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Neural networks-based variationally enhanced sampling

Luigi Bonati1*, Yue-Yu Zhang2, Michele Parrinello2

1 Department of Physics, ETH Zurich, 8092 Zurich, Switzerland and Facoltà di Informatica, Instituto di Scienze Computazionali, Università della Svizzera italiana, 6900 Lugano, Switzerland

2 Department of Chemistry and Applied Biosciences, ETH Zurich, 8092 Zurich, Switzerland and Facoltà di Informatica, Instituto di Scienze Computazionali, Università della Svizzera italiana (USI), 6900 Lugano, Switzerland

* Corresponding authors emails: luigi.bonati@phys.chem.ethz.ch
DOI10.24435/materialscloud:2019.0065/v1 [version v1]

Publication date: Oct 22, 2019

How to cite this record

Luigi Bonati, Yue-Yu Zhang, Michele Parrinello, Neural networks-based variationally enhanced sampling, Materials Cloud Archive 2019.0065/v1 (2019), https://doi.org/10.24435/materialscloud:2019.0065/v1


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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External references

Journal reference
L. Bonati, Y.-Y. Zhang, M. Parrinello, Proceedings of the National Academy of Sciences, 116(36), 17641–17647 (2019) doi:10.1073/pnas.1907975116


MARVEL MARVEL/DD1 enhanced-sampling deep-learning PLUMED biomolecules silicon crystallization rare-events

Version history:

2019.0065/v1 (version v1) [This version] Oct 22, 2019 DOI10.24435/materialscloud:2019.0065/v1