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Oliver T. Unke, Stefan Chmiela, Huziel E. Sauceda, Michael Gastegger, Igor Poltavsky, Kristof T. Schütt, Alexandre Tkatchenko, Klaus‐Robert Müller
methods and the efficiency of classical FFs. The key idea is to learn the statistical relation between chemical structure and potential energy without relying on a preconceived notion of fixed chemical bonds or knowledge about the relevant interactions. Such universal ML approximations are in principle only limited by the quality and quantity of the reference data used to train them. This review gives an overview of applications of ML-FFs and the chemical insights that can be obtained from them. The core concepts underlying ML-FFs are described in detail, and a step-by-step guide for constructing and testing them from scratch is given. The text concludes with a discussion of the challenges that remain to be overcome by the next generation of ML-FFs.
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Unke, O. T., Chmiela, S., Sauceda, H. E., Gastegger, M., Poltavsky, I., Schütt, K. T., Tkatchenko, A., & Müller, K. (2021). Machine Learning Force Fields. Chemical Reviews, 121(16), 10142–10186. https://doi.org/10.1021/acs.chemrev.0c01111
Unke, Oliver T., et al. "Machine Learning Force Fields." Chemical Reviews, vol. 121, no. 16, 2021, pp. 10142–10186, https://doi.org/10.1021/acs.chemrev.0c01111.
Unke, Oliver T., Stefan Chmiela, Huziel E. Sauceda, Michael Gastegger, Igor Poltavsky, Kristof T. Schütt, Alexandre Tkatchenko, and Klaus‐Robert Müller. "Machine Learning Force Fields." Chemical Reviews 121, no. 16 (2021): 10142–10186. https://doi.org/10.1021/acs.chemrev.0c01111.
Unke O. T., Chmiela S., Sauceda H. E. [и др.] Machine Learning Force Fields // Chemical Reviews. — 2021. — Vol. 121, № 16. — P. 10142–10186. — URL: https://doi.org/10.1021/acs.chemrev.0c01111
@article{unke2021machine,
title = {{Machine Learning Force Fields}},
author = {Oliver T. Unke and Stefan Chmiela and Huziel E. Sauceda and Michael Gastegger and Igor Poltavsky and Kristof T. Schütt and Alexandre Tkatchenko and Klaus‐Robert Müller},
journal = {Chemical Reviews},
year = {2021},
volume = {121},
number = {16},
pages = {10142--10186},
doi = {10.1021/acs.chemrev.0c01111},
url = {https://doi.org/10.1021/acs.chemrev.0c01111}
}TY - JOUR AU - Unke, Oliver T. AU - Chmiela, Stefan AU - Sauceda, Huziel E. AU - Gastegger, Michael AU - Poltavsky, Igor AU - Schütt, Kristof T. AU - Tkatchenko, Alexandre AU - Müller, Klaus‐Robert TI - Machine Learning Force Fields JO - Chemical Reviews VL - 121 IS - 16 SP - 10142 EP - 10186 PY - 2021 DA - 2021/03/11 DO - 10.1021/acs.chemrev.0c01111 UR - https://doi.org/10.1021/acs.chemrev.0c01111 ER -
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