<?xml version='1.0' encoding='utf-8'?> <oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"> <dc:creator>Bonati, Luigi</dc:creator> <dc:creator>Zhang, Yue-Yu</dc:creator> <dc:creator>Parrinello, Michele</dc:creator> <dc:date>2019-10-22</dc:date> <dc:description>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.</dc:description> <dc:identifier>https://archive.materialscloud.org/record/2019.0065/v1</dc:identifier> <dc:identifier>doi:10.24435/materialscloud:2019.0065/v1</dc:identifier> <dc:identifier>mcid:2019.0065/v1</dc:identifier> <dc:identifier>oai:materialscloud.org:231</dc:identifier> <dc:language>en</dc:language> <dc:publisher>Materials Cloud</dc:publisher> <dc:rights>info:eu-repo/semantics/openAccess</dc:rights> <dc:rights>Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights> <dc:subject>MARVEL</dc:subject> <dc:subject>MARVEL/DD1</dc:subject> <dc:subject>enhanced-sampling</dc:subject> <dc:subject>deep-learning</dc:subject> <dc:subject>PLUMED</dc:subject> <dc:subject>biomolecules</dc:subject> <dc:subject>silicon</dc:subject> <dc:subject>crystallization</dc:subject> <dc:subject>rare-events</dc:subject> <dc:title>Neural networks-based variationally enhanced sampling</dc:title> <dc:type>Dataset</dc:type> </oai_dc:dc>