Publication date: Oct 22, 2019
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.
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deep-ves-data.zip
MD5md5:5dbc1f387b7e5e53ae26b3a93552642c
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42.7 MiB | Inputs and results (ZIP) |
README.txt
MD5md5:9992c90534bde3a6bd680a9e2d4825a8
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504 Bytes | README |
2019.0065/v1 (version v1) [This version] | Oct 22, 2019 | DOI10.24435/materialscloud:2019.0065/v1 |