully meet these criteria [43].

A decade ago Hutchison et al. [13], in a pioneering experiment, studied the
photoisomerisation of spyropyran (SPI) to merocyanin (MC), a reaction promoted
by ultraviolet (UV) light. In particular, they examined the photoisomerisation
under conditions for strong coupling to visible light. This study has been
credited with initiating the field of polaritonic chemistry [10, 1]. Hutchison
et al. measured the rate of photoisomerisation in two planar microcavities and
two ‘cavity-free’ structures (i.e. in the absence of a top mirror), each of
different thickness, concluding that strong coupling of MC to a visible-light
cavity mode suppressed the rate of photoisomerisation. In the work reported
here we measured the photoisomersation rate of SPI in a range of planar
structures for a large range of cavity thicknesses. Our experimental results
are broadly consistent with those reported by Hutchison et al.: we found that
photoisomerisation rates can vary dramatically as cavity thickness and
incident angle are changed. However, our detailed analysis leads us to suggest
that strong coupling of visible light to MC has no discernible effect on the
measured photoisomerisation rate. Instead, we suggest that changes in the
photoisomerisation rate are better accounted for by the effectiveness of the
cavity at absorbing the UV light necessary for photoisomerisation. Our results
lead us to suggest caution in interpreting the results of polaritonic
chemistry experiments, in particular highlighting the need for vigilance in
ruling out non-polaritonic effects.

## Results

### Experiment

Spiropyran (SPI) is a photochromic molecule that, when exposed to UV radiation
(energy 3.5 eV), undergoes photoisomerisation to merocyanin (MC) [44].
Exposure to visible light can convert MC back into SPI. The chemical
structures of SPI and MC are given in Fig. 1a and their extinction spectra are
plotted in Fig. 1b (optical constants in Supplementary Section S1.1): SPI is
transparent in the visible whilst MC has a strong absorption peak at 2.2 eV.
Strong coupling to MC’s molecular resonance at 2.2 eV is possible and the
photosensitive nature of SPI/MC makes it an attractive molecule for use in
strong coupling experiments [45, 46, 42, 7]. The strength of light-matter
coupling is directly proportional to the square root of the number density of
coupled molecules [3]. UV irradiation of a SPI-filled cavity gradually
increases the number of MC molecules in the cavity, allowing one to observe
the transition from the weak to strong coupling regime.

We characterised the SPI-MC transition using spectroscopic ellipsometry
(schematic in Fig. 1c). Ellipsometry measures the complex reflection ratio
$\rho$ in terms of the parameters $\Psi$ and $\Delta$ [47]:

$\displaystyle\rho=\frac{r_{p}}{r_{s}}=\tan(\Psi)e^{i\Delta}.$ (1)

$r_{p}$ and $r_{s}$ are the Fresnel reflection coefficients for p- and
s-polarised light, respectively. $\tan(\Psi)$ is the amplitude of $\rho$ and
gives the ratio of the moduli of $r_{p}$ and $r_{s}$. P-polarised modes appear
as local minima in $\Psi$ ($|r_{p}|<|r_{s}|$ when $\Psi<45^{\circ}$) and
s-polarised modes appear as local maxima in $\Psi$ ($|r_{s}|<|r_{p}|$ when
$\Psi>45^{\circ}$). $\Delta$ is the difference in the phase shifts undergone
by p- and s-polarised light upon reflection. Jumps in $\Delta$ correspond to
photonic modes; points where $r_{p,s}=0$ are associated with phase
singularities [7]. The light source of our ellipsometer, a Xe arc lamp
(further details, including emission spectrum, in Supplementary Section S1.4),
emits a small quantity of UV radiation, allowing us to measure the transition
from the weak to strong coupling regime by undertaking a series of repeated
measurements. An example of this is shown in Figs. 1d ($\Psi$) and 1e
($\Delta$). Ellipsometry is routinely used to determine the optical constants
and thicknesses of thin films in multilayer structures. A model is used to
calculate $\Psi$ and $\Delta$ spectra and the best match to experimental
$\Psi$ and $\Delta$ spectra is achieved by adjusting the optical constants and
thicknesses of each film. Therefore, one series of measurements for a cavity
allows us to determine the thickness of the SPI/MC film and to track the
increase in MC concentration as a function of time (and hence
photoisomerisation rate - see Methods), thereby allowing us to monitor the
conversion rate.

Figure 1: Strong coupling in photochromic organic films. (a) Chemical
structures of spiropyran (SPI) and merocyanin (MC). (b) Extinction spectra of
SPI (black line) and MC (blue line) films normalised to extinction through a
fused silica substrate. (c) Ellipsometer schematic. (d,e) Spectra of the
ellipsometric parameters (d) $\Psi$ and (e) $\Delta$ of an Al (10 nm)/SPI (131
nm)/Al (10 nm) structure as a function of UV irradiation time acquired at an
incident angle of $\theta=65^{\circ}$. UV exposure increases the number of MC
molecules in the SPI/MC film; after $\sim 100$ s the MC density is high enough
to allow the cavity mode present in the structure to undergo strong coupling
to the 2.2 eV resonance in MC, resulting in the formation of two polariton
modes. Panel (e) shows the associated creation of a phase singularity similar
to what is reported in ref. [7].

We studied SPI-MC photoisomerisation in three types of structure (see Methods
for fabrication details): planar microcavity (SPI/MC film between two thin
metal films), mirror (SPI/MC film on a thin metal film), and bare substrate
(SPI/MC film on a bare substrate). In each case the range of SPI/MC film
thickness was at least 100-400 nm. This allowed us to measure the SPI-MC
photoisomerisation rate as successive confined electromagnetic field modes
were gradually tuned to match the MC molecular resonance at 2.2 eV. We
primarily used Al thin films since Al cavities can support high-quality cavity
modes in the visible and UV. We repeated our experiments with Ag and Cr films
and reached the same conclusions; see Supplementary Sections S4 and S5.4.

We first studied photoisomerisation in the incident angle range
$45^{\circ}\leq\theta\leq 75^{\circ}$ (the range of our ellipsometer). We then
studied Al cavities at normal incidence to determine whether reports of angle
independence in VSC experiments (that reaction rate modification occurs most
strongly in experiments designed to give zero detuning at normal incidence)
might also apply for strong coupling of MC with visible light.

### Probing photoisomerisation at oblique incident angles

The results from our Al structures probed at $\theta=65^{\circ}$ are presented
in Fig. 2. Sample designs are shown in Figs. 2a,e. In Figs. 2b,c,f,g we show
dispersion plots constructed from $\Psi$ spectra measured for structures with
a range of SPI/MC film thicknesses. SPI dispersion plots (panels b,f) were
constructed from $\Psi$ spectra acquired after 11 seconds of UV exposure, when
the cavities contained a negligible quantity of MC. In the Al cavity (panel b)
we observe a series of sharp cavity modes in the visible region of the
spectrum, where SPI is transparent. SPI becomes strongly absorbing above 3 eV
(see Fig. 1b), resulting in a deterioration of the cavity mode quality. The
dispersion plot for the Al mirror/SPI structure is shown in panel f. This
structure supports a series of leaky modes, alternating between transverse
electric (maxima in $\Psi$) and transverse magnetic (minima in $\Psi$) modes.
These modes are of lower-quality than the cavity modes observed in the Al
cavity structure in panel b; they are also sharpest in the visible and less
well-defined in the UV. The MC dispersion plots (panels c,g) were constructed
from $\Psi$ spectra acquired after 1,100 seconds of UV exposure. The MC
molecular resonance at 2.2 eV strongly couples to the optical modes in the Al
cavity (panel c) and the Al mirror (panel g) structures, giving clear
anticrossing signatures. The mirror structure is commonly used as a ‘cavity-
free’ control, but it is clear from the data that it too supports strong
coupling.

