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diff --git a/paper/paper-2020/paper-2020.md b/paper/paper-2020/paper-2020.md
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@@ -0,0 +1,120 @@
+---
+title: 'Chemiscope: interactive structure-property explorer for materials and molecules'
+tags:
+ - TypeScript
+ - JavaScript
+ - chemistry
+ - material science
+ - machine learning
+authors:
+ - name: Guillaume Fraux
+ orcid: 0000-0003-4824-6512
+ affiliation: 1
+ - name: Rose K. Cersonsky
+ orcid: 0000-0003-4515-3441
+ affiliation: 1
+ - name: Michele Ceriotti
+ orcid: 0000-0003-2571-2832
+ affiliation: 1
+affiliations:
+ - name: Laboratory of Computational Science and Modeling, IMX, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
+ index: 1
+date: 30 January 2020
+bibliography: paper.bib
+---
+
+# Summary
+
+The number of materials or molecules that can be created by combining different
+chemical elements in various proportions and spatial arrangements is enormous.
+Computational chemistry can be used to generate databases containing billions of
+potential structures [@Ruddigkeit2012], and predict some of the associated
+properties [@Montavon2013; @Ramakrishnan2014]. Unfortunately, the very large
+number of structures makes exploring such database — to understand
+structure-property relations or find the _best_ structure for a given
+application — a daunting task. In recent years, multiple molecular
+_representations_ [@Behler2007; @Bartok2013; @Willatt2019] have been developed
+to compute structural similarities between materials or molecules, incorporating
+physically-relevant information and symmetries. The features associated with
+these representations can be used for unsupervised machine learning
+applications, such as clustering or classification of the different structures,
+and high-throughput screening of database for specific properties [@Maier2007;
+@De2017; @Hautier2019]. Unfortunately, the dimensionality of these features (as
+well as most of other descriptors used in chemical and materials informatics) is
+very high, which makes the resulting classifications, clustering or mapping very
+hard to visualize. Dimensionality reduction algorithms [@Schlkopf1998;
+@Ceriotti2011; @McInnes2018] can reduce the number of relevant dimensions to a
+handful, creating 2D or 3D maps of the full database.
+
+![The Qm7b database [@Montavon2013] visualized with chemiscope](screenshot.png)
+
+Chemiscope is a graphical tool for the interactive exploration of materials and
+molecular databases, correlating local and global structural descriptors with
+the physical properties of the different systems. The interface consists of
+two panels. The left panel displays a 2D or 3D scatter plot, in which each
+point corresponds to a chemical entity. The axes, color, and style of each point
+can be set to represent a property or a structural descriptor to visualize
+structure-property relations directly. Structural descriptors are not computed
+directly by chemiscope, but must be obtained from one of the many codes
+implementing general-purpose atomic representation [@librascal; @QUIP] or more specialized descriptors. Since the most common
+descriptors can be very high dimensional, it can be convenient to apply a
+dimensionality reduction algorithm that maps them to a lower-dimensional space
+for easier visualization. For example the sketch-map algorithm [@Ceriotti2011]
+was used with the Smooth Overlap of Atomic Positions representation [@Bartok2013] to
+generate the visualization in Figure 1. The right panel displays the
+three-dimensional structure of the chemical entities, possibly including
+periodic repetition for crystals. Visualizing the chemical structure can help
+in finding an intuitive rationalization of the layout of the dataset and the
+structure-property relations.
+
+Whereas similar tools [@Gong2013; @Gutlein2014; @Probst2017; @ISV] only allow
+visualizing maps and structures in which each data point corresponds to a
+molecule, or a crystal structure, a distinctive feature of chemiscope is the
+possibility of visualizing maps in which points correspond to atom-centred
+environments. This is useful, for instance, to rationalize the relationship
+between structure and atomic properties such as nuclear chemical shieldings
+(Figure 2). This is also useful as a diagnostic tool for the many
+machine-learning schemes that decompose properties into atom-centred
+contributions [@Behler2007; @Bartok2010].
+
+![Database of chemical shieldings [@Paruzzo2018] in chemiscope demonstrating the use of a 3D plot and highlighting of atomic environments](./screenshot-3d.png)
+
+Chemiscope took strong inspiration from a previous similar graphical software,
+the interactive sketch-map visualizer [@ISV]. This previous software was used in
+multiple research publication, related to the exploration of large-scale
+databases, and the mapping of structure-property relationships [@De2016;
+@De2017; @Musil2018].
+
+# Implementation
+
+Chemiscope is implemented using the web platform: HTML5, CSS and WebGL to
+display graphical elements, and TypeScript (compiled to JavaScript) for
+interactivity. It uses [Plotly.js](https://plot.ly/javascript/) to render and
+animate 2D and 3D plots; and the JavaScript version of [Jmol](http://jmol.org/)
+to display atomic structures. The visualization is fast enough to be used with
+datasets containing up to a million points, reacting to user input within a few
+hundred milliseconds in the default 2D mode. More elaborate visualizations are
+slower, while still handling 100k points easily.
+
+The use of web technologies makes chemiscope usable from different operating
+systems without the need to develop, maintain and package the code for each
+operating system. It also means that we can provide an online service at
+http://chemiscope.org that allows users to visualize their own dataset without any
+local installation. Chemiscope is implemented as a library of re-usable
+components linked together via callbacks. This makes it easy to modify the
+default interface to generate more elaborate visualizations, for example,
+displaying multiple maps generated with different parameters of a dimensionality
+reduction algorithm. Chemiscope can also be distributed in a standalone mode,
+where the code and a predefined dataset are merged together as a single HTML
+file. This standalone mode is useful for archival purposes, for example as
+supplementary information for a published article and for use in corporate
+environments with sensitive datasets.
+
+# Acknowledgements
+
+The development of chemiscope have been funded by the [NCCR
+MARVEL](http://nccr-marvel.ch/), the [MAX](http://max-centre.eu/) European
+centre of excellence, and the European Research Council (Horizon 2020 grant
+agreement no. 677013-HBMAP).
