Journal
INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY
Volume 117, Issue 1, Pages 33-39Publisher
WILEY
DOI: 10.1002/qua.25307
Keywords
force fields; kernel ridge regression; machine learning; materials simulation
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Funding
- JSPS KAKENHI [26610120, 26246021]
- Nippon Sheet Glass Foundation for Materials Science and Engineering
- Grants-in-Aid for Scientific Research [15K21719, 26610120, 26105010, 26105001] Funding Source: KAKEN
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Recently, machine learning has emerged as an alternative, powerful approach for predicting quantum-mechanical properties of molecules and solids. Here, using kernel ridge regression and atomic fingerprints representing local environments of atoms, we trained a machine-learning model on a crystalline silicon system to directly predict the atomic forces at a wide range of temperatures. Our idea is to construct a machine-learning model using a quantum-mechanical dataset taken from canonical-ensemble simulations at a higher temperature, or an upper bound of the temperature range. With our model, the force prediction errors were about 2% or smaller with respect to the corresponding force ranges, in the temperature region between 300 K and 1650 K. We also verified the applicability to a larger system, ensuring the transferability with respect to system size.
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