Neural networks-based variationally enhanced sampling

L Bonati and YY Zhang and M Parrinello, PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 116, 17641-17647 (2019).

DOI: 10.1073/pnas.1907975116

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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