Figure 2: Photochemistry in Al structures. (a,e) Sample designs, (b,d) SPI and
(c,g) MC dispersion plots constructed from $\Psi$ and (d,h) photoisomerisation
rates for (a-d) Al cavity and (e-h) Al mirror structures. All data acquired at
$\theta=65^{\circ}$.

In addition to the initial SPI and final MC spectra presented in Fig.
2b,c,f,g, we acquired additional ellipsometry spectra for these samples every
11 s in the intervening time (see e.g. Figs. 1d,e). To investigate the change
in MC concentration during UV exposure we first determined the optical
constants of the SPI/MC film by fitting to ellipsometry spectra at each time;
see Methods and Supplementary Section 2.1 for further details. We then tracked
the growth in MC concentration by plotting $\alpha(E_{\text{MC}})$, the
absorption coefficient of the SPI/MC film at $E_{\text{MC}}=2.2$ eV (which is
directly proportional to MC concentration, see Supplementary Figure S5) as a
function of time (examples in Supplementary Section S2.2). A simple
exponential function cannot be fit to these data. To quantify the rate of
conversion in a structure we introduce the conversion constant $\kappa$, the
reciprocal of the time it takes for $\alpha(E_{\text{MC}})$ to reach 3.5 µm-1
in that structure. This target value was chosen because it is high enough to
place the system well within the strong coupling regime (see Supplementary
Section S2.1). When plotting $\kappa$, error bars are calculated from the time
interval between successive measurements (11 s). In Figs. 2d,h we plot the
conversion constants for each structure as the SPI film thickness is varied.
Both plots show the same basic trend: $\kappa$ is largest in thinner films
($\sim 5\times 10^{-3}$ s-1 for thicknesses below 100 nm), levelling off at
around $1\times 10^{-3}$ s-1 for the thickest films. SPI is lossy in the UV,
so it is harder for UV to penetrate through thicker films. Therefore, the
optical constants of thicker films will change more slowly than they will for
thinner films, resulting in lower conversion rates. Each plot also shows
smaller, approximately sinusoidal, deviations from this trend that become
weaker for thicker films. These trends are broadly consistent with Hutchison
et al.’s [13] results: an on-resonance cavity (thickness 155 nm in our case)
has a lower reaction rate than both a mirror sample with the same film
thickness (even when accounting for differences in UV intensity impinging on
the sample) and a fully detuned (thinner) off-resonance cavity (see also
Supplementary Section S2.2). However, in contrast to Hutchison et al. [13], we
found the trends in $\kappa$ were independent of the choice of target
absorption coefficient, even if that target value was insufficient for strong
coupling (see Supplementary Section S2.3).

We repeated the experiment for Al cavities probed at $\theta=45^{\circ}$,
$55^{\circ}$ and $75^{\circ}$. The plots of $\kappa$ against SPI/MC film
thickness are given in Supplementary Section S3. While a similar $\kappa$
trend occurs for all $\theta$, the trend is dispersive, i.e. the data show
that the trend varies with $\theta$. We also repeated this experiment at
$\theta=65^{\circ}$ for a series of different structures: Ag cavity, Ag
mirror, Cr mirror and SPI/MC films on bare substrates of Si and amorphous
glass (results in Supplementary Section S4). Data from all structures showed
the same fundamental trend in photoisomerisation ($\kappa$ decreases with
increasing SPI/MC film thickness). The metal-based structures all show the
same additional undulation as observed in Figs. 2d,h; these undulations are
not clear in the metal-free SPI/MC on Si and amorphous glass substrate
structures, which we note provide far weaker field enhancements in the SPI/MC
film than the metal-based structures.

Figure 3: Accounting for photochemical reaction rates. (a-b) Comparing
experimentally determined SPI conversion rates in Al cavities (plotted in Fig.
2d) with calculated SPI absorption normalised to SPI film thickness. (a) The
SPI absorption profiles calculated for $E=2.2$ eV incident at
$\theta=0^{\circ}$ (light green line) and $\theta=65^{\circ}$ (dark green
line) show no meaningful correlation with experimental data. (b) The SPI
absorption profiles calculated for $E=3.5$ eV incident at $\theta=0^{\circ}$
(light blue line) and $\theta=65^{\circ}$ (dark blue line) have much stronger
correlations with experimental data, the best match occurring for
$\theta=65^{\circ}$. (c) Bar chart showing least-squared $R^{2}$ correlation
coefficient between conversion rate profile (as measured at
$\theta=65^{\circ}$) and calculated SPI absorption profiles for a range of
different structures. The mean values of $R^{2}$ for all structures are also
plotted.

### Determining the cause of observed photoisomerisation trends

The data in Figs. 2d,h show that $\kappa$ varies with SPI/MC film thickness,
but what causes these trends? Hutchison et al. [13] suggested that strong
coupling of optical cavity modes to the MC resonance at 2.2 eV can lead to a
reduction of the rate $\kappa$. If so, we might expect a correlation between
the absorption of light of energy $E=2.2$ eV in the SPI film and $\kappa$. It
has also been reported that VSC vacuum-modified chemistry requires the
molecular resonance to coincide with the cavity’s normal-incidence resonance,
even if the experiment is probed with light at an oblique angle [21, 25, 1,
38]. If this is also true when excitonic resonances (rather than vibrational
resonances) of molecules are strongly coupled to visible (rather than
infrared) light, then we might anticipate a correlation between $\kappa$ and
the absorption of light of energy $E=2.2$ eV in the SPI film at normal
incidence. It would also suggest that, as $\theta$ is varied, some features in
the $\kappa$ profile should remain unchanged. These expectations are not met:
our results show the dispersive nature of $\kappa$ (Supplementary Section S3).
In Fig. 3a we re-plot the Al cavity $\kappa$ data in Fig. 2d along with the
calculated light absorbed in the SPI film (normalised to film thickness) for
$E=2.2$ eV light at incident angles of $\theta=0^{\circ}$ (green line) and
$\theta=65^{\circ}$ (orange line). (The amount of light absorbed in the SPI
film is influenced by interference effects caused by the thickness of the SPI
film and the impedance mismatch at each interface: it is not the same as the
intensity of light impinging on the SPI film; see Supplementary Section S5.1
for further details.) For our results to be consistent with Hutchison et al.’s
report that strong coupling suppresses photoisomerisation rates, we would
expect the peaks in the SPI absorption profile at 2.2 eV to coincide with
minima in the $\kappa$ profile. However, from the data it is not clear that
this is the case for either the $\theta=0^{\circ}$ or $\theta=65^{\circ}$ SPI
absorption profile at 2.2 eV.

