ArticleChemical science2022
Forecasting molecular dynamics energetics of polymers in solution from supervised machine learning.
Article in Chemical science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Frontier advances in molecular dynamics simulations for the design and optimization of oil-displacement polymers.RSC advances · 2025Review
- Applications of Long Short-Term Memory (LSTM) Networks in Polymeric Sciences: A Review.Polymers · 2024Review
- Detecting dynamic domains and local fluctuations in complex molecular systems via timelapse neighbors shuffling.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Forecasting molecular dynamics energetics of polymers in solution from supervised machine learning.Chemical science · 2022Article
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Machine learning techniques including neural networks are popular tools for chemical, physical and materials applications searching for viable alternative methods in the analysis of structure and energetics of systems ranging from crystals to biomolecules. Efforts are less abundant for prediction of kinetics and dynamics. Here we explore the ability of three well established recurrent neural network architectures for reproducing and forecasting the energetics of a liquid solution of ethyl acetate containing a macromolecular polymer-lipid aggregate at ambient conditions. Data models from three recurrent neural networks, ERNN, LSTM and GRU, are trained and tested on half million points time series of the macromolecular aggregate potential energy and its interaction energy with the solvent obtained from molecular dynamics simulations. Our exhaustive analyses convey that the recurrent neural network architectures investigated generate data models that reproduce excellently the time series although their capability of yielding short or long term energetics forecasts with expected statistical distributions of the time points is limited. We propose an
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Registered trials
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