Materials informatics

Machine learning in condensed matter physics, chemistry and materials science

August 1, 2023 — August 8, 2023

dynamical systems
how do science
machine learning
neural nets
stochastic processes
time series
Figure 1

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.

1 Master lists

2 Please help me sort out all these projects

3 References

Baird, Diep, and Sparks. 2022. DiSCoVeR: A Materials Discovery Screening Tool for High Performance, Unique Chemical Compositions.” Digital Discovery.
Beeler, Subramanian, Sprague, et al. 2023. ChemGymRL: An Interactive Framework for Reinforcement Learning for Digital Chemistry.”
Behler. 2016. Perspective: Machine Learning Potentials for Atomistic Simulations.” The Journal of Chemical Physics.
Bellinger, Drozdyuk, Crowley, et al. 2022. Balancing Information with Observation Costs in Deep Reinforcement Learning.” In Proceedings of the Canadian Conference on Artificial Intelligence.
Biegler. 2010. Nonlinear Programming: Concepts, Algorithms, and Applications to Chemical Processes.
Butler, Davies, Cartwright, et al. 2018. Machine Learning for Molecular and Materials Science.” Nature.
Carleo, Cirac, Cranmer, et al. 2019. Machine Learning and the Physical Sciences.” Reviews of Modern Physics.
Chanussot, Das, Goyal, et al. 2021. The Open Catalyst 2020 (OC20) Dataset and Community Challenges.” ACS Catalysis.
Deiana, Tran, Agar, et al. 2021. Applications and Techniques for Fast Machine Learning in Science.” arXiv:2110.13041 [Physics].
Deringer, Bartók, Bernstein, et al. 2021. Gaussian Process Regression for Materials and Molecules.” Chemical Reviews.
Durumeric, Charron, Templeton, et al. 2023. Machine Learned Coarse-Grained Protein Force-Fields: Are We There yet? Current Opinion in Structural Biology.
Faroughi, Pawar, Fernandes, et al. 2023. Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks in Scientific Computing.”
Haghighat, Raissi, Moure, et al. 2021. A Physics-Informed Deep Learning Framework for Inversion and Surrogate Modeling in Solid Mechanics.” Computer Methods in Applied Mechanics and Engineering.
Himanen, Geurts, Foster, et al. 2019. Data-Driven Materials Science: Status, Challenges, and Perspectives.” Advanced Science.
Jin, Zhang, and Espinosa. 2023. Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review.”
Joshi, and Deshmukh. 2021. A Review of Advancements in Coarse-Grained Molecular Dynamics Simulations.” Molecular Simulation.
Kontolati, Alix-Williams, Boffi, et al. 2021. Manifold Learning for Coarse-Graining Atomistic Simulations: Application to Amorphous Solids.” Acta Materialia.
Medasani, Gamst, Ding, et al. 2016. Predicting Defect Behavior in B2 Intermetallics by Merging Ab Initio Modeling and Machine Learning.” Npj Computational Materials.
Miret, Lee, Gonzales, et al. 2022. The Open MatSci ML Toolkit: A Flexible Framework for Machine Learning in Materials Science.”
Nguyen, Tao, and Li. 2022. Integration of Machine Learning and Coarse-Grained Molecular Simulations for Polymer Materials: Physical Understandings and Molecular Design.” Frontiers in Chemistry.
Ong, Richards, Jain, et al. 2013. Python Materials Genomics (Pymatgen): A Robust, Open-Source Python Library for Materials Analysis.” Computational Materials Science.
Otis, and Liu. 2017. Pycalphad: CALPHAD-Based Computational Thermodynamics in Python.”
Ramsundar, Eastman, Walters, et al. 2019. Deep Learning for the Life Sciences.
Schmidt, Marques, Botti, et al. 2019. Recent Advances and Applications of Machine Learning in Solid-State Materials Science.” Npj Computational Materials.
Shankar, and Zare. 2022. The Perils of Machine Learning in Designing New Chemicals and Materials.” Nature Machine Intelligence.
Somnath, Smith, Laanait, et al. 2019. USID and Pycroscopy – Open Frameworks for Storing and Analyzing Spectroscopic and Imaging Data.”
Tran, Lan, Shuaibi, et al. 2023. The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts.” ACS Catalysis.
van Saarloos, Vitelli, and Zeravcic. n.d. Soft Matter.
Ward, Dunn, Faghaninia, et al. 2018. Matminer: An Open Source Toolkit for Materials Data Mining.” Computational Materials Science.
White. 2021. Deep Learning for Molecules and Materials.” Living Journal of Computational Molecular Science.
Xie, Zong, Qiu, et al. 2023. PhysGaussian: Physics-Integrated 3D Gaussians for Generative Dynamics.”
Zitnick, Chanussot, Das, et al. 2020. An Introduction to Electrocatalyst Design Using Machine Learning for Renewable Energy Storage.”