2026-04-10T23:38:35Z https://archive.materialscloud.org/oai2d

oai:materialscloud.org:231 2019-10-22T00:00:00Z openaire_data community-mcarchive

Bonati, Luigi Bonati, Luigi Zhang, Yue-Yu Parrinello, Michele 2019-10-22 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. application/zip text/plain text/markdown https://doi.org/10.24435/materialscloud:2019.0065/v1 oai:materialscloud.org:231 mcid:2019.0065/v1 eng Materials Cloud https://doi.org/10.1073/pnas.1907975116 https://archive.materialscloud.org/communities/mcarchive https://doi.org/10.24435/materialscloud:pc-6d info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode MARVEL MARVEL/DD1 enhanced-sampling deep-learning PLUMED biomolecules silicon crystallization rare-events Neural networks-based variationally enhanced sampling info:eu-repo/semantics/other