+
+# References
diff --git a/paper/paper-2020/paper.bib b/paper/paper-2020/paper.bib
new file mode 100644
index 000000000..c2635cec3
--- /dev/null
+++ b/paper/paper-2020/paper.bib
@@ -0,0 +1,273 @@
+@article{Ceriotti2011,
+ doi = {10.1073/pnas.1108486108},
+ url = {https://doi.org/10.1073/pnas.1108486108},
+ year = {2011},
+ month = {07},
+ publisher = {Proceedings of the National Academy of Sciences},
+ volume = {108},
+ number = {32},
+ pages = {13023--13028},
+ author = {Michele Ceriotti and Gareth A. Tribello and Michele Parrinello},
+ title = {Simplifying the representation of complex free-energy landscapes using sketch-map},
+ journal = {Proceedings of the National Academy of Sciences}
+}
+
+@article{Bartok2013,
+ doi = {10.1103/physrevb.87.184115},
+ url = {https://doi.org/10.1103/physrevb.87.184115},
+ year = {2013},
+ month = {05},
+ publisher = {American Physical Society ({APS})},
+ volume = {87},
+ number = {18},
+ author = {Albert P. Bart{\'{o}}k and Risi Kondor and G{\'{a}}bor Cs{\'{a}}nyi},
+ title = {On representing chemical environments},
+ journal = {Physical Review B}
+}
+
+@article{Montavon2013,
+ doi = {10.1088/1367-2630/15/9/095003},
+ url = {https://doi.org/10.1088/1367-2630/15/9/095003},
+ year = {2013},
+ month = {09},
+ publisher = {{IOP} Publishing},
+ volume = {15},
+ number = {9},
+ pages = {095003},
+ author = {Grégoire Montavon and Matthias Rupp and Vivekanand Gobre and Alvaro Vazquez-Mayagoitia and Katja Hansen and Alexandre Tkatchenko and Klaus-Robert M\"{u}ller and O Anatole von Lilienfeld},
+ title = {Machine learning of molecular electronic properties in chemical compound space},
+ journal = {New Journal of Physics}
+}
+
+@article{Gutlein2014,
+ doi = {10.1186/s13321-014-0041-7},
+ url = {https://doi.org/10.1186/s13321-014-0041-7},
+ year = {2014},
+ month = sep,
+ publisher = {Springer Science and Business Media {LLC}},
+ volume = {6},
+ number = {1},
+ author = {Martin G\"{u}tlein and Andreas Karwath and Stefan Kramer},
+ title = {{CheS}-Mapper 2.0 for visual validation of (Q){SAR} models},
+ journal = {Journal of Cheminformatics}
+}
+
+@article{Probst2017,
+ doi = {10.1093/bioinformatics/btx760},
+ url = {https://doi.org/10.1093/bioinformatics/btx760},
+ year = {2017},
+ month = {10},
+ publisher = {Oxford University Press ({OUP})},
+ volume = {34},
+ number = {8},
+ pages = {1433--1435},
+ author = {Daniel Probst and Jean-Louis Reymond},
+ editor = {Jonathan Wren},
+ title = {{FUn}: a framework for interactive visualizations of large, high-dimensional datasets on the web},
+ journal = {Bioinformatics}
+}
+
+@article{Gong2013,
+ doi = {10.1093/bioinformatics/btt270},
+ url = {https://doi.org/10.1093/bioinformatics/btt270},
+ year = {2013},
+ month = {05},
+ publisher = {Oxford University Press ({OUP})},
+ volume = {29},
+ number = {14},
+ pages = {1827--1829},
+ author = {Jiayu Gong and Chaoqian Cai and Xiaofeng Liu and Xin Ku and Hualiang Jiang and Daqi Gao and Honglin Li},
+ title = {{ChemMapper}: a versatile web server for exploring pharmacology and chemical structure association based on molecular 3D similarity method},
+ journal = {Bioinformatics}
+}
+
+@article{Paruzzo2018,
+ doi = {10.1038/s41467-018-06972-x},
+ url = {https://doi.org/10.1038/s41467-018-06972-x},
+ year = {2018},
+ month = oct,
+ publisher = {Springer Science and Business Media {LLC}},
+ volume = {9},
+ number = {1},
+ author = {Federico M. Paruzzo and Albert Hofstetter and Félix Musil and Sandip De and Michele Ceriotti and Lyndon Emsley},
+ title = {Chemical shifts in molecular solids by machine learning},
+ journal = {Nature Communications}
+}
+
+@software{ISV,
+ author = {De, Sandip and Ceriotti, Michele},
+ title = {Interactive Sketchmap Visualizer},
+ publisher = {Zenodo},
+ year = {2019},
+ version = {1.0.0},
+ doi = {10.5281/zenodo.3541831},
+ url = {https://doi.org/10.5281/zenodo.3541831}
+}
+
+@article{De2016,
+ doi = {10.1039/c6cp00415f},
+ url = {https://doi.org/10.1039/c6cp00415f},
+ year = {2016},
+ publisher = {Royal Society of Chemistry ({RSC})},
+ volume = {18},
+ number = {20},
+ pages = {13754--13769},
+ author = {Sandip De and Albert P. Bart{\'{o}}k and G{\'{a}}bor Cs{\'{a}}nyi and Michele Ceriotti},
+ title = {Comparing molecules and solids across structural and alchemical space},
+ journal = {Physical Chemistry Chemical Physics}
+}
+
+@article{De2017,
+ doi = {10.1186/s13321-017-0192-4},
+ url = {https://doi.org/10.1186/s13321-017-0192-4},
+ year = {2017},
+ month = {02},
+ publisher = {Springer Science and Business Media {LLC}},
+ volume = {9},
+ number = {1},
+ author = {Sandip De and Félix Musil and Teresa Ingram and Carsten Baldauf and Michele Ceriotti},
+ title = {Mapping and classifying molecules from a high-throughput structural database},
+ journal = {Journal of Cheminformatics}
+}
+
+@article{Musil2018,
+ doi = {10.1039/c7sc04665k},
+ url = {https://doi.org/10.1039/c7sc04665k},
+ year = {2018},
+ publisher = {Royal Society of Chemistry ({RSC})},
+ volume = {9},
+ number = {5},
+ pages = {1289--1300},
+ author = {Félix Musil and Sandip De and Jack Yang and Joshua E. Campbell and Graeme M. Day and Michele Ceriotti},
+ title = {Machine learning for the structure-energy-property landscapes of molecular crystals},
+ journal = {Chemical Science}
+}
+
+@article{Hautier2019,
+ doi = {10.1016/j.commatsci.2019.02.040},
+ url = {https://doi.org/10.1016/j.commatsci.2019.02.040},
+ year = {2019},
+ month = {06},
+ publisher = {Elsevier {BV}},
+ volume = {163},
+ pages = {108--116},
+ author = {Geoffroy Hautier},
+ title = {Finding the needle in the haystack: Materials discovery and design through computational ab initio high-throughput screening},
+ journal = {Computational Materials Science}
+}
+
+@article{Willatt2019,
+ doi = {10.1063/1.5090481},
+ url = {https://doi.org/10.1063/1.5090481},
+ year = {2019},
+ month = {04},
+ publisher = {{AIP} Publishing},
+ volume = {150},
+ number = {15},
+ pages = {154110},
+ author = {Michael J. Willatt and F{\'{e}}lix Musil and Michele Ceriotti},
+ title = {Atom-density representations for machine learning},
+ journal = {The Journal of Chemical Physics}
+}
+
+@article{Behler2007,
+ doi = {10.1103/physrevlett.98.146401},
+ url = {https://doi.org/10.1103/physrevlett.98.146401},
+ year = {2007},
+ month = {04},
+ publisher = {American Physical Society ({APS})},
+ volume = {98},
+ number = {14},
+ author = {J\"{o}rg Behler and Michele Parrinello},
+ title = {Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces},
+ journal = {Physical Review Letters}
+}
+
+@article{Ruddigkeit2012,
+ doi = {10.1021/ci300415d},
+ url = {https://doi.org/10.1021/ci300415d},
+ year = {2012},
+ month = {11},
+ publisher = {American Chemical Society ({ACS})},
+ volume = {52},
+ number = {11},
+ pages = {2864--2875},
+ author = {Lars Ruddigkeit and Ruud van Deursen and Lorenz C. Blum and Jean-Louis Reymond},
+ title = {Enumeration of 166 Billion Organic Small Molecules in the Chemical Universe Database {GDB}-17},
+ journal = {Journal of Chemical Information and Modeling}
+}
