Artificial intelligence in computational materials science

Kulik, Heather J.; Tiwary, Pratyush

doi:10.1557/s43577-022-00431-1

Artificial intelligence in computational materials science

Journal Article · 01 September 2022 · MRS Bulletin

DOI:https://doi.org/10.1557/s43577-022-00431-1· OSTI ID:1981010

Kulik, Heather J.; Tiwary, Pratyush

Not provided.

Research Organization:: Univ. of Maryland, College Park, MD (United States)

Sponsoring Organization:: USDOE Office of Science (SC)

DOE Contract Number:: SC0021009

OSTI ID:: 1981010

Journal Information:: MRS Bulletin, Vol. 47, Issue 9; ISSN 0883-7694

Publisher:: Materials Research Society

Country of Publication:: United States

Language:: English

References (16)

Opportunities and challenges of text mining in materials research Kononova, Olga; He, Tanjin; Huo, Haoyan iScience, Vol. 24, Issue 3 https://doi.org/10.1016/j.isci.2021.102155	journal	March 2021
A Two-Stage Framework for Compound Figure Separation Jiang, Weixin; Schwenker, Eric; Spreadbury, Trevor 2021 IEEE International Conference on Image Processing (ICIP) https://doi.org/10.1109/ICIP42928.2021.9506171	conference	September 2021
ImageDataExtractor: A Tool To Extract and Quantify Data from Microscopy Images Mukaddem, Karim T.; Beard, Edward J.; Yildirim, Batuhan Journal of Chemical Information and Modeling, Vol. 60, Issue 5 https://doi.org/10.1021/acs.jcim.9b00734	journal	November 2019
Machine-learning-assisted materials discovery using failed experiments Raccuglia, Paul; Elbert, Katherine C.; Adler, Philip D. F. Nature, Vol. 533, Issue 7601 https://doi.org/10.1038/nature17439	journal	May 2016
Anthropogenic biases in chemical reaction data hinder exploratory inorganic synthesis Jia, Xiwen; Lynch, Allyson; Huang, Yuheng Nature, Vol. 573, Issue 7773 https://doi.org/10.1038/s41586-019-1540-5	journal	September 2019
Adding Stochastic Negative Examples into Machine Learning Improves Molecular Bioactivity Prediction Cáceres, Elena L.; Mew, Nicholas C.; Keiser, Michael J. Journal of Chemical Information and Modeling, Vol. 60, Issue 12 https://doi.org/10.1021/acs.jcim.0c00565	journal	November 2020
Pushing the frontiers of density functionals by solving the fractional electron problem Kirkpatrick, James; McMorrow, Brendan; Turban, David H. P. Science, Vol. 374, Issue 6573 https://doi.org/10.1126/science.abj6511	journal	December 2021
Probabilistic performance estimators for computational chemistry methods: The empirical cumulative distribution function of absolute errors Pernot, Pascal; Savin, Andreas The Journal of Chemical Physics, Vol. 148, Issue 24 https://doi.org/10.1063/1.5016248	journal	June 2018
Rational Density Functional Selection Using Game Theory McAnanama-Brereton, Suzanne; Waller, Mark P. Journal of Chemical Information and Modeling, Vol. 58, Issue 1 https://doi.org/10.1021/acs.jcim.7b00542	journal	December 2017
Deep learning for electron and scanning probe microscopy: From materials design to atomic fabrication Kalinin, Sergei V.; Ziatdinov, Maxim; Spurgeon, Steven R. MRS Bulletin, Vol. 47, Issue 9 https://doi.org/10.1557/s43577-022-00413-3	journal	September 2022
The LEGOLAS Kit: A low-cost robot science kit for education with symbolic regression for hypothesis discovery and validation Saar, Logan; Liang, Haotong; Wang, Alex MRS Bulletin, Vol. 47, Issue 9 https://doi.org/10.1557/s43577-022-00430-2	journal	September 2022
High-throughput computations and machine learning for halide perovskite discovery Yang, Jiaqi; Mannodi-Kanakkithodi, Arun MRS Bulletin, Vol. 47, Issue 9 https://doi.org/10.1557/s43577-022-00414-2	journal	September 2022
Machine learning for molecular simulations of crystal nucleation and growth Sarupria, Sapna; Hall, Steven W.; Rogal, Jutta MRS Bulletin, Vol. 47, Issue 9 https://doi.org/10.1557/s43577-022-00407-1	journal	September 2022
Kinetic network models to study molecular self-assembly in the wake of machine learning Liu, Bojun; Qiu, Yunrui; Goonetilleke, Eshani C. MRS Bulletin, Vol. 47, Issue 9 https://doi.org/10.1557/s43577-022-00415-1	journal	September 2022
FAIR data enabling new horizons for materials research Scheffler, Matthias; Aeschlimann, Martin; Albrecht, Martin Nature, Vol. 604, Issue 7907 https://doi.org/10.1038/s41586-022-04501-x	journal	April 2022
Recent advances and applications of machine learning in solid-state materials science Schmidt, Jonathan; Marques, Mário R. G.; Botti, Silvana npj Computational Materials, Vol. 5, Issue 1 https://doi.org/10.1038/s41524-019-0221-0	journal	August 2019

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