Juq496 2021 [2021] May 2026
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This review article provides a comprehensive overview of the emerging field of machine learning (ML) in molecular simulations. It addresses a fundamental challenge in computational chemistry: the trade-off between accuracy and speed. Traditional methods are either very fast but approximate (Classical Force Fields) or very accurate but computationally expensive (Quantum Mechanics/DFT). Behler discusses how ML bridges this gap.