+
+@article{Ramakrishnan2014,
+ doi = {10.1038/sdata.2014.22},
+ url = {https://doi.org/10.1038/sdata.2014.22},
+ year = {2014},
+ month = {08},
+ publisher = {Springer Science and Business Media {LLC}},
+ volume = {1},
+ number = {1},
+ author = {Raghunathan Ramakrishnan and Pavlo O. Dral and Matthias Rupp and O. Anatole von Lilienfeld},
+ title = {Quantum chemistry structures and properties of 134 kilo molecules},
+ journal = {Scientific Data}
+}
+
+@article{Bartok2010,
+ doi = {10.1103/physrevlett.104.136403},
+ url = {https://doi.org/10.1103/physrevlett.104.136403},
+ year = {2010},
+ month = {04},
+ publisher = {American Physical Society ({APS})},
+ volume = {104},
+ number = {13},
+ author = {Albert P. Bart{\'{o}}k and Mike C. Payne and Risi Kondor and G{\'{a}}bor Cs{\'{a}}nyi},
+ title = {Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons},
+ journal = {Physical Review Letters}
+}
+
+@article{Schlkopf1998,
+ doi = {10.1162/089976698300017467},
+ url = {https://doi.org/10.1162/089976698300017467},
+ year = {1998},
+ month = {08},
+ publisher = {{MIT} Press - Journals},
+ volume = {10},
+ number = {5},
+ pages = {1299--1319},
+ author = {Bernhard Sch\"{o}lkopf and Alexander Smola and Klaus-Robert M\"{u}ller},
+ title = {Nonlinear Component Analysis as a Kernel Eigenvalue Problem},
+ journal = {Neural Computation}
+}
+
+@article{Maier2007,
+ doi = {10.1002/anie.200603675},
+ url = {https://doi.org/10.1002/anie.200603675},
+ year = {2007},
+ month = aug,
+ publisher = {Wiley},
+ volume = {46},
+ number = {32},
+ pages = {6016--6067},
+ author = {Wilhelm{\hspace{0.25em}}F. Maier and Klaus St\"{o}we and Simone Sieg},
+ title = {Combinatorial and High-Throughput Materials Science},
+ journal = {Angewandte Chemie International Edition}
+}
+
+@article{McInnes2018,
+ title={UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction},
+ author={Leland McInnes and John Healy and James Melville},
+ year={2018},
+ eprint={1802.03426},
+}
+
+@online{librascal,
+ author = {librascal},
+ title = {},
+ date = {},
+ url = {https://github.com/lab-cosmo/librascal}
+}
+
+@online{QUIP,
+ author = {QUIP},
+ title = {},
+ date = {},
+ url = {http://libatoms.github.io/QUIP/}
+}
diff --git a/paper/screenshot-3d.png b/paper/paper-2020/screenshot-3d.png
similarity index 100%
rename from paper/screenshot-3d.png
rename to paper/paper-2020/screenshot-3d.png
diff --git a/paper/screenshot.png b/paper/paper-2020/screenshot.png
similarity index 100%
rename from paper/screenshot.png
rename to paper/paper-2020/screenshot.png
diff --git a/paper/paper.bib b/paper/paper.bib
index c2635cec3..bc4fb07b9 100644
--- a/paper/paper.bib
+++ b/paper/paper.bib
@@ -1,107 +1,383 @@
-@article{Ceriotti2011,
- doi = {10.1073/pnas.1108486108},
- url = {https://doi.org/10.1073/pnas.1108486108},
- year = {2011},
- month = {07},
- publisher = {Proceedings of the National Academy of Sciences},
- volume = {108},
- number = {32},
- pages = {13023--13028},
- author = {Michele Ceriotti and Gareth A. Tribello and Michele Parrinello},
- title = {Simplifying the representation of complex free-energy landscapes using sketch-map},
- journal = {Proceedings of the National Academy of Sciences}
-}
-
-@article{Bartok2013,
- doi = {10.1103/physrevb.87.184115},
- url = {https://doi.org/10.1103/physrevb.87.184115},
- year = {2013},
- month = {05},
- publisher = {American Physical Society ({APS})},
- volume = {87},
- number = {18},
- author = {Albert P. Bart{\'{o}}k and Risi Kondor and G{\'{a}}bor Cs{\'{a}}nyi},
- title = {On representing chemical environments},
- journal = {Physical Review B}
-}
-
-@article{Montavon2013,
- doi = {10.1088/1367-2630/15/9/095003},
- url = {https://doi.org/10.1088/1367-2630/15/9/095003},
- year = {2013},
- month = {09},
- publisher = {{IOP} Publishing},
- volume = {15},
- number = {9},
- pages = {095003},
- author = {Grégoire Montavon and Matthias Rupp and Vivekanand Gobre and Alvaro Vazquez-Mayagoitia and Katja Hansen and Alexandre Tkatchenko and Klaus-Robert M\"{u}ller and O Anatole von Lilienfeld},
- title = {Machine learning of molecular electronic properties in chemical compound space},
- journal = {New Journal of Physics}
-}
-
-@article{Gutlein2014,
- doi = {10.1186/s13321-014-0041-7},
- url = {https://doi.org/10.1186/s13321-014-0041-7},
- year = {2014},
- month = sep,
- publisher = {Springer Science and Business Media {LLC}},
- volume = {6},
+@article{Fraux2020,
+ doi = {10.21105/joss.02117},
+ url = {https://doi.org/10.21105/joss.02117},
+ year = {2020},
+ publisher = {The Open Journal},
+ volume = {5},
+ number = {51},
+ pages = {2117},
+ author = {Fraux, Guillaume and Cersonsky, Rose K. and Ceriotti, Michele},
+ title = {{Chemiscope}: interactive structure-property explorer for materials and
+ molecules},
+ journal = {Journal of Open Source Software},
+}
+
+@article{ase-paper,
+ author = {Ask Hjorth Larsen and Jens Jørgen Mortensen and Jakob Blomqvist and Ivano E
+ Castelli and Rune Christensen and Marcin Dułak and Jesper Friis and Michael N Groves
+ and Bjørk Hammer and Cory Hargus and Eric D Hermes and Paul C Jennings and Peter
+ Bjerre Jensen and James Kermode and John R Kitchin and Esben Leonhard Kolsbjerg and
+ Joseph Kubal and Kristen Kaasbjerg and Steen Lysgaard and Jón Bergmann Maronsson and
+ Tristan Maxson and Thomas Olsen and Lars Pastewka and Andrew Peterson and Carsten
+ Rostgaard and Jakob Schiøtz and Ole Schütt and Mikkel Strange and Kristian S Thygesen
+ and Tejs Vegge and Lasse Vilhelmsen and Michael Walter and Zhenhua Zeng and Karsten W
+ Jacobsen},
+ title = {The atomic simulation environment—a {Python} library for working with atoms},
+ journal = {Journal of Physics: Condensed Matter},
+ volume = {29},
+ number = {27},
+ pages = {273002},
+ doi = {10.1088/1361-648X/aa680e},
+ url = {http://stacks.iop.org/0953-8984/29/i=27/a=273002},
+ year = {2017},
+}
+
+@article{MDAnalysis,
+ author = {Gowers, Richard J. and Linke, Max and Barnoud, Jonathan and Reddy, Tyler J.
+ E. and Melo, Manuel N. and Seyler, Sean L. and Domański, Jan and Dotson, David L. and
+ Buchoux, Sébastien and Kenney, Ian M. and Beckstein, Oliver},
+ title = {{MDAnalysis}: A {Python} Package for the Rapid Analysis of Molecular Dynamics
+ Simulations},
+ journal = {SciPy 2016},
+ year = {2016},
+ doi = {10.25080/Majora-629e541a-00e},
+ url = {https://doi.org/10.25080/Majora-629e541a-00e},
+}
+
+@article{STK,
+ title = {stk: An extendable {Python} framework for automated molecular and
+ supramolecular structure assembly and discovery},
+ author = {Turcani, Lukas and Tarzia, Andrew and Szczypi{\'n}ski, Filip T. and Jelfs,
+ Kim E.},
+ journal = {The Journal of Chemical Physics},
+ year = {2021},
+ volume = {154},
+ number = {21},
+ pages = {214102},
+ doi = {10.1063/5.0049708},
+}
+
+@software{chemfiles,
+ author = {Guillaume Fraux and Len Kimms and Jonathan Fine and German P. Barletta and
+ Mykola Dimura and FX Coudert and pelsa and Maximilien Levesque and Shoubhik Maiti and
+ Simon Guionniere and jmintser},
+ title = {{chemfiles}/{chemfiles}: Version 0.10.4},
