diff --git a/reports/paper.md b/reports/paper.md
index aaaeebe..8eb940e 100644
--- a/reports/paper.md
+++ b/reports/paper.md
@@ -59,20 +59,6 @@ literature.\label{fig:summary}](figures/time-split-abstract.png)
 <!--- Mention similar options in molecular discovery benchmarking, e.g. guacamol which I believe has something similar in terms of rediscovery, though maybe not time-based. Mention legacy materials informatics (CrabNet, CGCNN, etc.) and the shift towards inverse design via generative modeling (CDVAE, FTCP, PGCGM, CubicGAN, etc.). --->
 
 
-<!-- The latest advances in machine learning are often in natural language processing such as with long
-short-term memory networks (LSTMs) and Transformers, or image processing such as with
-generative adversarial networks (GANs), variational autoencoders (VAEs), and guided
-diffusion models. `xtal2png` encodes and decodes crystal structures via PNG
-images (see e.g. \autoref{fig:64-bit}) by writing and reading the necessary information
-for crystal reconstruction (unit cell, atomic elements, atomic coordinates) as a square
-matrix of numbers. This is akin to making/reading a QR code for crystal
-structures, where the `xtal2png` representation is an invertible representation. The
-ability to feed these images directly into image-based pipelines allows you, as a
-materials informatics practitioner, to get streamlined results for new state-of-the-art
-image-based machine learning models applied to crystal structures.
-
-![A real size $64\times64$ pixel `xtal2png` representation of a crystal structure.\label{fig:64-bit}](figures/Zn8B8Pb4O24,volume=623,uid=bc2d.png) -->
-
 # Statement of need
 
 Time-based splits have been used in the past for validating materials informatics
@@ -106,129 +92,6 @@ rigorous benchmarking of generative materials discovery models. `mp-time-split`
 as the basis for a set of benchmarking metrics hosted in the [`matbench-genmetrics`](https://github.com/sparks-baird/matbench-genmetrics) suite
 which has recently been applied to `xtal2png` [@baird_xtal2png_2022], a generative model for crystal structure.
 
