<?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>Yang, Manyi</dc:creator> <dc:creator>Bonati, Luigi</dc:creator> <dc:creator>Polino, Daniela</dc:creator> <dc:creator>Parrinello, Michele</dc:creator> <dc:date>2021-04-08</dc:date> <dc:description>The study of chemical reactions in aqueous media is very important for its implications in several fields of science, from biology to industrial processes. However, modeling these reactions is difficult when water directly participates in the reaction, since it requires a fully quantum mechanical description of the system. Ab-initio molecular dynamics is the ideal candidate to shed light on these processes. However, its scope is limited by a high computational cost. A popular alternative is to perform molecular dynamics simulations powered by machine learning potentials, trained on an extensive set of quantum mechanical calculations. Doing so reliably for reactive processes is difficult because it requires including very many intermediate and transition state configurations. In this study we used an active learning procedure accelerated by enhanced sampling to harvest such structures and to build a neural-network potential to study the urea decomposition process in water. This allowed us to obtain the free energy profiles of this important reaction in a wide range of temperatures, to discover several novel metastable states, and improve the accuracy of the kinetic rates calculations. Furthermore, we found that the formation of the zwitterionic intermediate has the same probability of occurring via an acidic or a basic pathway, which could be the cause of the insensitivity of reaction rates to the solution pH.</dc:description> <dc:identifier>https://archive.materialscloud.org/record/2021.55</dc:identifier> <dc:identifier>doi:10.24435/materialscloud:4v-0w</dc:identifier> <dc:identifier>mcid:2021.55</dc:identifier> <dc:identifier>oai:materialscloud.org:801</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>Neural network potentials</dc:subject> <dc:subject>Metadynamics</dc:subject> <dc:subject>Urea decomposition</dc:subject> <dc:subject>Free energy surface</dc:subject> <dc:subject>Kinetic rates</dc:subject> <dc:subject>MARVEL/DD1</dc:subject> <dc:subject>ERC</dc:subject> <dc:title>Using metadynamics to build neural network potentials for reactive events: the case of urea decomposition in water</dc:title> <dc:type>Dataset</dc:type> </oai_dc:dc>