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- W4229448169 abstract "Machine learning (ML) has emerged as a pervasive tool in science, engineering, and beyond. Its success has also led to several synergies with molecular dynamics (MD) simulations, which we use to identify and characterize the major metastable states of molecular systems. Typically, we aim to determine the relative stabilities of these states and how rapidly they interchange. This information allows mechanistic descriptions of molecular mechanisms, enables a quantitative comparison with experiments, and facilitates their rational design. ML impacts all aspects of MD simulations -- from analyzing the data and accelerating sampling to defining more efficient or more accurate simulation models." @default.
- W4229448169 created "2022-05-11" @default.
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- W4229448169 date "2022-05-06" @default.
- W4229448169 modified "2023-09-23" @default.
- W4229448169 title "Machine Learning in Molecular Dynamics Simulations of Biomolecular Systems" @default.
- W4229448169 doi "https://doi.org/10.48550/arxiv.2205.03135" @default.
- W4229448169 hasPublicationYear "2022" @default.
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