+ month = {may},
+ year = {2023},
+ publisher = {Zenodo},
+ version = {0.10.4},
+ doi = {10.5281/zenodo.7904565},
+ url = {https://doi.org/10.5281/zenodo.7904565},
+}
+
+@article{Mazitov2025,
+ title = {{PET-MAD} as a lightweight universal interatomic potential for advanced
+ materials modeling},
+ author = {Mazitov, Arslan and Bigi, Filippo and Kellner, Matthias and Pegolo, Paolo
+ and Tisi, Davide and Fraux, Guillaume and Pozdnyakov, Sergey and Loche, Philip and
+ Ceriotti, Michele},
+ journal = {Nature Communications},
+ volume = {16},
+ number = {1},
+ pages = {10653},
+ year = {2025},
+ doi = {10.1038/s41467-025-65662-7},
+ url = {https://doi.org/10.1038/s41467-025-65662-7},
+}
+
+@article{MAD,
+ author = {Mazitov, Arslan and Chorna, Sofiia and Fraux, Guillaume and Bercx, Marnik
+ and Pizzi, Giovanni and De, Sandip and Ceriotti, Michele},
+ title = {Massive Atomic Diversity: a compact universal dataset for atomistic machine
+ learning},
+ journal = {Scientific Data},
+ volume = {12},
number = {1},
- author = {Martin G\"{u}tlein and Andreas Karwath and Stefan Kramer},
- title = {{CheS}-Mapper 2.0 for visual validation of (Q){SAR} models},
- journal = {Journal of Cheminformatics}
+ pages = {1857},
+ year = {2025},
+ doi = {10.1038/s41597-025-06109-y},
+ url = {https://doi.org/10.1038/s41597-025-06109-y},
}
-@article{Probst2017,
- doi = {10.1093/bioinformatics/btx760},
- url = {https://doi.org/10.1093/bioinformatics/btx760},
+@article{metatensor,
+ title = {{metatensor} and {metatomic}: Foundational libraries for interoperable
+ atomistic machine learning},
+ author = {Filippo Bigi and Joseph W. Abbott and Philip Loche and Arslan Mazitov and
+ Davide Tisi and Marcel F. Langer and Alexander Goscinski and Paolo Pegolo and Sanggyu
+ Chong and Rohit Goswami and Pol Febrer and Sofiia Chorna and Matthias Kellner and
+ Michele Ceriotti and Guillaume Fraux},
+ journal = {Journal of Chemical Physics},
+ volume = {164},
+ number = {6},
+ pages = {064113},
+ year = {2026},
+ month = {Feb},
+ doi = {10.1063/5.0304911},
+ url = {https://doi.org/10.1063/5.0304911}
+}
+
+@article{Jupyter,
+ author = {Granger, Brian E. and Pérez, Fernando},
+ journal = {Computing in Science & Engineering},
+ title = {{Jupyter}: Thinking and Storytelling With Code and Data},
+ year = {2021},
+ volume = {23},
+ number = {2},
+ pages = {7-14},
+ doi = {10.1109/MCSE.2021.3059263},
+}
+
+@incollection{JupyterNotebook,
+ author = {{Kluyver}, Thomas and {Ragan-Kelley}, Benjain and {P{\'e}rez}, Fernando and
+ {Granger}, Brian and {Bussonnier}, Matthias and {Frederic}, Jonathan and {Kelley},
+ Kyle and {Hamrick}, Jessica and {Grout}, Jason and {Corlay}, Sylvain and {Ivanov},
+ Paul and {Avila}, Dami{\'a}n and {Abdalla}, Safia and {Willing}, Carol and {Jupyter
+ Development Team}},
+ title = {{Jupyter} Notebooks--a publishing format for reproducible computational
+ workflows},
+ booktitle = {Positioning and Power in Academic Publishing: Players, Agents and
+ Agendas},
+ year = {2016},
+ pages = {87--90},
+ publisher = {IOS Press},
+ doi = {10.3233/978-1-61499-649-1-87},
+}
+
+@article{Du2024,
+ title = {{Jupyter} widgets and extensions for education and research in computational
+ physics and chemistry},
+ journal = {Computer Physics Communications},
+ volume = {305},
+ pages = {109353},
+ year = {2024},
+ issn = {0010-4655},
+ doi = {10.1016/j.cpc.2024.109353},
+ url = {https://www.sciencedirect.com/science/article/pii/S0010465524002765},
+ author = {Dou Du and Taylor J. Baird and Kristjan Eimre and Sara Bonella and Giovanni
+ Pizzi},
+}
+
+@article{Humphrey1996,
+ author = {William Humphrey and Andrew Dalke and Klaus Schulten},
+ title = {{VMD} -- {V}isual {M}olecular {D}ynamics},
+ journal = {Journal of Molecular Graphics},
+ year = {1996},
+ volume = {14},
+ pages = {33--38},
+ doi = {10.1016/0263-7855(96)00018-5},
+}
+
+@article{Stukowski2010,
+ author = {Stukowski, Alexander},
+ title = {Visualization and analysis of atomistic simulation data with {OVITO}--the
+ {Open Visualization Tool}},
+ journal = {Modelling and Simulation in Materials Science and Engineering},
+ year = {2010},
+ volume = {18},
+ number = {1},
+ pages = {015012},
+ doi = {10.1088/0965-0393/18/1/015012},
+}
+
+@article{Rego2015,
+ author = {Rego, Nicholas and Koes, David},
+ title = {{3Dmol.js}: molecular visualization with {WebGL}},
+ journal = {Bioinformatics},
+ year = {2015},
+ volume = {31},
+ number = {8},
+ pages = {1322--1324},
+ issn = {1367-4803},
+ doi = {10.1093/bioinformatics/btu829},
+ url = {https://doi.org/10.1093/bioinformatics/btu829},
+ note = {Available at: \url{http://3Dmol.csb.pitt.edu}},
+ publisher = {Oxford University Press},
+}
+
+@article{Nguyen2017,
+ author = {Nguyen, Hai and Case, David A. and Rose, Alexander S.},
+ title = {NGLview-interactive molecular graphics for {Jupyter} notebooks},
+ journal = {Bioinformatics},
year = {2017},
- month = {10},
- publisher = {Oxford University Press ({OUP})},
volume = {34},
- number = {8},
- pages = {1433--1435},
- author = {Daniel Probst and Jean-Louis Reymond},
- editor = {Jonathan Wren},
- title = {{FUn}: a framework for interactive visualizations of large, high-dimensional datasets on the web},
- journal = {Bioinformatics}
-}
-
-@article{Gong2013,
- doi = {10.1093/bioinformatics/btt270},
- url = {https://doi.org/10.1093/bioinformatics/btt270},
- year = {2013},
- month = {05},
- publisher = {Oxford University Press ({OUP})},
- volume = {29},
- number = {14},
- pages = {1827--1829},
- author = {Jiayu Gong and Chaoqian Cai and Xiaofeng Liu and Xin Ku and Hualiang Jiang and Daqi Gao and Honglin Li},
- title = {{ChemMapper}: a versatile web server for exploring pharmacology and chemical structure association based on molecular 3D similarity method},
- journal = {Bioinformatics}
+ number = {7},
+ pages = {1241--1242},
+ issn = {1367-4803},
+ doi = {10.1093/bioinformatics/btx789},
+ url = {https://doi.org/10.1093/bioinformatics/btx789},
+ note = {Source code available at: \url{https://github.com/arose/nglview}},
+ publisher = {Oxford University Press},
}
-@article{Paruzzo2018,
- doi = {10.1038/s41467-018-06972-x},
- url = {https://doi.org/10.1038/s41467-018-06972-x},
- year = {2018},
- month = oct,
- publisher = {Springer Science and Business Media {LLC}},
+@article{IPython,
+ author = {Perez, Fernando and Granger, Brian E.},
+ journal = {Computing in Science & Engineering},
+ title = {{IPython}: A System for Interactive Scientific Computing},
+ year = {2007},
volume = {9},
- number = {1},
- author = {Federico M. Paruzzo and Albert Hofstetter and Félix Musil and Sandip De and Michele Ceriotti and Lyndon Emsley},
- title = {Chemical shifts in molecular solids by machine learning},
- journal = {Nature Communications}
+ number = {3},
+ pages = {21-29},
+ doi = {10.1109/MCSE.2007.53},
}
-@software{ISV,
- author = {De, Sandip and Ceriotti, Michele},
- title = {Interactive Sketchmap Visualizer},
+@software{sphinx,
+ author = {Óscar Nájera and Eric Larson and Lucy Liu and Loïc Estève and Gael Varoquaux