As an alternative, we consider the influence of the UV intensity inside the
cavity on $\kappa$. In Fig. 3b we plot the absorption profiles for $E=3.5$ eV
(the energy of SPI’s lowest-energy UV absorption peak) at incident angles of
$\theta=0^{\circ}$ (blue line) and $\theta=65^{\circ}$ (purple line). These
both give a much stronger match to $\kappa$ than 2.2 eV: the
$\theta=65^{\circ}$ profile gives the best fit ($\theta=0^{\circ}$ gives an
additional peak at 280 nm that does not match with any feature in the $\kappa$
data). This suggests that the profile of $\kappa$ in Al cavities when
illuminated at $\theta=65^{\circ}$ can be predicted by how effectively the
cavity absorbs UV radiation. In Supplementary Section S5.2 we show that the
SPI absorption profile at 3.5 eV is a significantly better predictor of
$\kappa$ than the SPI absorption profile at 4.6 eV (corresponding to another
peak in SPI absorption, see Fig. 1b). In Supplementary Section S5.4 we
repeated this analysis for all structures studied at $\theta=65^{\circ}$; the
results are summarised in Fig. 3c, where we plot the least-squares $R^{2}$
correlation coefficient between $\kappa$ and the calculated absorption
profiles (some limitations of these summary statistics are outlined in
Supplementary Sections 5.3). In every case, the best predictor of $\kappa$ is
not the SPI absorption at 2.2 eV but the SPI absorption at 3.5 eV. In no case
is the $\theta=0^{\circ}$ profile a better match than the $\theta=65^{\circ}$
profile.

Figure 4: Photochemistry in Al cavities probed at normal incidence. (a-b)
Dispersion plots for (a) SPI and (b) MC constructed from transmission
intensity spectra for a range of cavity thicknesses. (c-d) Photoisomerisation
rates for a range of Al cavity thicknesses (black data points), plotted with
SPI absorption profiles for (c) $E=2.2$ eV, $\theta=0^{\circ}$ (green line)
and (d) $E=3.50$ eV, $\theta=0^{\circ}$ (blue line). The dashed green and blue
lines in panel (a) show the energies at which the absorption profiles were
calculated in panels (c) and (d) respectively.

### Cavity-modified photochemistry at normal incidence

Several VSC experiments have found that vacuum-modified chemistry effects are
strongest at zero detuning, i.e. when the molecular resonance energy matches
that of the cavity mode at normal incidence [21, 25, 1]. This observation is
now gaining theoretical support [37, 38]. To the best of our knowledge no
comparable effect has been reported (either experimentally or theoretically)
for strong coupling to visible light. To determine if a similar effect occurs
with visible light, we studied photoisomerisation of Al microcavities at
normal incidence; our results are presented in Fig. 4. The SPI dispersion plot
(Fig. 4a) reveals the same set of cavity modes (albeit slightly shifted in
energy) as seen in Fig. 2b. Unlike the situation at $\theta=65^{\circ}$, at
$\theta=0^{\circ}$ there is clear anticrossing taking place at $E=3.5$ eV,
suggesting that the electric field confinement of Al cavity modes at normal
incidence is high enough to allow them to strongly couple to the 3.5 eV SPI
absorption peak. The MC dispersion plot (Fig. 4b) shows, as expected, clear
anticrossing at $E=2.2$ eV with Rabi splitting $\hbar\Omega\sim 700$ meV,
consistent with previously reported results [45, 13]. Hutchison et al.
compared the reaction rate under coupling to the first-order cavity mode with
the reaction rate in a thinner, off-resonance cavity; however, calculations in
Supplementary Section S6 indicate that some splitting will be observed in all
cavities likely to be made using spin-coating, leaving a question mark over
such an off-resonance control. In Fig. 4c, we plot profiles of the measured
rate $\kappa$ and the calculated SPI absorption at 2.2 eV. For our results to
be consistent with Hutchison et al.’s findings, then - as noted above - we
would expect to see minima in $\kappa$ matching with peaks in the SPI
absorption at 2.2 eV. While we do see an increase in $\kappa$ between the
first- and second-order cavity mode coupling conditions, this increase is a
poor match with the decrease in SPI absorption; the trend is less decisive at
greater thicknesses. Instead, we find (Fig. 4d) that the UV absorption of SPI
at 3.5 eV is a better predictor of $\kappa$. This suggests, once again, that
strong coupling to MC’s 2.2 eV resonance has little if any measurable effect
on $\kappa$.

These results do not challenge the observation of the normal-incidence effect
in VSC experiments, but they do appear to rule out any analogous effects in
the cavity-modified SPI-MC photoisomerisation process.

## Discussion

Our experiments are not a like-for-like reproduction of Hutchison et al.’s
[13] pioneering work [10, 1]. In particular, the UV illumination conditions in
the two works are quite different. Nevertheless, the experimental results from
both studies are broadly in agreement. However, the more extensive nature of
our study leads us to offer an alternative explanation to that of strong
coupling to MC’s 2.2 eV resonance influencing SPI/MC photoisomerisation. We
suggest that photoisomerisation rates can be increased by enhancing the
cavity’s UV absorption. We substantially varied our irradiation conditions by
studying a range of structures including Al cavities, Ag cavities, and samples
with no top mirrors. By taking account of the way the UV light is absorbed in
each structure we showed that all of our results can be explained by the same
mechanism. Since the rate of a photochemical reaction is the rate of photons
absorbed by the reactant multiplied by the quantum yield of the reaction (i.e.
the number of molecules that complete the reaction per absorbed photon), our
results suggest that strong light-matter coupling with MC does not affect the
quantum yield of the SPI-MC photoisomerisation process. In Supplementary
Section S7 we normalise our reaction rate data to SPI absorption at 3.5 eV
(shown in Figs. 3-4) and find no discernible trend. We did not perform
transient absorption experiments as done by Hutchison et al. [13] since their
transient absorption measurements do not appear to contradict our own
findings. It has recently been argued that transient absorption experiments
are particularly prone to misleading optical signatures, and may often have
their origin in photoexcitation effects other than the generation of polariton
states [48]. Our results do not immediately challenge other reports of strong
coupling modifying photochemistry [14, 15, 17] (especially those where light
strongly couples to the reactant rather than the product). However, these
experiments may benefit from similar re-examinations to verify that changes in
reaction rates are indeed due to strong coupling rather than the inadvertent
enhanced absorption of photons critical for the photochemical process, or some
other effect. Our findings may provide an explanation for the recent work of
Zeng et al. [49], who also report that SPI/MC photoisomerisation is suppressed
when the strong coupling condition is met, but find that fulgicide
photoswitching is accelerated under strong coupling. This could simply be
explained by the two processes being promoted by absorption of different
energy light, resulting in cavity-modified photochemistry being predicted by
different absorption profiles. In the SPI/MC case the off-resonance cavity
more efficiently absorbs UV light than the on-resonance sample; in the
fulgicide case the opposite might be true.