-<!-- Using a state-of-the-art method in a separate domain with a custom data representation
-is often an expensive and drawn-out process. For example, [@vaswaniAttentionAllYou2017]
-introduced the revolutionary natural language processing Transformer architecture in
-June 2017, yet the application of Transformers to the adjacent domain of materials
-informatics (chemical-formula-based predictions) was not publicly realized until late
-2019 [@goodallPredictingMaterialsProperties2019], approximately two-and-a-half years
-later, with peer-reviewed publications dating to late 2020
-[@goodallPredictingMaterialsProperties2020]. Interestingly, a nearly identical
-implementation was being developed concurrently in a different research group with
-slightly later public release [@wangCompositionallyrestrictedAttentionbasedNetwork2020]
-and publication [@wangCompositionallyRestrictedAttentionbased2021] dates. Another
-example of a state-of-the-art algorithm domain transfer is refactoring image-processing
-models for crystal structure applications, which was first introduced in a preprint
-[@kipfSemisupervisedClassificationGraph2016] and published with application for
-materials' property prediction in a peer-reviewed journal over a year later
-[@xieCrystalGraphConvolutional2018]. Similarly, VAEs were introduced in 2013
-[@kingmaAutoEncodingVariationalBayes2014a] and implemented for molecules in 2016
-[@gomez-bombarelliAutomaticChemicalDesign2016], and denoising diffusion probabilistic
-models (DDPMs) were introduced in 2015 [@sohl-dicksteinDeepUnsupervisedLearning2015] and
-implemented for crystal structures in 2021 [@xieCrystalDiffusionVariational2021]. Here,
-we focus on state-of-the-art domain transfer (especially of generative models) from
-image processing to crystal structure to enable materials science practitioners to
-leverage the most advanced image processing models for materials' property prediction
-and inverse design. -->
-
-<!-- `xtal2png` is a Python package that allows you to convert between a crystal structure
-and a PNG image for direct use with image-based machine learning models. Let's take
-[Google's image-to-image diffusion model,
-Palette](https://iterative-refinement.github.io/palette/)
-[@sahariaPaletteImagetoImageDiffusion2022a], which supports unconditional image
-generation, conditional inpainting, and conditional image restoration, which are modeling tasks
-that can be used in crystal generation, structure prediction, and structure
-relaxation, respectively. Rather than dig into the code and spending hours, days, or
-weeks modifying, debugging, and playing GitHub phone tag with the developers before you
-can (maybe) get preliminary results, `xtal2png` lets you get comparable results using the default parameters, assuming the instructions can be run without
-error. While there are other invertible representations for crystal structures
-[@xieCrystalDiffusionVariational2022;@renInvertibleCrystallographicRepresentation2022a]
-as well as cross-domain conversions such as converting between molecules and strings
-[@weiningerSMILESChemicalLanguage1988;@selfies], to our knowledge, this is the first
-package that enables conversion between a crystal structure and an image file format.
-
-![(a) upscaled example image and (b) legend of the `xtal2png` encoding.\label{fig:example-and-legend}](figures/example-and-legend.png)
-
-`xtal2png` was designed to be easy to use by both
-"[Pythonistas](https://en.wiktionary.org/wiki/Pythonista)" and entry-level coders alike.
-`xtal2png` provides a straightforward Python application programming interface (API) and
-command-line interface (CLI). `xtal2png` relies on `pymatgen.core.structure.Structure`
-[@ongPythonMaterialsGenomics2013] objects for representing crystal structures and also
-supports reading crystallographic information files (CIFs) from directories. `xtal2png`
-encodes crystallographic information related to the unit cell, crystallographic
-symmetry, and atomic elements and coordinates which are each scaled individually
-according to the information type. An upscaled version of the PNG image and a legend of
-the representation are given in \autoref{fig:example-and-legend}. Due to the encoding of
-numerical values as PNG images (allowable values are integers between 0 and
-255), a round-off error is present during a single round of encoding and decoding.
-An example comparing an original vs. decoded structure is given in
-\autoref{fig:original-decoded}.
-
-There are some limitations and design considerations for `xtal2png` that are described
-in `xtal2png`'s [documentation](https://xtal2png.readthedocs.io/en/latest/index.html) in
-the Overview section.
-At this time, it is unclear to what extent deviation from the aforementioned design
-choices will affect performance. We intend to use hyperparameter optimization to
-determine an optimal configuration for crystal structure generation tasks using the
-`xtal2png` representation.
-
-![(a) Original and (b) `xtal2png` decoded visualizations of
-[`mp-560471`](https://materialsproject.org/materials/mp-560471/) / Zn$_2$B$_2$PbO$_6$. Images were generated using [ase visualizations](https://wiki.fysik.dtu.dk/ase/ase/visualize/visualize.html). \label{fig:original-decoded}](figures/original-decoded.png){ width=50% }
-
-The significance of the representation lies in being able to directly use the PNG
-representation with image-based models which often do not directly support custom
-dataset types. We expect the use of `xtal2png` as a screening tool for such models to
-save significant user time of code refactoring and adaptation during the process of
-obtaining preliminary results on a newly released model. After obtaining preliminary
-results, you get to decide whether it's worth it to you to take on the
-higher-cost/higher-expertise task of modifying the codebase and using a more customized
-approach. Or you can stick with the results of xtal2png. It's up to you!
-
-We plan to apply `xtal2png` to a probabilistic diffusion generative model as a
-proof of concept and present our findings in the near future. -->
-
-<!-- ![Caption for example figure.\label{fig:example}](figure.png) -->
-
-<!-- # Mathematics
-
-Single dollars ($) are required for inline mathematics e.g. $f(x) = e^{\pi/x}$
-
-Double dollars make self-standing equations:
-
-$$\Theta(x) = \left\{\begin{array}{l}
-0\textrm{ if } x < 0\cr
-1\textrm{ else}
-\end{array}\right.$$
-
-You can also use plain \LaTeX for equations
-\begin{equation}\label{eq:fourier}
-\hat f(\omega) = \int_{-\infty}^{\infty} f(x) e^{i\omega x} dx
-\end{equation}
-and refer to \autoref{eq:fourier} from text. -->
-
-<!--
-# Citations
-Citations to entries in paper.bib should be in
-[rMarkdown](http://rmarkdown.rstudio.com/authoring_bibliographies_and_citations.html)
-format.
-
-If you want to cite a software repository URL (e.g. something on GitHub without a preferred
-citation) then you can do it with the example BibTeX entry below for @fidgit.
-
-For a quick reference, the following citation commands can be used:
-- `@author:2001`  ->  "Author et al. (2001)"
-- `[@author:2001]` -> "(Author et al., 2001)"
-- `[@author1:2001; @author2:2001]` -> "(Author1 et al., 2001; Author2 et al., 2002)" -->
-
-<!-- # Figures
-
-Figures can be included like this:
-![Caption for example figure.\label{fig:example}](figure.png)
-and referenced from text using \autoref{fig:example}.
-
-Figure sizes can be customized by adding an optional second parameter:
-![Caption for example figure.](figure.png){ width=20% } -->
-
 # Acknowledgements
 
 S.G.B. and T.D.S. acknowledge support by the National Science Foundation, USA under