Materials informatics

Machine learning in condensed matter physics, chemistry and materials science

Placeholder linkdump on the theme of machine learning in condensed matter physics and materials science is a rapidly growing field. See also learnable coarse-graining and machine learning in physical sciences.

Master lists

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Baird, Sterling G., Tran Q. Diep, and Taylor D. Sparks. 2022. β€œDiSCoVeR: A Materials Discovery Screening Tool for High Performance, Unique Chemical Compositions.” Digital Discovery 1 (3): 226–40.
Beeler, Chris, Sriram Ganapathi Subramanian, Kyle Sprague, Nouha Chatti, Colin Bellinger, Mitchell Shahen, Nicholas Paquin, et al. 2023. β€œChemGymRL: An Interactive Framework for Reinforcement Learning for Digital Chemistry.” arXiv.
Behler, JΓΆrg. 2016. β€œPerspective: Machine Learning Potentials for Atomistic Simulations.” The Journal of Chemical Physics 145 (17): 170901.
Bellinger, Colin, Andriy Drozdyuk, Mark Crowley, and Isaac Tamblyn. 2022. β€œBalancing Information with Observation Costs in Deep Reinforcement Learning.” In Proceedings of the Canadian Conference on Artificial Intelligence. Canadian Artificial Intelligence Association (CAIAC).
Butler, Keith T., Daniel W. Davies, Hugh Cartwright, Olexandr Isayev, and Aron Walsh. 2018. β€œMachine Learning for Molecular and Materials Science.” Nature 559 (7715): 547–55.
Carleo, Giuseppe, Ignacio Cirac, Kyle Cranmer, Laurent Daudet, Maria Schuld, Naftali Tishby, Leslie Vogt-Maranto, and Lenka ZdeborovΓ‘. 2019. β€œMachine Learning and the Physical Sciences.” Reviews of Modern Physics 91 (4): 045002.
Chanussot, Lowik, Abhishek Das, Siddharth Goyal, Thibaut Lavril, Muhammed Shuaibi, Morgane Riviere, Kevin Tran, et al. 2021. β€œThe Open Catalyst 2020 (OC20) Dataset and Community Challenges.” ACS Catalysis 11 (10): 6059–72.
Deiana, Allison McCarn, Nhan Tran, Joshua Agar, Michaela Blott, Giuseppe Di Guglielmo, Javier Duarte, Philip Harris, et al. 2021. β€œApplications and Techniques for Fast Machine Learning in Science.” arXiv:2110.13041 [Physics], October.
Deringer, Volker L., Albert P. BartΓ³k, Noam Bernstein, David M. Wilkins, Michele Ceriotti, and GΓ‘bor CsΓ‘nyi. 2021. β€œGaussian Process Regression for Materials and Molecules.” Chemical Reviews 121 (16): 10073–141.
Durumeric, Aleksander E. P., Nicholas E. Charron, Clark Templeton, FΓ©lix Musil, Klara Bonneau, Aldo S. Pasos-Trejo, Yaoyi Chen, Atharva Kelkar, Frank NoΓ©, and Cecilia Clementi. 2023. β€œMachine Learned Coarse-Grained Protein Force-Fields: Are We There yet?” Current Opinion in Structural Biology 79 (April): 102533.
Faroughi, Salah A., Nikhil Pawar, Celio Fernandes, Maziar Raissi, Subasish Das, Nima K. Kalantari, and Seyed Kourosh Mahjour. 2023. β€œPhysics-Guided, Physics-Informed, and Physics-Encoded Neural Networks in Scientific Computing.” arXiv.
Haghighat, Ehsan, Maziar Raissi, Adrian Moure, Hector Gomez, and Ruben Juanes. 2021. β€œA Physics-Informed Deep Learning Framework for Inversion and Surrogate Modeling in Solid Mechanics.” Computer Methods in Applied Mechanics and Engineering 379 (June): 113741.
Himanen, Lauri, Amber Geurts, Adam Stuart Foster, and Patrick Rinke. 2019. β€œData-Driven Materials Science: Status, Challenges, and Perspectives.” Advanced Science 6 (21): 1900808.