Our results have other important implications. There is a growing appreciation
that planar cavities filled with organic molecules are much more complicated
than previously thought [48, 50], making systematic studies of strong coupling
all the more essential. In VSC, there are concerns around reproducibility [33,
34] and the limited agreement between experiment and theory [31, 32]. We have
shown that what are sometimes called ‘cavity-free’ controls can in fact
support polaritonic states (Fig. 2e-h, Supplementary Section S4) and that it
can be very difficult to fully de-tune cavity and molecular modes (see
Supplementary Sections S2.2 and S6, which show evidence of strong coupling to
MC in highly detuned cavities). Many reports about the effect of vibrational
strong coupling on chemical processes have involved comparison of the results
from on- and off-resonance samples alongside a cavity-free sample [21, 25, 39,
26, 51, 52, 53]. These experiments are unlikely to provide a complete picture
of the rich variety of optical and chemical processes that occur under the
conditions for VSC. It would be helpful if future studies explored a far
greater range of cavity thicknesses, so as to help rule out non-polaritonic
effects [54]. While our results concern cavity-modified photochemistry, we
hope that our work provides a potential blueprint for future VSC studies that
may help to resolve the current differences between experiment and theory.

In conclusion, we have re-examined the SPI-MC photoisomerisation process
inside a microcavity, as originally investigated by Hutchison et al. [13]. We
have shown that photoisomerisation rates can be modified by cavity-based
changes in UV absorption in the SPI/MC film. We found no evidence that strong
coupling to MC’s 2.2 eV molecular resonance needs to be invoked to explain the
observed effect on photoisomerisation. Our results suggest that care must be
taken to account for the influence of non-polaritonic effects in polaritonic
chemistry experiments.

## Methods

### Sample fabrication

SPI (1,3,3-trimethylindolino-6’-nitrobenzopyrylospiran) / PMMA poly(methyl-
methacrylate) films were prepared by dissolving PMMA (molar weight 996 kDa) in
toluene (1 wt$\%$ PMMA). SPI and PMMA powders were purchased from Sigma
Aldrich. SPI was then dissolved in the PMMA-toluene solution with a weight
ratio of 3:2 SPI to PMMA. The solution was then filtered using a syringe
filter of pore size 0.2 µm. SPI films were deposited by drop-casting a small
amount of the solution $\sim 2$ µL slightly off-centre on the sample, and spun
at a wide range of spin speeds, from 1500 rpm (revolutions per minute) to 9000
rpm, to ensure the greatest range of film thicknesses. Ag, Al and Cr films
were deposited by thermal evaporation. Details of Al thin film evaporation are
given in Supplementary Section S1.3. Representative pictures of final samples
are provided in Supplementary Section S1.2. Unlike in our work, Hutchison et
al.[13] added dielectric spacer layers between their SPI and metal films out
of concern that the metal might interfere with the photoisomerisation process.
In Supplementary Section S1.5 we repeated our experiments with spacer layers
and found that including spacers did not alter our conclusions.

### Determination of photoisomerisation rate using ellipsometry

Spectroscopic ellipsometry at oblique angles was carried out using a J. A.
Woollam Co. M-2000XI. The measurement of a structure with a given SPI film
thickness consisted of the collection of 100 ellipsometry spectra (one every
11 seconds). Over the course of this measurement the SPI film was converted to
MC by UV radiation from the ellipsometer’s Xe arc lamp. The thicknesses of the
metal and SPI films were determined from the first measurement (assumed to be
100$\%$ SPI). The MC molecular resonance at 2.2 eV was modelled using a Tauc-
Lorentz oscillator and the optical constants for the SPI/MC film for each of
the 100 measurements was determined by using the Tauc-Lorentz oscillator
amplitude as the fit parameter. The change in MC concentration was monitored
by tracking how the SPI/MC film’s absorption coefficient at 2.2 eV (which was
linearly proportional to the MC concentration in the film) changed with time.
The plotted conversion rates, $\kappa$, were determined by measuring the time
taken for the SPI/MC film to reach a target absorption coefficient at 2.2 eV.
For $\theta=65^{\circ}$, (Figs. 2-3) the target was 3.5 µm-1; for
$\theta=0^{\circ}$, the target was 5.8 µm-1. Further details can be found in
Supplementary Section S2. Transmission measurements were conducted using a
modified J. A. Woollam Co. M-2000F ellipsometer. The same procedure was used
to determine $\kappa$, performing fits to transmission intensity spectra
instead of the ellipsometric parameters $\Psi$ and $\Delta$.

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## Acknowledgements

P.A.T. and W.L.B. acknowledge the support of the European Research Council
through Project Photmat (ERC-2016-AdG-742222: www.photmat.eu). W. J. T.
acknowledges financial support from the Engineering and Physical Sciences
Research Council (EPSRC) of the United Kingdom via the EPSRC Centre for
Doctoral Training in Metamaterials (EP/L015331/1). V.G.K. and A.N.G.
acknowledge the support of Graphene Flagship programme, Core 3 (881603). We
thank Kishan S. Menghrajani for discussions in the preliminary stages of this
work. For the purpose of open access, the authors have applied a Creative
Commons Attribution (CC BY) licence to any Author Accepted Manuscript version
arising.

## Author contributions

W. L. B. and P. A. T. designed the experiment. W. J. T. fabricated samples and
performed calculations. P. A. T. and V. G. K. performed measurements. V. G. K.
and A. N. G. designed and built the modified ellipsometer setup used for
normal incidence measurements in Fig. 4. All authors contributed to
discussions. P. A. T. wrote the manuscript with input from all authors.

## Competing interests

The authors declare no competing interests.

Supplementary Information for:

Non-polaritonic effects in cavity-modified photochemistry

Philip A. Thomas1, Wai Jue Tan1, Vasyl G. Kravets2,

Alexander N. Grigorenko2 and William L. Barnes1

1Department of Physics and Astronomy, University of Exeter,
Exeter, EX4 4QL, United Kingdom
2School of Physics and Astronomy, University of Manchester,
Manchester, M13 9PL, United Kingdom

###### Contents

  1. Competing interests
  2. S1. Further fabrication and sample details
    1. S1.2 Sample images
    2. S1.3 Deposition of Al thin films using thermal evaporation
    3. S1.4 Characterisation of UV light source
    4. S1.5 Photoisomerisation in cavities with PVA spacer layers
  3. S2. Quantifying photoisomerisation rates in Al cavities
    1. S2.1 Modelling absorption in SPI/MC films
    2. S2.2 Comparison of photoisomerisation in strongly coupled and fully detuned cavities
    3. S2.3 Comparison of photoisomerisation rates before and after entering the strong coupling regime
  4. S3. Photochemistry in Al cavities at a range of incident angles
  5. S4. Photochemistry in non-Al structures
  6. S5. Accounting for photochemical reaction rates with calculated SPI absorption
    1. S5.1 Calculation of SPI film absorption
    2. S5.3 Limitations in our analysis
    3. S5.4 Conversion rates and SPI absorption at different energies for all structures
  7. S6. Calculated normal incidence transmittance through thin Al/MC/Al cavities
  8. S7. Quantum yield as a function of cavity thickness

## S1. Further fabrication and sample details

### S1.1 SPI/MC optical constants

Figure S1: Optical constants of (a) SPI and (b) MC films determined using
spectroscopic ellipsometry.

