# Machine learning approaches for analyzing and enhancing molecular dynamics simulations

Source: [Machine learning approaches for analyzing and enhancing molecular dynamics simulations](<https://www.sota2.com/research/paper/68fa3aa41e209131ce8cc758>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T00:10:47.570Z

Authors: Yihang Wang, Joao Marcelo Lamim Ribeiro, Pratyush Tiwary

Published: 2019-09-25T20:19:40.000Z

## Abstract

Molecular dynamics (MD) has become a powerful tool for studying biophysical systems, due to increasing computational power and availability of software. Although MD has made many contributions to better understanding these complex biophysical systems, there remain methodological difficulties to be surmounted. First, how to make the deluge of data generated in running even a microsecond long MD simulation human comprehensible. Second, how to efficiently sample the underlying free energy surface and kinetics. In this short perspective, we summarize machine learning based ideas that are solving both of these limitations, with a focus on their key theoretical underpinnings and remaining challenges.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1909.11748>)

[PDF](<https://arxiv.org/pdf/1909.11748.pdf>)