The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Machine Learning of Coarse-Grained Molecular Dynamics Force Fields
Open accessAbstract
Atomistic or ab initio molecular dynamics simulations are widely used to predict thermodynamics and kinetics and relate them to molecular structure. A common approach to go beyond the time- and length-scales accessible with such computationally expensive simulations is the definition of coarse-grained molecular models. Existing coarse-graining approaches define an effective interaction potential to match defined properties of high-resolution models or experimental data. In this paper, we reformulate coarse-graining as a supervised machine learning problem. We use statistical learning theory to decompose the coarse-graining error and cross-validation to select and compare the performance of different models. We introduce CGnets, a deep learning approach, that learns coarse-grained free energy functions and can be trained by a force-matching scheme. CGnets maintain all physically relevant invariances and allow one to incorporate prior physics knowledge to avoid sampling of unphysical structures. We show that CGnets can capture all-atom explicit-solvent free energy surfaces with models using only a few coarse-grained beads and no solvent, while classical coarse-graining methods fail to capture crucial features of the free energy surface. Thus, CGnets are able to capture multibody terms that emerge from the dimensionality reduction.
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Cites
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Artificial intelligence: A powerful paradigm for scientific research
Xu, Y., Liu, X., Cao, X., Huang, C., Liu, E., Qian, S., Liu, X., Wu, Y., Dong, F., Qiu, C.-W., et al. (2021) · The Innovation · doi:10.1016/j.xinn.2021.100179 · Cited by 1,726
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Noé, F., Tkatchenko, A., Müller, K.-R., & Clementi, C. (2020) · Annual Review of Physical Chemistry · doi:10.1146/annurev-physchem-042018-052331 · Cited by 956
Self-citationCombining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems
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Nguyen, P. H., Ramamoorthy, A., Sahoo, B. R., Zheng, J., Faller, P., Straub, J. E., Dominguez, L., Shea, J.-E., Dokholyan, N. V., De Simone, A., et al. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.0c01122 · Cited by 678
Metadata
- Authors
- Wang, J., Olsson, S., Wehmeyer, C., Pérez, A., Charron, N. E., de Fabritiis, G., Noé, F., & Clementi, C.
- Venue
- ACS Central Science
- Publisher
- American Chemical Society (ACS)
- Published
- 2019-04-15
- Volume
- 5
- Issue
- 5
- Pages
- 755-767
- ISSN
- 2374-7943 (ISSN-L)
- 2374-7951
- OpenAlex
- W2901995873
- Cited by
- 524
- References
- 20
- FWCI
- 20.41
- Type
- journal article
- Language
- en
- Open access
- diamond
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