+ and Jaques Grobler and Elliott Sales de Andrade and Chris Holdgraf and Alexandre
+ Gramfort and Mainak Jas and Joel Nothman and Steffen Rehberg and Olivier Grisel and
+ Nelle Varoquaux and Steven Hiscocks and alexis and Emmanuelle Gouillart and Tim
+ Hoffmann and Antony Lee and Gavin Uberti and Martin Luessi and Albert Y. Shih and Jake
+ Vanderplas and Jody Klymak and Alex Rockhill and John Muradeli and Thomas A Caswell
+ and Bane Sullivan and Alyssa Batula and Patrick Kunzmann},
+ title = {sphinx-gallery/sphinx-gallery: v0.12.2},
+ month = {mar},
+ year = {2023},
publisher = {Zenodo},
- year = {2019},
- version = {1.0.0},
- doi = {10.5281/zenodo.3541831},
- url = {https://doi.org/10.5281/zenodo.3541831}
+ version = {v0.12.2},
+ doi = {10.5281/zenodo.7716999},
+ url = {https://doi.org/10.5281/zenodo.7716999},
+}
+
+@article{Talirz_2020,
+ title = {{Materials Cloud}, a platform for open computational science},
+ volume = {7},
+ issn = {2052-4463},
+ url = {http://dx.doi.org/10.1038/s41597-020-00637-5},
+ doi = {10.1038/s41597-020-00637-5},
+ number = {1},
+ journal = {Scientific Data},
+ publisher = {Springer Science and Business Media LLC},
+ author = {Talirz, Leopold and Kumbhar, Snehal and Passaro, Elsa and Yakutovich,
+ Aliaksandr V. and Granata, Valeria and Gargiulo, Fernando and Borelli, Marco and
+ Uhrin, Martin and Huber, Sebastiaan P. and Zoupanos, Spyros and Adorf, Carl S. and
+ Andersen, Casper Welzel and Schütt, Ole and Pignedoli, Carlo A. and Passerone, Daniele
+ and VandeVondele, Joost and Schulthess, Thomas C. and Smit, Berend and Pizzi, Giovanni
+ and Marzari, Nicola},
+ year = {2020},
+ month = {sep},
+}
+
+@article{Goscinski2025scicodewidgets,
+ title = {scicode-widgets: Bringing Computational Experiments to the Classroom with
+ {Jupyter} Widgets},
+ author = {Goscinski, Alexander and Baird, Taylor J. and Du, Dou and Prado, Jo{\~a}o
+ and Suman, Divya and Sodjargal, Tulga-Erdene and Bonella, Sara and Pizzi, Giovanni and
+ Ceriotti, Michele},
+ year = {2025},
+ eprint = {2507.05734},
+ archivePrefix = {arXiv},
+ primaryClass = {physics.ed-ph},
+ doi = {10.48550/arXiv.2507.05734},
+ url = {https://arxiv.org/abs/2507.05734},
+}
+
+@article{orlov2025,
+ author = {Orlov, Alexey A. and Sosnin, Sergey and Fedorov, Maxim V.},
+ title = {From High Dimensions to Human Insight: Exploring Dimensionality Reduction for
+ Chemical Space Visualization},
+ journal = {Molecular Informatics},
+ year = {2025},
+ volume = {44},
+ number = {1},
+ pages = {e202400265},
+ doi = {10.1002/minf.202400265},
+ issn = {1868-1743},
+ url = {https://doi.org/10.1002/minf.202400265},
+ publisher = {Wiley},
+}
+
+@article{Walsh2025mapping,
+ author = {Park, Hyunsoo and Onwuli, Anthony and Butler, Keith T. and Walsh, Aron},
+ title = {Mapping inorganic crystal chemical space},
+ journal = {Faraday Discussions},
+ year = {2025},
+ volume = {256},
+ pages = {601--613},
+ doi = {10.1039/D4FD00063C},
+ url = {https://doi.org/10.1039/D4FD00063C},
+}
+
+@article{Cheng2020,
+ author = {Cheng, Bingqing and Griffiths, Ryan-Rhys and Wengert, Simon and Kunkel,
+ Christian and Stenczel, Tamas and Zhu, Bonan and Deringer, Volker L. and Bernstein,
+ Noam and Margraf, Johannes T. and Reuter, Karsten and Csanyi, Gabor},
+ title = {Mapping Materials and Molecules},
+ journal = {Accounts of Chemical Research},
+ volume = {53},
+ number = {9},
+ pages = {1981--1991},
+ year = {2020},
+ doi = {10.1021/acs.accounts.0c00403},
+ url = {https://doi.org/10.1021/acs.accounts.0c00403},
+}
+
+@article{Tamura2022,
+ author = {Tamura, Ryo and Matsuda, Momo and Lin, Jianbo and Futamura, Yasunori and
+ Sakurai, Tetsuya and Miyazaki, Tsuyoshi},
+ title = {Structural analysis based on unsupervised learning: Search for a
+ characteristic low-dimensional space by local structures in atomistic simulations},
+ journal = {Physical Review B},
+ volume = {105},
+ number = {7},
+ pages = {075107},
+ year = {2022},
+ doi = {10.1103/PhysRevB.105.075107},
+ url = {https://doi.org/10.1103/PhysRevB.105.075107},
+}
+
+@article{Chapman2022,
+ author = {Chapman, James and Goldman, Nir and Wood, Brandon C.},
+ title = {Efficient and universal characterization of atomic structures through a
+ topological graph order parameter},
+ journal = {npj Computational Materials},
+ volume = {8},
+ number = {1},
+ pages = {37},
+ year = {2022},
+ doi = {10.1038/s41524-022-00717-7},
+ url = {https://doi.org/10.1038/s41524-022-00717-7},
+}
+
+@article{Huang2020,
+ author = {Huang, Yue and Zhang, Jingtian and Jiang, Edwin S. and Oya, Yutaka and
+ Saeki, Akinori and Kikugawa, Gota and Okabe, Tomonaga and Ohuchi, Fumio S.},
+ title = {Structure--Property Correlation Study for Organic Photovoltaic Polymer
+ Materials Using Data Science Approach},
+ journal = {The Journal of Physical Chemistry C},
+ volume = {124},
+ number = {24},
+ pages = {12871--12882},
+ year = {2020},
+ doi = {10.1021/acs.jpcc.0c00517},
+ url = {https://doi.org/10.1021/acs.jpcc.0c00517},
+}
+
+@article{xie2018,
+ author = {Tian Xie and Jeffrey C. Grossman},
+ title = {Hierarchical visualization of materials space with graph convolutional neural
+ networks},
+ journal = {The Journal of Chemical Physics},
+ year = {2018},
+ volume = {149},
+ number = {17},
+ pages = {174111},
+ month = {nov},
+ doi = {10.1063/1.5047803},
+ issn = {0021-9606},
+ url = {https://doi.org/10.1063/1.5047803},
+ publisher = {AIP Publishing},
+}
+
+@article{Nicholas2020,
+ author = {Nicholas, Thomas C. and Goodwin, Andrew L. and Deringer, Volker L.},
+ title = {Understanding the geometric diversity of inorganic and hybrid frameworks
+ through structural coarse-graining},
+ journal = {Chemical Science},
+ volume = {11},
+ number = {46},
+ pages = {12580--12587},
+ year = {2020},
+ doi = {10.1039/D0SC03287E},
+ url = {https://doi.org/10.1039/D0SC03287E},
}
@article{De2016,
@@ -112,162 +388,192 @@ @article{De2016
volume = {18},
number = {20},
pages = {13754--13769},
- author = {Sandip De and Albert P. Bart{\'{o}}k and G{\'{a}}bor Cs{\'{a}}nyi and Michele Ceriotti},
+ author = {Sandip De and Albert P. Bart{\'{o}}k and G{\'{a}}bor Cs{\'{a}}nyi and
+ Michele Ceriotti},
title = {Comparing molecules and solids across structural and alchemical space},
- journal = {Physical Chemistry Chemical Physics}
+ journal = {Physical Chemistry Chemical Physics},
}
-@article{De2017,
- doi = {10.1186/s13321-017-0192-4},
- url = {https://doi.org/10.1186/s13321-017-0192-4},
- year = {2017},
- month = {02},
- publisher = {Springer Science and Business Media {LLC}},
- volume = {9},
+@article{HernandezLeon2024,
+ doi = {10.1088/1402-4896/ad432e},
+ url = {https://doi.org/10.1088/1402-4896/ad432e},
+ year = {2024},
+ month = {may},
+ publisher = {IOP Publishing},
+ volume = {99},
+ number = {6},
+ pages = {066004},
+ author = {Hernández-León, Patricia and Caro, Miguel A},
+ title = {Cluster-based multidimensional scaling embedding tool for data
+ visualization},
+ journal = {Physica Scripta},
+}
+
+@article{Wurger2021,
+ author = {W{\"u}rger, Tim and Mei, Di and Vaghefinazari, Bahram and Winkler, David A.