Jin, Hanxun, Enrui Zhang, and Horacio D. Espinosa. 2023. β€œRecent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review.” arXiv.
Joshi, Soumil Y., and Sanket A. Deshmukh. 2021. β€œA Review of Advancements in Coarse-Grained Molecular Dynamics Simulations.” Molecular Simulation 47 (10-11): 786–803.
Kontolati, Katiana, Darius Alix-Williams, Nicholas M. Boffi, Michael L. Falk, Chris H. Rycroft, and Michael D. Shields. 2021. β€œManifold Learning for Coarse-Graining Atomistic Simulations: Application to Amorphous Solids.” Acta Materialia 215 (August): 117008.
Medasani, Bharat, Anthony Gamst, Hong Ding, Wei Chen, Kristin A. Persson, Mark Asta, Andrew Canning, and Maciej Haranczyk. 2016. β€œPredicting Defect Behavior in B2 Intermetallics by Merging Ab Initio Modeling and Machine Learning.” Npj Computational Materials 2 (1): 1.
Miret, Santiago, Kin Long Kelvin Lee, Carmelo Gonzales, Marcel Nassar, and Matthew Spellings. 2022. β€œThe Open MatSci ML Toolkit: A Flexible Framework for Machine Learning in Materials Science.” arXiv.
Nguyen, Danh, Lei Tao, and Ying Li. 2022. β€œIntegration of Machine Learning and Coarse-Grained Molecular Simulations for Polymer Materials: Physical Understandings and Molecular Design.” Frontiers in Chemistry 9.
Ong, Shyue Ping, William Davidson Richards, Anubhav Jain, Geoffroy Hautier, Michael Kocher, Shreyas Cholia, Dan Gunter, Vincent L. Chevrier, Kristin A. Persson, and Gerbrand Ceder. 2013. β€œPython Materials Genomics (Pymatgen): A Robust, Open-Source Python Library for Materials Analysis.” Computational Materials Science 68 (February): 314–19.
Otis, Richard, and Zi-Kui Liu. 2017. β€œPycalphad: CALPHAD-Based Computational Thermodynamics in Python” 5 (1): 1.
Ramsundar, Bharath, Peter Eastman, Patrick Walters, Vijay Pande, Karl Leswing, and Zhenqin Wu. 2019. Deep Learning for the Life Sciences. O’Reilly Media.
Schmidt, Jonathan, MΓ‘rio R. G. Marques, Silvana Botti, and Miguel A. L. Marques. 2019. β€œRecent Advances and Applications of Machine Learning in Solid-State Materials Science.” Npj Computational Materials 5 (1): 1–36.
Shankar, Sadasivan, and Richard N. Zare. 2022. β€œThe Perils of Machine Learning in Designing New Chemicals and Materials.” Nature Machine Intelligence 4 (4): 314–15.
Somnath, Suhas, Chris R. Smith, Nouamane Laanait, Rama K. Vasudevan, Anton Ievlev, Alex Belianinov, Andrew R. Lupini, Mallikarjun Shankar, Sergei V. Kalinin, and Stephen Jesse. 2019. β€œUSID and Pycroscopy – Open Frameworks for Storing and Analyzing Spectroscopic and Imaging Data.” arXiv.
Tran, Richard, Janice Lan, Muhammed Shuaibi, Brandon M. Wood, Siddharth Goyal, Abhishek Das, Javier Heras-Domingo, et al. 2023. β€œThe Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts.” ACS Catalysis 13 (5): 3066–84.
Ward, Logan, Alexander Dunn, Alireza Faghaninia, Nils E. R. Zimmermann, Saurabh Bajaj, Qi Wang, Joseph Montoya, et al. 2018. β€œMatminer: An Open Source Toolkit for Materials Data Mining.” Computational Materials Science 152 (September): 60–69.
White, Andrew D. 2021. β€œDeep Learning for Molecules and Materials.” Living Journal of Computational Molecular Science 3 (1): 1499–99.
Zitnick, C. Lawrence, Lowik Chanussot, Abhishek Das, Siddharth Goyal, Javier Heras-Domingo, Caleb Ho, Weihua Hu, et al. 2020. β€œAn Introduction to Electrocatalyst Design Using Machine Learning for Renewable Energy Storage.” arXiv.

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