The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Coarse-graining auto-encoders for molecular dynamics
Open accessAbstract
Molecular dynamics simulations provide theoretical insight into the microscopic behavior of condensed-phase materials and, as a predictive tool, enable computational design of new compounds. However, because of the large spatial and temporal scales of thermodynamic and kinetic phenomena in materials, atomistic simulations are often computationally infeasible. Coarse-graining methods allow larger systems to be simulated by reducing their dimensionality, propagating longer timesteps, and averaging out fast motions. Coarse-graining involves two coupled learning problems: defining the mapping from an all-atom representation to a reduced representation, and parameterizing a Hamiltonian over coarse-grained coordinates. We propose a generative modeling framework based on variational auto-encoders to unify the tasks of learning discrete coarse-grained variables, decoding back to atomistic detail, and parameterizing coarse-grained force fields. The framework is tested on a number of model systems including single molecules and bulk-phase periodic simulations.
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Cites
Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules
Gómez-Bombarelli, R., Wei, J. N., Duvenaud, D., Hernández-Lobato, J. M., Sánchez-Lengeling, B., Sheberla, D., Aguilera-Iparraguirre, J., Hirzel, T. D., Adams, R. P., & Aspuru-Guzik, A. (2018) · ACS Central Science · doi:10.1021/acscentsci.7b00572 · Cited by 3,106
Self-citationGenerating Sentences from a Continuous Space
Bowman, S. R., Vilnis, L., Vinyals, O., Dai, A., Jozefowicz, R., & Bengio, S. (2016) · Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning · doi:10.18653/v1/k16-1002 · Cited by 2,061
Transferability of coarse-grained force fields: The polymer case
Carbone, P., Varzaneh, H. A. K., Chen, X., & Müller-Plathe, F. (2008) · The Journal of Chemical Physics · doi:10.1063/1.2829409 · Cited by 219
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Physics-informed machine learning
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Scientific discovery in the age of artificial intelligence.
Wang, H., Fu, T., Du, Y., Gao, W., Huang, K., Liu, Z., Chandak, P., Liu, S., Van Katwyk, P., Deac, A., et al. (2023) · Nature · doi:10.1038/s41586-023-06221-2 · Cited by 1,823
Machine Learning for Molecular Simulation
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
Molecular contrastive learning of representations via graph neural networks
Wang, Y., Wang, J., Cao, Z., & Barati Farimani, A. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00447-x · Cited by 851
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Coley, C. W., Eyke, N. S., & Jensen, K. F. (2020) · Angewandte Chemie International Edition · doi:10.1002/anie.201909987 · Cited by 317
Metadata
- Authors
- Wang, W., & Gómez-Bombarelli, R.
- Venue
- npj Computational Materials
- Published
- 2019-12-18
- Volume
- 5
- Issue
- 1
- ISSN
- 2057-3960 (ISSN-L)
- OpenAlex
- W2994626356
- Cited by
- 175
- References
- 3
- FWCI
- 6.86
- Type
- journal article
- Language
- en
- Open access
- gold
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