Neural networks-based variationally enhanced sampling.
- Bonati L Department of Physics, ETH Zurich, 8092 Zurich, Switzerland.
- Zhang YY Facoltà di Informatica, Instituto di Scienze Computazionali, Università della Svizzera italiana (USI), 6900 Lugano, Switzerland.
- Parrinello M Facoltà di Informatica, Instituto di Scienze Computazionali, Università della Svizzera italiana (USI), 6900 Lugano, Switzerland; parrinello@phys.chem.ethz.ch.
- 2019-08-17
Published in:
- Proceedings of the National Academy of Sciences of the United States of America. - 2019
English
Sampling complex free-energy surfaces is one of the main challenges of modern atomistic simulation methods. The presence of kinetic bottlenecks in such surfaces often renders a direct approach useless. A popular strategy is to identify a small number of key collective variables and to introduce a bias potential that is able to favor their fluctuations in order to accelerate sampling. Here, we propose to use machine-learning techniques in conjunction with the recent variationally enhanced sampling method [O. Valsson, M. Parrinello, Phys. Rev. Lett. 113, 090601 (2014)] in order to determine such potential. This is achieved by expressing the bias as a neural network. The parameters are determined in a variational learning scheme aimed at minimizing an appropriate functional. This required the development of a more efficient minimization technique. The expressivity of neural networks allows representing rapidly varying free-energy surfaces, removes boundary effects artifacts, and allows several collective variables to be handled.
- Language
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- English
- Open access status
- bronze
- Identifiers
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- DOI 10.1073/pnas.1907975116
- PMID 31416918
- Persistent URL
- https://folia.unifr.ch/global/documents/288635
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