+ and Lamaka, Sviatlana V. and Zheludkevich, Mikhail L. and Mei{\ss}ner, Robert H. and
+ Feiler, Christian},
+ title = {Exploring structure-property relationships in magnesium dissolution
+ modulators},
+ journal = {npj Materials Degradation},
+ volume = {5},
number = {1},
- author = {Sandip De and Félix Musil and Teresa Ingram and Carsten Baldauf and Michele Ceriotti},
- title = {Mapping and classifying molecules from a high-throughput structural database},
- journal = {Journal of Cheminformatics}
+ pages = {2},
+ year = {2021},
+ doi = {10.1038/s41529-020-00148-z},
+ url = {https://doi.org/10.1038/s41529-020-00148-z},
}
-@article{Musil2018,
- doi = {10.1039/c7sc04665k},
- url = {https://doi.org/10.1039/c7sc04665k},
- year = {2018},
- publisher = {Royal Society of Chemistry ({RSC})},
+@article{Helfrecht2020,
+ doi = {10.1088/2632-2153/aba9ef},
+ url = {https://doi.org/10.1088/2632-2153/aba9ef},
+ year = {2020},
+ publisher = {IOP},
+ volume = {1},
+ pages = {045021},
+ author = {Helfrecht, Benjamin A. and Cersonsky, Rose K. and Fraux, Guillaume and Ceriotti, Michele},
+ title = {Structure-property maps with Kernel principal covariates regression},
+ journal = {Machine Learning: Science and Technology},
+}
+
+@article{Jorgensen2026,
+ title = {Interpretable Visualizations of Data Spaces for Classification Problems},
+ author = {Jorgensen, Christian and Lin, Arthur Y. and Vasavada, Rhushil and Cersonsky,
+ Rose K.},
+ journal = {Machine Learning: Science and Technology},
+ volume = {7},
+ number = {2},
+ pages = {025008},
+ year = {2026},
+ doi = {10.1088/2632-2153/ae466e},
+ url = {https://doi.org/10.1088/2632-2153/ae466e},
+}
+
+@article{MD22,
+ title = {Accurate global machine learning force fields for molecules with hundreds of
+ atoms},
+ author = {Stefan Chmiela and Valentin Vassilev-Galindo and Oliver T. Unke and Adil
+ Kabylda and Huziel E. Sauceda and Alexandre Tkatchenko and Klaus-Robert Müller},
+ year = {2023},
+ journal = {Science Advances},
volume = {9},
- number = {5},
- pages = {1289--1300},
- author = {Félix Musil and Sandip De and Jack Yang and Joshua E. Campbell and Graeme M. Day and Michele Ceriotti},
- title = {Machine learning for the structure-energy-property landscapes of molecular crystals},
- journal = {Chemical Science}
-}
-
-@article{Hautier2019,
- doi = {10.1016/j.commatsci.2019.02.040},
- url = {https://doi.org/10.1016/j.commatsci.2019.02.040},
- year = {2019},
- month = {06},
- publisher = {Elsevier {BV}},
+ number = {2},
+ pages = {eadf0873},
+ doi = {10.1126/sciadv.adf0873},
+ url = {https://www.science.org/doi/abs/10.1126/sciadv.adf0873},
+ eprint = {https://www.science.org/doi/pdf/10.1126/sciadv.adf0873},
+}
+
+@misc{plotlyjs,
+ author = {{Plotly Technologies Inc.}},
+ title = {Collaborative data science},
+ year = {2015},
+ url = {https://plot.ly},
+}
+
+@misc{MaterialsCloudChemiscopeSearch,
+ title = {Materials Cloud Archive},
+ url = {https://archive.materialscloud.org/search?q=&f=ext_apps%3Achemiscope&l=list&p=1&s=10&sort=newest},
+ note = {Accessed 2026-05-14},
+ year = {2026},
+}
+
+@misc{PyPIStatsChemiscope,
+ title = {{PyPI} Stats for chemiscope},
+ url = {https://pypistats.org/packages/chemiscope},
+ note = {Accessed 2026-05-14},
+ year = {2026},
+}
+
+@misc{AtomisticCookbook,
+ title = {The Atomistic Cookbook},
+ url = {https://atomistic-cookbook.org/software/chemiscope.html},
+ note = {Accessed 2026-05-14},
+ year = {2026},
+}
+
+@article{MACE,
+ author = {Ilyes Batatia and Philipp Benner and Yuan Chiang and Alin M. Elena and Dávid
+ P. Kovács and Janosh Riebesell and Xavier R. Advincula and Mark Asta and Matthew
+ Avaylon and William J. Baldwin and Fabian Berger and Noam Bernstein and Arghya Bhowmik
+ and Filippo Bigi and Samuel M. Blau and Vlad Cărare and Michele Ceriotti and Sanggyu
+ Chong and James P. Darby and Sandip De and Flaviano Della Pia and Volker L. Deringer
+ and Rokas Elijošius and Zakariya El-Machachi and Edvin Fako and Fabio Falcioni and
+ Andrea C. Ferrari and John L. A. Gardner and Mikołaj J. Gawkowski and Annalena
+ Genreith-Schriever and Janine George and Rhys E. A. Goodall and Jonas Grandel and
+ Clare P. Grey and Petr Grigorev and Shuang Han and Will Handley and Hendrik H. Heenen
+ and Kersti Hermansson and Cheuk Hin Ho and Stephan Hofmann and Christian Holm and Jad
+ Jaafar and Konstantin S. Jakob and Hyunwook Jung and Venkat Kapil and Aaron D. Kaplan
+ and Nima Karimitari and James R. Kermode and Panagiotis Kourtis and Namu Kroupa and
+ Jolla Kullgren and Matthew C. Kuner and Domantas Kuryla and Guoda Liepuoniute and Chen
+ Lin and Johannes T. Margraf and Ioan-Bogdan Magdău and Angelos Michaelides and J.
+ Harry Moore and Aakash A. Naik and Samuel P. Niblett and Sam Walton Norwood and Niamh
+ O’Neill and Christoph Ortner and Kristin A. Persson and Karsten Reuter and Andrew S.
+ Rosen and Louise A. M. Rosset and Lars L. Schaaf and Christoph Schran and Benjamin X.
