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• W4313237572 startingPage "339" @default.
• W4313237572 abstract "Diffusion Monte Carlo (DMC) is one of the most accurate techniques available for calculating the electronic properties of molecules and materials, yet it often remains a challenge to economically compute forces using this technique. As a result, ab initio molecular dynamics simulations and geometry optimizations that employ Diffusion Monte Carlo forces are often out of reach. One potential approach for accelerating the computation of DMC forces is to machine learn these forces from DMC energy calculations. In this work, we employ Behler-Parrinello Neural Networks to learn DMC forces from DMC energy calculations for geometry optimization and molecular dynamics simulations of small molecules. We illustrate the unique challenges that stem from learning forces without explicit force data and from noisy energy data by making rigorous comparisons of potential energy surface, dynamics, and optimization predictions among ab initio density functional theory (DFT) simulations and machine-learning models trained on DFT energies with forces, DFT energies without forces, and DMC energies without forces. We show for three small molecules─C2, H2O, and CH3Cl─that machine-learned DMC dynamics can reproduce average bond lengths and angles within a few percent of known experimental results at one hundredth of the typical cost. Our work describes a much-needed means of performing dynamics simulations on high-accuracy, DMC PESs and for generating DMC-quality molecular geometries given current algorithmic constraints." @default.
• W4313237572 created "2023-01-06" @default.
• W4313237572 creator A5036947439 @default.
• W4313237572 creator A5070641855 @default.
• W4313237572 date "2022-12-28" @default.
• W4313237572 modified "2023-10-12" @default.
• W4313237572 title "Machine Learning Diffusion Monte Carlo Forces" @default.
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• W4313237572 cites W1678941157 @default.
• W4313237572 cites W1843396089 @default.
• W4313237572 cites W1968260576 @default.
• W4313237572 cites W1978183953 @default.
• W4313237572 cites W1979680610 @default.
• W4313237572 cites W1981368803 @default.
• W4313237572 cites W1986088231 @default.
• W4313237572 cites W1988926951 @default.
• W4313237572 cites W1989152148 @default.
• W4313237572 cites W1990834052 @default.
• W4313237572 cites W2008041424 @default.
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• W4313237572 cites W2015068945 @default.
• W4313237572 cites W2016612689 @default.
• W4313237572 cites W2025444507 @default.
• W4313237572 cites W2036060950 @default.
• W4313237572 cites W2037023338 @default.
• W4313237572 cites W2049310329 @default.
• W4313237572 cites W2053117030 @default.
• W4313237572 cites W2055526416 @default.
• W4313237572 cites W2057262256 @default.
• W4313237572 cites W2057987165 @default.
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• W4313237572 cites W2923091270 @default.
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• W4313237572 doi "https://doi.org/10.1021/acs.jpca.2c05904" @default.
• W4313237572 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36576803" @default.
• W4313237572 hasPublicationYear "2022" @default.
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