### S1.2 Sample images

Figure S2: (a) Representative picture of a sample used in our experiments. SPI
was spin-coated off-centre to create a large range of SPI film thicknesses on
a single substrate. (b) Representative close-up image of the surface of the
sample. The green ellipse indicates the sample area interrogated in a single
measurement at $\theta=65^{\circ}$. SPI films had effectively uniform
thickness in a single measured region. The thickness of each measured spot was
determined using ellipsometry (typical accuracy $\sim 1$ nm). This allowed us
to effectively create hundreds of cavities with a huge range of thicknesses
(at least 100-400 nm) on a single substrate. (c) Exposed regions were clearly
distinguishable from unexposed regions, eliminating the risk of the same
region being used for two different measurements.

### S1.3 Deposition of Al thin films using thermal evaporation

In thermal evaporation of thin metal films, metal pellets are placed in an
evacuated chamber and heated with an electric current that is gradually
increased until the metal begins to evaporate. A thin film of metal is then
gradually deposited on the target substrate also inside the vacuum chamber.
The target substrate is covered with a shield which can be removed once the
metal deposition rate has stabilised. The deposition of high-quality thin
films requires a slow and steady deposition rate.

The Al pellets typically used for thermal evaporation are covered in a thin
oxide layer with a melting point far greater than that of pure Al. Therefore,
when heating the Al pellets for thermal evaporation, the Al inside the pellet
will melt while the outer oxide layer remains intact. Once the oxide layer is
broken, all the vapour pressure built up inside the pellet is released,
causing a sudden increase in chamber pressure. This means that the Al
deposition rate can rapidly increase, making it hard to control the thickness
of the Al thin film.

To mitigate this effect we employed the following procedure:

  1. 1.

Maintain current at a lower level (before Al starts being deposited) to raise
the Al vapour pressure inside pellets.

  2. 2.

If no deposition happens, gradually increase the current in small increments.

  3. 3.

When the outer oxide layer is broken, the pressure in the chamber suddenly
increases.

  4. 4.

Lower the current to obtain a steady deposition rate of $0.1\sim 0.2$ nm s-1.

  5. 5.

Remove the sample shield to deposit the metal film on the target substrate.

### S1.4 Characterisation of UV light source

Figure S3: (a) Intensity spectrum from ellipsometer’s Xe arc lamp used to
conduct experiments. The lamp has a smooth, continuous emission spectrum in
the ultraviolet. (b) UV intensity spectrum of light from Xe arc lamp passing
through air (black line), after transmission through a 30 nm Ag film (dark
blue line) and after transmission through a 10 nm Al film (light blue line) at
an incident angle of $\theta=65^{\circ}$. The intensity profiles of UV
radiation exposed to samples in various experiments (with Al top mirror, Ag
top mirror, or no top mirror) are therefore very different, yet the results
from all these experiments can be explained by the same mechanism. This
suggests that the SPI$\rightarrow$MC photoisomerisation process dominates in
all our experiments.

### S1.5 Photoisomerisation in cavities with PVA spacer layers

Figure S4: Hutchison et al. [1] suggested that Ag evaporated directly on to
the SPI film could influence the photochemical dynamics of SPI, leading the
authors to produce cavities with PVA spacer layers separating the Ag films and
SPI layer. Here we repeat our experiment using this sample design, schematic
in (a). We observe the same (b) SPI and (c) MC dispersion plots as before
(c.f. Supplementary Figure S(b,c)). In (d) and (e) we observe the same trends
in photoisomerisation rates as we did in previous experiments (c.f.
Supplementary Figure S11(e,f)), and reached the same conclusion (that
photoisomerisation rates are influenced by the cavity’s UV absorption rather
than by satisfying the MC strong coupling condition). We conclude that the
direct deposition of Ag films on the SPI film has a negligible influence on
SPI/MC photochemistry, we conclude that the inclusion of PVA spacer layers is
thus unnecessary in these experiments.

## S2. Quantifying photoisomerisation rates in Al cavities

### S2.1 Modelling absorption in SPI/MC films

SPI is transparent in the visible spectrum while MC has an absorption peak at
$E_{\text{MC}}=2.2$ eV. We tracked the rate of photoisomerisation by studying
how the absorption coefficient at $E_{\text{MC}}$, $\alpha(E_{\text{MC}})$,
changes under UV irradiation. A higher value of $\alpha(E_{\text{MC}})$
corresponds to a greater MC concentration. We determined the optical constants
($n,k$) of the SPI/MC film by fitting to data acquired using spectroscopic
ellipsometry, an experimental technique expressly designed to accurately
determine the optical constants of thin films. The absorption coefficient
$\alpha=4\pi k/\lambda$ is then calculated from $k$, the imaginary component
of the optical constants ($\alpha(E_{\text{MC}})=4\pi
k(E_{\text{MC}})/\lambda_{\text{MC}}$). We performed fits in the visible range
(480 nm $<\lambda<$ 680 nm), where SPI is essentially transparent but MC has a
molecular resonance at 2.2 eV. Such absorption peaks are frequently modelled
with simple Lorentz oscillators; however, we found that the MC molecular
resonance was better modelled with a Tauc-Lorentz oscillator[2], a modified
Lorentz oscillator that is used to model absorption in amorphous materials
with zero absorption below a defined bandgap energy. This was achieved by
adding the following term to the dielectric function for MC:

$\displaystyle\epsilon_{TL}=\epsilon_{1}+i\epsilon_{2},$

where

$\displaystyle\epsilon_{1}$
$\displaystyle=\frac{2}{\pi}P\int_{Eg}^{\infty}\frac{\xi\epsilon_{2}(\xi)}{\xi^{2}-E^{2}}\text{d}\xi$

and

$\displaystyle\epsilon_{2}$
$\displaystyle=\left[\frac{AE_{o}\gamma(E-E_{g})^{2}}{\left(E^{2}-E_{o}^{2}\right)^{2}+\gamma^{2}E^{2}}\cdot\frac{1}{E}\right]$

for $E>E_{g}$, else $\epsilon_{2}=0$. $A$ (units eV) is the amplitude,
$\gamma=0.315$ eV is the full width at half maximum, $E_{o}=2.175$ eV is the
energy of the resonance and $E_{g}=1.2$ eV is the energy of the band gap. This
model is appropriate for MC since the absorption peak at 2.2 eV can become
very strong while the film remains transparent at lower energies.

Figure S5: (a) The effect of Tauc-Lorentz oscillator amplitude on
$\alpha(E_{\text{MC}})$ (left axis, black line) and $\hbar\Omega$ (right axis,
green lines). (b) Relationship between $\alpha(E_{\text{MC}})$ and
$(\hbar\Omega)^{2}$.