+ Shi and Eric Sivonxay and Tamás K. Stenczel and Christopher Sutton and Viktor Svahn
+ and Thomas D. Swinburne and Jules Tilly and Cas van der Oord and Santiago Vargas and
+ Eszter Varga-Umbrich and Tejs Vegge and Martin Vondrák and Yangshuai Wang and William
+ C. Witt and Thomas Wolf and Fabian Zills and Gábor Csányi},
+ title = {A foundation model for atomistic materials chemistry},
+ journal = {The Journal of Chemical Physics},
volume = {163},
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- title = {Finding the needle in the haystack: Materials discovery and design through computational ab initio high-throughput screening},
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- author = {Wilhelm{\hspace{0.25em}}F. Maier and Klaus St\"{o}we and Simone Sieg},
- title = {Combinatorial and High-Throughput Materials Science},
- journal = {Angewandte Chemie International Edition}
+@article{He2025,
+ author = {He, Yuqing and De Breuck, Pierre-Paul and Weng, Hongming and Giantomassi,
+ Matteo and Rignanese, Gian-Marco},
+ title = {Machine learning on multiple topological materials datasets},
+ journal = {npj Computational Materials},
+ volume = {11},
+ number = {1},
+ pages = {181},
+ year = {2025},
+ doi = {10.1038/s41524-025-01687-2},
+ url = {https://doi.org/10.1038/s41524-025-01687-2},
}
-@article{McInnes2018,
- title={UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction},
- author={Leland McInnes and John Healy and James Melville},
- year={2018},
- eprint={1802.03426},
+@article{Keith2021,
+ author = {Keith, John A. and Vassilev-Galindo, Valentin and Cheng, Bingqing and
+ Chmiela, Stefan and Gastegger, Michael and M{\"u}ller, Klaus-Robert and Tkatchenko,
+ Alexandre},
+ title = {Combining Machine Learning and Computational Chemistry for Predictive
+ Insights Into Chemical Systems},
+ journal = {Chemical Reviews},
+ volume = {121},
+ number = {16},
+ pages = {9816--9872},
+ year = {2021},
+ doi = {10.1021/acs.chemrev.1c00107},
+ url = {https://doi.org/10.1021/acs.chemrev.1c00107},
}
-@online{librascal,
- author = {librascal},
- title = {},
- date = {},
- url = {https://github.com/lab-cosmo/librascal}
+@article{Gallarati2022,
+ author = {Simone Gallarati and Puck van Gerwen and Ruben Laplaza and Sergi Vela and
+ Alberto Fabrizio and Clémence Corminboeuf},
+ title = {{OSCAR}: An extensive repository of chemically and functionally diverse
+ organocatalysts},
+ journal = {Chemical Science},
+ volume = {13},
+ number = {46},
+ pages = {13782-13794},
+ year = {2022},
+ doi = {10.1039/D2SC04251G},
}
-@online{QUIP,
- author = {QUIP},
- title = {},
- date = {},
- url = {http://libatoms.github.io/QUIP/}
+@article{Blaskovits2024,
+ author = {Blaskovits, J. Terence and Laplaza, Ruben and Vela, Sergi and Corminboeuf,
+ Cl{\'e}mence},
+ title = {Data-Driven Discovery of Organic Electronic Materials Enabled by Hybrid
+ Top-Down/Bottom-Up Design},
+ journal = {Advanced Materials},
+ volume = {36},
+ number = {2},
+ pages = {2305602},
+ year = {2024},
+ doi = {10.1002/adma.202305602},
+ url = {https://doi.org/10.1002/adma.202305602},
}
diff --git a/paper/paper.md b/paper/paper.md
index 911a552c6..cc20a27bf 100644
--- a/paper/paper.md
+++ b/paper/paper.md
@@ -1,17 +1,28 @@
---
-title: 'Chemiscope: interactive structure-property explorer for materials and molecules'
+title: 'Chemiscope 1.0: interactive exploration of atomistic data from analysis to dissemination'
tags:
- TypeScript
- JavaScript
+ - Python
- chemistry
- - material science
+ - materials science
- machine learning
+ - visualization
authors:
- - name: Guillaume Fraux
- orcid: 0000-0003-4824-6512
+ - name: Sofiia Chorna
+ orcid: 0009-0008-7426-0856
+ affiliation: 1
+ - name: Jakub Lála
+ orcid: 0000-0002-5424-5260
+ affiliation: "1, 2"
+ - name: Qianjun Xu
+ orcid: 0000-0003-0778-7208
affiliation: 1
- name: Rose K. Cersonsky
orcid: 0000-0003-4515-3441
+ affiliation: 3
+ - name: Guillaume Fraux
+ orcid: 0000-0003-4824-6512
affiliation: 1
- name: Michele Ceriotti
orcid: 0000-0003-2571-2832
@@ -19,102 +30,176 @@ authors:
affiliations:
- name: Laboratory of Computational Science and Modeling, IMX, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
index: 1
-date: 30 January 2020
+ - name: Department of Materials, Imperial College London, London SW7 2AZ, United Kingdom
+ index: 2
+ - name: Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI 53705, United States
+ index: 3
+date: 31 January 2026
bibliography: paper.bib
---
# Summary
+Chemiscope is an interactive visualization tool for exploring structure-property
+relationships in molecular and materials datasets [@Fraux2020]. It links a map view,
+e.g. a low-dimensional embedding or property-property scatter plot, to an interactive 3D
+structure viewer, which streamlines inspection of clusters and outliers by moving
+between points in feature and property space and the corresponding atomic
+configurations.
+
+Chemiscope 1.0 turns the original browser-based visualizer into a versatile,
+multi-purpose tool that fits into Python-centric workflows. The same visualization can
+be rendered as a standalone web viewer, embedded as a Jupyter widget [@Jupyter;
+@IPython], included in Streamlit web applications, or integrated into Sphinx-built
+documentation and sphinx-gallery examples for reproducible software manuals [@sphinx].
+Chemiscope 1.0 also provides support for visualizing datasets directly from widely used
+atomistic Python toolkits, including ASE [@ase-paper], MDAnalysis [@MDAnalysis], stk
+[@STK], and Chemfiles [@chemfiles].
+
+{width=100%}
+
+# Statement of need
+
+Atomistic modeling workflows produce collections of molecular and materials structures
+together with associated quantities, including energies, forces, charges, and other
+scalar or tensorial properties. These datasets are commonly explored using
+post-processing analysis, including property-property correlations [@Huang2020;
+@Wurger2021] and low-dimensional projections [@Helfrecht2020; @Jorgensen2026;
+@orlov2025; @Tamura2022; @HernandezLeon2024], to relate abstract representations to the
+underlying atomic configurations [@Chapman2022; @Nicholas2020]. Interactive
+visualization provides a practical means to interpret structure-property relationships
+[@Wurger2021], verify computational results, identify unexpected patterns [@xie2018],
+and explore learned representations [@Walsh2025mapping; @Cheng2020; @De2016].
+
+# State of the field
+
+Chemiscope has been adopted across multiple atomistic modeling and coarse-grained
+studies, with interactive viewers shared alongside publications and archived datasets on
+platforms such as Materials Cloud [@Talirz_2020]. While complementary visualization
+tools exist, from desktop applications such as VMD and OVITO [@Humphrey1996;
+@Stukowski2010] to WebGL-based molecular viewers such as 3Dmol.js and NGLview
+[@Rego2015; @Nguyen2017], Chemiscope distinguishes itself by providing a single dataset
+representation and rendering stack that can be reused across multiple contexts. This is
+especially important in Python-based workflows, where the same visualization is often
+needed in a Jupyter notebook for analysis, a web view for sharing, and documentation for
+reproducibility and teaching [@JupyterNotebook; @Goscinski2025scicodewidgets; @Du2024].
+
+# Software design
+
+Chemiscope 1.0 is implemented as a TypeScript visualization library with the Python
+package providing platform-specific integrations. The Python API can be used to build a
+Chemiscope dataset from atomic structures, associated properties, and visualization
+settings, and export it in the JSON schema consumed by the JavaScript renderer. The
+interface is organized into linked map, structure, and information panels. The map panel
+uses Plotly.js to render 2D and 3D scatter plots [@plotlyjs], the structure panel uses
+3Dmol.js for molecular rendering.
+
+The map rendering is a primary performance bottleneck for large datasets. Chemiscope 1.0
+introduces adaptive Level of Detail (LOD) rendering for scatter views, which downsample
+large datasets based on screen-space density, i.e., how many points would overlap in the
+current view. As users zoom or change view parameters, the displayed subset is updated
+to preserve both responsiveness and visual structure. In practice, this handles maps
+with more than 500,000 points on commodity hardware, without requiring users to
+pre-filter or manually decimate their data. Structure data can also be off-loaded to
+external files, reducing memory footprint and initial loading time.
+
+Chemiscope 1.0 introduces the possibility of rendering atom-centered shapes to represent
+vectorial and tensorial properties, including arrows (e.g. dipoles or forces),
+ellipsoids (e.g. polarizabilities), and user-defined triangular meshes. For biomolecular
+systems, it supports cartoon representations based on residue and chain information. The
+structure viewer handles a grid layout for side-by-side comparison of multiple
+structures or local environments.
+
+In Jupyter notebooks, the viewer is exposed as a widget with bidirectional communication
+between Python and the JavaScript runtime, implemented via traitlets [@Jupyter;
+@IPython]. The widget supports programmatic control of the visualization, including
+selection synchronization, settings modification, and export of map snapshots. Users can
+create a visualization by preparing structures and associated properties and calling
+`chemiscope.show`:
+
+```python
+import ase.io
+import chemiscope
+
+structures = ase.io.read("trajectory.xyz", ":")
+
+# Extract properties present in the trajectory (e.g. energy, forces)
+properties = chemiscope.extract_properties(structures)
+
+# Set default settings for multi-frame trajectories
+settings = chemiscope.quick_settings(trajectory=True)
+
+# Display the viewer
+chemiscope.show(structures=structures, properties=properties, settings=settings)
+```
+
+For web applications built with Streamlit, the Chemiscope component renders a viewer
+from an in-memory dataset and propagates user interactions (e.g. selection and settings
+changes) back to Python, coupling to other Streamlit widgets. For reproducible
+documentation, Chemiscope includes a Sphinx extension that embeds interactive viewers
+alongside narrative text and executable examples [@sphinx].
