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• W4297263419 endingPage "2098" @default.
• W4297263419 startingPage "2084" @default.
• W4297263419 abstract "Machine learning is becoming increasingly more important in the field of force field development. Never has it been more vital to have chemically accurate machine learning potentials because force fields become more sophisticated and their applications expand. In this study a method for developing chemically accurate Gaussian process regression models is demonstrated for an increasingly complex set of molecules. This work is an extension to previous work showing the progression of the active learning technique in producing more accurate models in much less CPU time than ever before. The per-atom active learning approach has unlocked the potential to generate chemically accurate models for molecules such as peptide-capped glycine." @default.
• W4297263419 created "2022-09-28" @default.
• W4297263419 creator A5024957621 @default.
• W4297263419 creator A5078177431 @default.
• W4297263419 date "2022-09-27" @default.
• W4297263419 modified "2023-10-01" @default.
• W4297263419 title "Producing chemically accurate atomic Gaussian process regression models by active learning for molecular simulation" @default.
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• W4297263419 doi "https://doi.org/10.1002/jcc.27006" @default.
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• W4297263419 hasPublicationYear "2022" @default.
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