In Figure S5 we plot the relationship between $A$, $\alpha(E_{\text{MC}})$ and
the Rabi splitting $\hbar\Omega$. In panel (a) we see a linear relationship
between $\alpha(E_{\text{MC}})$ and $A$, with $\alpha(E_{\text{MC}})$
remaining small but slightly positive at $A=0$ resulting from the tail end of
strong absorption peaks in the UV. As expected, $\hbar\Omega$ increases with
increasing $A$. The electric field strength inside a cavity is greater at
$\theta=0^{\circ}$ than at $\theta=65^{\circ}$: hence, strong coupling at
$\theta=0^{\circ}$ requires a lower value of $A$ and yields a greater
magnitude of splitting than it does at $\theta=65^{\circ}$. In panel (b) we
plot $(\hbar\Omega)^{2}$ against $\alpha(E_{\text{MC}})$ and observe a linear
relationship between the two quantities. Since $\hbar\Omega\propto\sqrt{N}$
(where $N$ is the number of MC molecules in the film), this indicates that
there is a linear relationship between $\alpha(E_{\text{MC}})$ and the number
of MC molecules in the film. We therefore conclude that studying the growth of
$\alpha(E_{\text{MC}})$ with time is a suitable way of tracking the growth of
MC concentration in the SPI/MC film.

We quantified the rate of conversion in a given structure with a conversion
constant $\kappa$, the reciprocal of the time it takes for the SPI/MC film to
reach $A=0.70$ eV i.e. $\alpha(E_{\text{MC}})=3.5$ µm-1 in that structure.
This target value was chosen because it is high enough to place the system
well within the strong coupling regime ($\hbar\Omega=0.36$ eV at
$\theta=65^{\circ}$). For our normal incidence measurements (main article Fig.
4), the reaction proceeded much more rapidly, so a target value of $A=1.2$ eV
i.e. $\alpha(E_{\text{MC}})=5.8$ µm-1 ($\hbar\Omega=0.59$ eV at
$\theta=0^{\circ}$) was chosen to reduce experimental error. Note, however,
that while the choice of target $\alpha(E_{\text{MC}})$ has an impact on the
absolute value of $\kappa$, this choice has little effect on the trends in
$\kappa$ as film thickness varies (see below).

Each sample was exposed to UV radiation for 1,100 s, with one ellipsometry
measurement (consisting of one $\Psi$ spectrum and one $\Delta$ spectrum)
taken every 11 s. This allowed us to observe the conversion of SPI into MC and
the transition from the weak to the strong coupling regime. An example is
plotted in main article Fig. 1d-e. Each vertical strip of main Fig. 1d is a
$\Psi$ spectrum acquired at a given time. First, we determine the cavity
thickness by fitting to the ellipsometry spectrum at $t=11$ s. When
determining the cavity thickness we assume that for this initial measurement
the MC concentration is negligible, i.e. $A_{\text{MC}}=0$. We then fix the
thickness and set $A_{\text{MC}}$ as the fit parameter for all measurements.

### S2.2 Comparison of photoisomerisation in strongly coupled and fully
detuned cavities

Figure S6: (a-c) Spectra of the ellipsometric parameter $\Psi$ of three Al
cavity (thicknesses (a) 78.3 nm, (b) 116 nm and (c) 154 nm) as a function of
UV irradiation time acquired at an incident angle of $\theta=65^{\circ}$. The
dashed green lines indicate the energy of the MC’s 2.2 eV resonance. (d) $A$
(left axis) and $\alpha(E_{\text{MC}})$ (right axis) as a function of UV
exposure time for the three cavities in panels (a-c): cavity thickness 78.3 nm
supporting a cavity mode at 3.50 eV (magenta points), thickness 116 nm
supporting a cavity mode at 2.73 eV (black points) and thickness 154 nm
supporting a cavity mode at 2.2 eV (green points).

In Fig. S6a-c we plot the way the $\Psi$ spectra for three Al cavities change
as a function of UV exposure time. All data are acquired at an incident angle
of $\theta=65^{\circ}$. In panel (a) we plot data acquired for a cavity of
thickness 78.3 nm, for which the first-order cavity mode occurs at an energy
of 3.5 eV. In panel (b) the thickness is 116 nm and the first-order cavity
mode occurs at 2.7 eV. In panel (c) the thickness is 154 nm and the first-
order cavity mode occurs at 2.2 eV, matching the MC molecular resonance, the
energy of which has been marked in all plots by the dashed green line. As
expected, in panel (c) we see the initial uncoupled cavity mode split into two
polariton branches. However, we also see polariton branches form in panels (a)
and (b): both plots show a new mode appearing, but since neither of these
features are centred on 2.2 eV they must be polariton branches emerging from
the strong coupling of MC with the detuned cavity modes. The observation of
strong coupling for such high detuning (as much as 1.3 eV) challenges the idea
that one can create a cavity that is fully detuned with no strong coupling; we
further explore this idea in Supplementary Section S6.

In Fig. S6d we plot how $A$ and $\alpha(E_{\text{MC}})$ changes with time for
these three cavities. The error bars for $A$ are calculated from the $90\%$
confidence intervals for $A$, which are calculated during fitting to
ellipsometry data (performed using the Levenberg-Marquardt method: a standard,
iterative, non-linear regression algorithm). We note that
$\alpha(E_{\text{MC}})$ varies with time in the same manner as in Hutchison et
al.’s work [1]. The curves are nonlinear and cannot be modelled by a simple
linear or exponential relationship. The reaction rate therefore changes as a
function of time. However, we note from Fig. S6c that for $L=154$ nm the
strong coupling regime is reached within the first 200 s. Yet within this time
the gradient of $\alpha(E_{\text{MC}})$ remains approximately constant. This
contrasts with Hutchison et al.’s report [1] that entering the strong coupling
regime causes a change in reaction rate, which implies we would expect to see
a change in gradient before 200 s.

Nevertheless, Fig. S6 could easily lead one to the same conclusion as
Hutchison et al. [1]: the reaction rate increases as the cavity is detuned
from the strong coupling condition. Does the correlation between the
suppression of photoisomerisation when the cavity thickness matches the
condition for strong coupling have a causal explanation, or is
photoisomerisation enhanced when the cavity mode moves into the ultraviolet
spectrum? Based on the data presented in Fig. S6 (or, indeed, in Hutchison et
al.’s work), it is not possible to tell. We therefore expanded our work to
study photoisomerisation trends for a wide range of film thicknesses.

### S2.3 Comparison of photoisomerisation rates before and after entering the
strong coupling regime

Figure S7: Reaction rate plots for Al cavities probed at $\theta=65^{\circ}$
calculated with different target $\alpha(E_{\text{MC}})$. In (a) we plot the
reciprocal of the time taken to reach $\alpha(E_{\text{MC}})=1.7$ µm-1, in (b)
we use a target of $\alpha(E_{\text{MC}})=3.5$ µm-1 and in (c) we use a target
of $\alpha(E_{\text{MC}})=4.4$ µm-1.