+
+![50k random structures from the MD22 dataset [@MD22] visualized with Chemiscope by
+projecting them into the PET-MAD reduced latent space using `chemiscope.explore`. Panel
+a) shows the Chemiscope widget overall, panel b) a zoom-in of the map demonstrating
+adaptive level-of-detail rendering, panel c) the 3D view with selective coloring by
+cohesive energy, and panel d) the shape functionality displaying forces as
+arrows.](chemiscope-v1.0-overview.svg){width=100%}
+
+Finally, the package includes an `explore` function that generates interactive
+visualizations starting from structures alone. It integrates metatomic models
+[@metatensor], particularly the PET-MAD model [@Mazitov2025], which is used by default,
+to derive informative representations and produce map coordinates without requiring
+manual descriptor engineering or an explicit dimensionality reduction step [@MAD]:
+
+```python
+chemiscope.explore(structures, featurizer="pet-mad-1.0")
+```
+
+Chemiscope is distributed as an open-source package that can be installed from PyPI, and
+the default standalone viewer is available online at https://chemiscope.org for quick
+inspection of datasets without local installation. Optional features can be installed
+via extras: `pip install 'chemiscope[streamlit]'` and `pip install
+'chemiscope[explore]'`.
+
+# Research impact statement
+
+Chemiscope has been adopted by the atomistic modeling community as a tool for
+interactive exploration of structure-property relationships. Interactive visualizations
+built with Chemiscope accompany publications and archived datasets on Materials Cloud
+[@Talirz_2020] and provide citable links that readers can use to explore data beyond
+static images. As of May 2026, the Materials Cloud Archive lists 19 publications using
+Chemiscope as an external app [@MaterialsCloudChemiscopeSearch].
+
+The tool has been cited in studies spanning machine-learned interatomic potentials and
+datasets [@MACE; @He2025; @MAD; @Keith2021; @Cheng2020], coarse-grained molecular
+representations [@Helfrecht2020; @Nicholas2020], and high-throughput screening of
+materials [@Jorgensen2026; @Blaskovits2024; @Gallarati2022]. Chemiscope is integrated
+with the metatensor ecosystem [@metatensor] through the `chemiscope.explore` function,
+which uses foundation models like PET-MAD [@Mazitov2025] to generate map coordinates
+without manual descriptor engineering.
+
+Beyond research papers, Chemiscope serves educational and reproducibility purposes:
+tutorials and course materials use live widgets to demonstrate dimensionality reduction
+and structure-property correlations [@Goscinski2025scicodewidgets], and Chemiscope
+viewers can be embedded directly into manuals, as is done in the Atomistic Cookbook
+recipes [@AtomisticCookbook]. The Python package reached 5,726 downloads in the last
+month on PyPI Stats, accessed May 2026 [@PyPIStatsChemiscope].
+
+# AI usage disclosure
-The number of materials or molecules that can be created by combining different
-chemical elements in various proportions and spatial arrangements is enormous.
-Computational chemistry can be used to generate databases containing billions of
-potential structures [@Ruddigkeit2012], and predict some of the associated
-properties [@Montavon2013; @Ramakrishnan2014]. Unfortunately, the very large
-number of structures makes exploring such database — to understand
-structure-property relations or find the _best_ structure for a given
-application — a daunting task. In recent years, multiple molecular
-_representations_ [@Behler2007; @Bartok2013; @Willatt2019] have been developed
-to compute structural similarities between materials or molecules, incorporating
-physically-relevant information and symmetries. The features associated with
-these representations can be used for unsupervised machine learning
-applications, such as clustering or classification of the different structures,
-and high-throughput screening of database for specific properties [@Maier2007;
-@De2017; @Hautier2019]. Unfortunately, the dimensionality of these features (as
-well as most of other descriptors used in chemical and materials informatics) is
-very high, which makes the resulting classifications, clustering or mapping very
-hard to visualize. Dimensionality reduction algorithms [@Schlkopf1998;
-@Ceriotti2011; @McInnes2018] can reduce the number of relevant dimensions to a
-handful, creating 2D or 3D maps of the full database.
-
-![The Qm7b database [@Montavon2013] visualized with chemiscope](screenshot.png)
-
-Chemiscope is a graphical tool for the interactive exploration of materials and
-molecular databases, correlating local and global structural descriptors with
-the physical properties of the different systems. The interface consists of
-two panels. The left panel displays a 2D or 3D scatter plot, in which each
-point corresponds to a chemical entity. The axes, color, and style of each point
-can be set to represent a property or a structural descriptor to visualize
-structure-property relations directly. Structural descriptors are not computed
-directly by chemiscope, but must be obtained from one of the many codes
-implementing general-purpose atomic representation [@librascal; @QUIP] or more specialized descriptors. Since the most common
-descriptors can be very high dimensional, it can be convenient to apply a
-dimensionality reduction algorithm that maps them to a lower-dimensional space
-for easier visualization. For example the sketch-map algorithm [@Ceriotti2011]
-was used with the Smooth Overlap of Atomic Positions representation [@Bartok2013] to
-generate the visualization in Figure 1. The right panel displays the
-three-dimensional structure of the chemical entities, possibly including
-periodic repetition for crystals. Visualizing the chemical structure can help
-in finding an intuitive rationalization of the layout of the dataset and the
-structure-property relations.
-
-Whereas similar tools [@Gong2013; @Gutlein2014; @Probst2017; @ISV] only allow
-visualizing maps and structures in which each data point corresponds to a
-molecule, or a crystal structure, a distinctive feature of chemiscope is the
-possibility of visualizing maps in which points correspond to atom-centred
-environments. This is useful, for instance, to rationalize the relationship
-between structure and atomic properties such as nuclear chemical shieldings
-(Figure 2). This is also useful as a diagnostic tool for the many
-machine-learning schemes that decompose properties into atom-centred
-contributions [@Behler2007; @Bartok2010].
-
-![Database of chemical shieldings [@Paruzzo2018] in chemiscope demonstrating the use of a 3D plot and highlighting of atomic environments](./screenshot-3d.png)
-
-Chemiscope took strong inspiration from a previous similar graphical software,
-the interactive sketch-map visualizer [@ISV]. This previous software was used in
-multiple research publication, related to the exploration of large-scale
-databases, and the mapping of structure-property relationships [@De2016;
-@De2017; @Musil2018].
-
-# Implementation
-
-Chemiscope is implemented using the web platform: HTML5, CSS and WebGL to
-display graphical elements, and TypeScript (compiled to JavaScript) for
-interactivity. It uses [Plotly.js](https://plot.ly/javascript/) to render and
-animate 2D and 3D plots; and the JavaScript version of [Jmol](http://jmol.org/)
-to display atomic structures. The visualization is fast enough to be used with
-datasets containing up to a million points, reacting to user input within a few
-hundred milliseconds in the default 2D mode. More elaborate visualizations are
-slower, while still handling 100k points easily.
-
-The use of web technologies makes chemiscope usable from different operating
-systems without the need to develop, maintain and package the code for each
-operating system. It also means that we can provide an online service at
-http://chemiscope.org that allows users to visualize their own dataset without any
-local installation. Chemiscope is implemented as a library of re-usable
-components linked together via callbacks. This makes it easy to modify the
-default interface to generate more elaborate visualizations, for example,
-displaying multiple maps generated with different parameters of a dimensionality
-reduction algorithm. Chemiscope can also be distributed in a standalone mode,
-where the code and a predefined dataset are merged together as a single HTML
-file. This standalone mode is useful for archival purposes, for example as
-supplementary information for a published article and for use in corporate
-environments with sensitive datasets.
+Generative AI tools were used occasionally during software development (e.g. to obtain
+code suggestions). All AI-generated suggestions were reviewed, modified, and verified by
+the authors before inclusion, and the authors take full responsibility for the final
+content of the software.
# Acknowledgements
-The development of chemiscope have been funded by the [NCCR
-MARVEL](http://nccr-marvel.ch/), the [MAX](http://max-centre.eu/) European
-centre of excellence, and the European Research Council (Horizon 2020 grant
-agreement no. 677013-HBMAP).
+The development of Chemiscope 1.0 has been funded primarily by the [NCCR
+MARVEL](https://nccr-marvel.ch/).
# References