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On the emergence of machine-learning methods in bottom-up coarse-graining
Patrick G Sahrmann1, Gregory A Voth1
1Department of Chemistry, Chicago Center for Theoretical Chemistry, James Franck Institute, and Institute for Biophysical Dynamics, The University of Chicago, Chicago, IL 60637, USA.
Current Opinion in Structural Biology
|January 3, 2025
Summary
Machine learning is advancing molecular modeling by creating coarse-grained force fields. This review explores the potential and hurdles of using these AI methods for chemical and biological systems.
Area of Science:
- Computational Chemistry
- Biophysics
- Materials Science
Background:
- Machine learning (ML) is increasingly adopted in computational chemistry.
- Neural networks have shown success in learning atomistic force fields.
- This has led to interest in applying ML to thermodynamic coarse-graining.
Purpose of the Study:
- To review the current viability of ML for coarse-grained force fields.
- To discuss challenges in ML-driven coarse-grained modeling.
- To highlight the utility of ML across various coarse-grained applications.
Main Methods:
- Review of recent literature on machine learning in coarse-grained modeling.
- Analysis of neural network applications for force-field development.
- Exploration of ML's role in thermodynamic coarse-graining.
Main Results:
- ML methods show promise for developing accurate coarse-grained force fields.
- Challenges include data requirements and model interpretability.
- ML offers utility in diverse aspects of coarse-grained simulations.
Conclusions:
- ML is a viable and powerful tool for coarse-grained force-field development.
- Addressing current challenges will further enhance ML's impact.
- ML integration is crucial for advancing molecular and systems modeling.