To obtain a better picture of what influences the photosisomerisation rates in
cavity structures, for the remainder of this work we studied many cavities
with a wide range of thicknesses, in all cases sweeping through at least two
conditions for strong coupling. (Main Figs. 2-4, Supp Figs. S8 onward.) Unless
otherwise specified we calculated $\kappa$, as defined above, using a target
of $A=0.70$ eV i.e. $\alpha(E_{\text{MC}})=3.5$ µm-1. For on-resonance
structures this corresponds to a splitting of $\hbar\Omega=0.36$ eV at
$\theta=65^{\circ}$. Here we show that the trends of $\kappa$ with film
thickness are approximately independent of our choice of target
$\alpha(E_{\text{MC}})$. In Fig. S7 we plot $\kappa$ for the same Al cavity
dataset ($\theta=65^{\circ}$) using three different targets for $\kappa$. In
panel (a) we choose $A=0.30$ eV i.e. $\alpha(E_{\text{MC}})=1.7$ µm-1 which is
insufficient for strong coupling; in panel (b) we use $A=0.70$ eV i.e.
$\alpha(E_{\text{MC}})=3.5$ µm-1, which gives $\hbar\Omega=0.36$ eV (the
values that we use for the majority of this study); in panel (c) we use
$A=0.90$ eV i.e. $\alpha(E_{\text{MC}})=4.4$ µm-1, which gives
$\hbar\Omega=0.42$ eV. All three plots reveal effectively the same trend.

Hutchison et al. [1] say that, in samples capable of supporting strong
coupling, the reaction rate decreases upon entering the strong coupling regime
and decreases further as $\hbar\Omega$ increases. (However, the authors do not
state when their on-resonance cavity enters the strong coupling regime, it is
impossible to verify this from the results they present.) In contrast, our
results suggest that trends in reaction rate are not changed when entering the
strong coupling regime.

## S3. Photochemistry in Al cavities at a range of incident angles

Figure S8: Photoisomerisation conversion constant measured in Al cavities for
a range of SPI/MC film thicknesses with incident angles $\theta=45^{\circ}$,
$55^{\circ}$, $65^{\circ}$ and $75^{\circ}$. At $\theta=75^{\circ}$ most films
never reach the target of $\alpha(E_{\text{MC}})=3.5$ µm-1; hence, we reduce
the target to $\alpha(E_{\text{MC}})=2.2$ µm-1 when calculating $\kappa$ at
all angles in this figure. As $\theta$ is increased the magnitude of the
conversion constant decreases (since at higher $\theta$ more light is
reflected from the top mirror and never enters the cavity) and the peaks and
troughs in the curves shift to higher film thicknesses (consistent with the
shifting of the absorption peaks at 3.5 eV). Otherwise, the relationship
between conversion constant and film thickness is unchanged.

## S4. Photochemistry in non-Al structures

Figure S9: Sample design (a,e,i,m,q), SPI (b,f,j,n,r) and MC (c,g,k,o,s)
dispersion plots constructed from $\Psi$ spectra, and SPI conversion rate
plots (d,h,l,p,t) for Ag cavity (a-d), Ag mirror (e-h), Cr mirror (i-l), Si
substrate (m-p) and amorphous glass substrate (q-t). All data acquired at
$\theta=65^{\circ}$. The target absorption coefficient used to calculate
$\kappa$ for the Ag cavity (panel d) was reduced from the usual
$\alpha(E_{\text{MC}})=3.5$ µm-1 to $\alpha(E_{\text{MC}})=2.6$ µm-1 because
the thickest Ag cavity samples never attained $\alpha(E_{\text{MC}})=3.5$
µm-1. The trend in the plotted data is otherwise the same as when using a
target of $\alpha(E_{\text{MC}})=3.5$ µm-1. The anticrossing feature at 4 eV
in the SPI and MC Ag mirror plots (f,g) is caused by impedance matching
between the Ag and SPI/MC film and is unrelated to strong coupling [3].

## S5. Accounting for photochemical reaction rates with calculated SPI
absorption

### S5.1 Calculation of SPI film absorption

We have established that photoisomerisation rate is strongly dependent on
SPI/MC film thickness in a wide variety of structures. To try and understand
the relationship between $\kappa$ and film thickness, we calculated the
absorption of SPI films in these various structures as a function of film
thickness. These calculations, based solely on the optical constants of SPI
and other materials (Ag, Al, etc), are independent of the experimental
ellipsometry data used to calculate photosiomerisation rates.

The absorption profile of the SPI layer can be calculated by finding the power
loss density within the layer, which can be obtained by finding the divergence
of the average Poynting vector within the film and integrate over the
thickness of the SPI film. The power loss density is given by

$\nabla\cdot\vec{\bar{S}}=-2\kappa k_{0}|\vec{\bar{S}}|$ (2)

where $\vec{\bar{S}}$ is the average Poynting vector, $\kappa$ is the
imaginary part of permittivity, and $k_{0}$ is the magnitude of wavevector in
freespace. By normalising the power loss density to the energy of the incident
beam, the absorption per unit distance within the layer is [4, 5]:

$P(z)=\frac{2\kappa
k_{0}\text{Re}(n_{\text{SPI}}\cos\theta_{m})}{\text{Re}(n_{1}\cos\theta_{1})}F(z)$
(3)

where $n_{\text{SPI}}$ is the refractive index of SPI, $\theta_{m}$ is the
refractive angle within the SPI layer, $F(z)=E(z)^{2}/E_{0}^{2}$, $E(z)$ is
the electric field within the SPI layer, and $E_{0}$ is the electric field of
the incident beam. The absorption profile can then be obtained by integrating
$P(z)$ over the thickness of the SPI layer. The average SPI absorption is then
calculated by dividing the absorption profile by the thickness of the SPI
layer.

### S5.2 Correlation between SPI film absorption and photochemical reaction
rates

Figure S10: Comparing experimentally determined SPI conversion rates in Al
cavities with calculated SPI absorption normalised to SPI film thickness for
(a) light of energy 2.2 eV at incident angles $0^{\circ}$ (green line) and
$65^{\circ}$ (orange line), (b) energy 3.5 eV at $\theta=0^{\circ}$ (cyan
line) and $\theta=65^{\circ}$ (purple line), and (c) energy 4.6 eV at
$\theta=0^{\circ}$ (magenta line) and $\theta=65^{\circ}$ (blue line). In
panels (d)-(i) we plot scatter plots to determine the correlation between each
of the calculated SPI absorption profiles in panels (a)-(c) and the
experimental reaction rate data.

In Figs. S10a-b we reproduce main article figs. 3a-b, which compare the
experimentally measured reaction rate data with SPI absorption profiles
calculated for different energies and incident angles (as outlined above). In
addition to Figs. S10a-b, which show SPI absorption profiles calculated for
$E=2.2$ eV, 3.5 eV at $\theta=0^{\circ}$, $\theta=65^{\circ}$, in panel (c) we
plot the calculated absorption profiles for $E=4.6$ eV (which corresponds to
another absorption peak in SPI, see main article Fig. 1b) at
$\theta=0^{\circ}$ and $\theta=65^{\circ}$. The 3.5 eV and 4.6 eV profiles all
do a good job of reproducing the trend in reaction rate over hundreds of
nanometres, with the 3.5 eV, $\theta=65^{\circ}$ profile giving the best
prediction of undulations that happen when the film thickness is varied by a
few tens of nanometres.

It is hard to see any clear relationship between either of the 2.2 eV
absorption profiles and the experimental data. In panels d-i we present
scatter plots with experimental conversion rates plotted against the various
calculated SPI absorption profiles. In panels (d,g) we see no clear
correlation between conversion rate and the 2.2 eV absorption, suggesting
there is no measurable effect of strong coupling on reaction rate. All the UV
absorption profiles in panels (e,f,h,i) show a clearer correlation, but it is
the $E=3.5$ eV, $\theta=65^{\circ}$ plot that shows the clearest relationship.
Note that while the $E=3.5$ eV, $\theta=0^{\circ}$ and $E=3.5$ eV,
$\theta=65^{\circ}$ absorption data give approximately the same $R^{2}$, it is
clear from inspecting panels (e) and (h) that the $E=3.5$ eV,
$\theta=65^{\circ}$ data is a much better predictor of conversion rate than
$E=3.5$ eV, $\theta=0^{\circ}$.

### S5.3 Limitations in our analysis

Our SPI absorption profiles were calculated using a single discrete energy
(e.g. 3.5 eV), when in reality the absorption peaks of interest have a finite
width. All our measurements were made using focussing optics, so that our
samples were not only illuminated with light at one discrete angle $\theta$
but over a narrow range of angles centred around $\theta$. Given this, it is
remarkable that the match between our relatively simplistic $E=3.5$ eV,
$\theta=65^{\circ}$ SPI absorption calculations and photosiomerisation rate
curves is as good as it is, especially at lower thicknesses.

We also note that the $R^{2}$ values calculated and plotted in main Figure 3c
exist as a summary of Supplementary Figure S11, which is too large to include
in the main manuscript. Analysis based solely on $R^{2}$ values should be
treated with caution, since one can compute the same $R^{2}$ from hugely
different data distributions (see e.g. Figs. S10e,h,i or Anscombe’s quartet).
The main limiting factor on the upper limit of $R^{2}$ is the level of noise
in the experimental data, which varies from sample to sample. One can
reasonably compare the magnitude of $R^{2}$ values calculated from the same
experimental dataset (e.g. to compare the match between 3.5 eV and 2.2 eV at
$65^{\circ}$ for the Al cavity dataset). However, it does not make much sense
to compare the $R^{2}$ values for e.g. Al cavity and Ag cavity (since one
would expect the Ag cavity dataset to give lower $R^{2}$ values anyway because
it is slightly noisier).

Finally we note that, strictly speaking, our analysis does not definitively
prove that strong coupling has no influence on photoisomerisation rate. We can
only say that the trends we do see appear to be well explained by modulation
of UV absorption and that any influence of strong coupling is in comparison
negligible.

### S5.4 Conversion rates and SPI absorption at different energies for all
structures

Figure continues overleaf.

Figure S11: Comparing photoisomerisation rates in a variety of structures with
their calculated average absorption at 2.2 eV (left-hand column) and 3.5 eV
(right-hand column) at $\theta=0^{\circ}$ and $\theta=65^{\circ}$. Where there
is a clearly resolvable trend in experimental data, the best match (acquired
at $\theta=65^{\circ}$) is given by the absorption at 3.5 eV,
$\theta=65^{\circ}$. In no case does the condition for strong coupling (at
either $\theta=0^{\circ}$ or $\theta=65^{\circ}$) give a good match to the
photoisomerisation rate.

The peaks in the $E=2.2$ eV, $\theta=65^{\circ}$ curve for the Ag cavity in
panel (e) are slightly split. The exact positions of the cavity modes for
$\theta\neq 0^{\circ}$ is polarisation-dependent. Because our calculations are
for unpolarised light, this means that two closely-overlapping modes appear.
This effect is only visible for the Ag cavity data in panel (e) because Ag
cavities are the only structures studied whose cavity modes are sufficiently
high-quality to clearly reveal this effect.

The noisier nature of our glass substrate dataset (panels m-n) can be
explained by the close impedance matching of the SPI/MC film and substrate.
This makes the field confinement much weaker, reducing the influence of UV
absorption and allowing other sample characteristics (such as roughness) to
have a greater influence on conversion rate.

## S6. Calculated normal incidence transmittance through thin Al/MC/Al
cavities

Figure S12: Calculated normal incidence transmittance through quartz (1 mm) /
Al(10 nm) / MC($L$) / Al(10 nm) / air structure. (a) Dispersion plot for a
range of MC film thicknesses 10 nm $\leq L\leq$ 180 nm showing clear
anticrossing between MC’s resonance at 2.2 eV and the first-order cavity mode,
whose energy matches the MC resonance energy at $L=140$ nm. The lower
polariton band is clearly distinguishable for MC film thicknesses well below
100 nm and only starts to disappear once the film thickness approaches $L=50$
nm. In panel (b) we plot the transmittance spectra corresponding to $L=140$ nm
(black line), $L=90$ nm (blue line) and $L=50$ nm (orange line), reiterating
that the lower polariton band is clearly visible at $L=90$ nm and still
faintly visible at $L=50$ nm. These results are consistent with the
$\theta=65^{\circ}$ Al cavity data plots in Supplementary Figure S6, where
splitting is also observable even when the first-order cavity mode is highly
detuned from 2.2 eV.

In the context of Hutchison et al.’s work [1], these results are significant.
To determine the effect of strong coupling on the SPI/MC photoconversion
process, Hutchison et al. compared the photoisomerisation rate of SPI in an
“on-resonance” cavity (thickness $L=130$ nm) to the photoisomerisation rate of
SPI in a thinner “off-resonance” cavity. The thickness of this “off-resonance”
control cavity is unspecified; however, our calculations suggest that, in
order to be a meaningful control, the “off-resonance” cavity thickness would
need to be substantially thinner than 90 nm. Given that Hutchison et al.’s
cavities contained two 20 nm PVA spacer layers separating their mirrors from
the SPI/MC film, our calculations suggest that the SPI/MC film would have had
to be extraordinarily thin (below 50 nm - something we could not achieve in
our experiments with the SPI molecule concentration necessary for ultrastrong
coupling).

## S7. Quantum yield as a function of cavity thickness

Figure S13: Black points: Reaction rate divided by SPI’s absorption of UV
radiation ($E=3.5$ eV) for (a,b) ellipsometry experiment at
$\theta=65^{\circ}$ (Main Figs. 2-3) and (c) normal incidence transmission
experiment (Main Fig. 4). Orange curves: Calculated SPI absorption at 2.2 eV
for (a) $\theta=0^{\circ}$ (strong coupling at normal incidence), (b)
$\theta=65^{\circ}$ (strong coupling at $\theta=65^{\circ}$) and (c)
$\theta=0^{\circ}$ (strong coupling at normal incidence).

Since the rate of a photochemical reaction is the rate of photons absorbed by
the reactant times the quantum yield of the reaction (i.e. the number of
molecules that complete the reaction per absorbed photon), our results suggest
that strong light-matter coupling with MC does not affect the quantum yield of
the SPI-MC photoisomerisation process. Here we plot our reaction rate data
normalised to SPI absorption at 3.5 eV, which is proportional to the quantum
yield of the SPI-MC process. We also plot the SPI absorption curves for 2.2
eV, whose maxima indicate the cavity thicknesses required for strong coupling.
We observe no correlation between reaction quantum yield and the conditions
for strong coupling.

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