{"title":"A High-Dimensional Neural Network Potential for Co$_3$O$_4$","authors":"Amir Omranpour, Jörg Behler","doi":"arxiv-2409.11037","DOIUrl":null,"url":null,"abstract":"The Co$_3$O$_4$ spinel is an important material in oxidation catalysis. Its\nproperties under catalytic conditions, i.e., at finite temperatures, can be\nstudied by molecular dynamics simulations, which critically depend on an\naccurate description of the atomic interactions. Due to the high complexity of\nCo$_3$O$_4$, which is related to the presence of multiple oxidation states of\nthe cobalt ions, to date \\textit{ab initio} methods have been essentially the\nonly way to reliably capture the underlying potential energy surface, while\nmore efficient atomistic potentials are very challenging to construct.\nConsequently, the accessible length and time scales of computer simulations of\nsystems containing Co$_3$O$_4$ are still severely limited. Rapid advances in\nthe development of modern machine learning potentials (MLPs) trained on\nelectronic structure data now make it possible to bridge this gap. In this\nwork, we employ a high-dimensional neural network potential (HDNNP) to\nconstruct a MLP for bulk Co$_3$O$_4$ spinel based on density functional theory\ncalculations. After a careful validation of the potential, we compute various\nstructural, vibrational, and dynamical properties of the Co$_3$O$_4$ spinel\nwith a particular focus on its temperature-dependent behavior, including the\nthermal expansion coefficient.","PeriodicalId":501234,"journal":{"name":"arXiv - PHYS - Materials Science","volume":"21 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - PHYS - Materials Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.11037","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The Co$_3$O$_4$ spinel is an important material in oxidation catalysis. Its
properties under catalytic conditions, i.e., at finite temperatures, can be
studied by molecular dynamics simulations, which critically depend on an
accurate description of the atomic interactions. Due to the high complexity of
Co$_3$O$_4$, which is related to the presence of multiple oxidation states of
the cobalt ions, to date \textit{ab initio} methods have been essentially the
only way to reliably capture the underlying potential energy surface, while
more efficient atomistic potentials are very challenging to construct.
Consequently, the accessible length and time scales of computer simulations of
systems containing Co$_3$O$_4$ are still severely limited. Rapid advances in
the development of modern machine learning potentials (MLPs) trained on
electronic structure data now make it possible to bridge this gap. In this
work, we employ a high-dimensional neural network potential (HDNNP) to
construct a MLP for bulk Co$_3$O$_4$ spinel based on density functional theory
calculations. After a careful validation of the potential, we compute various
structural, vibrational, and dynamical properties of the Co$_3$O$_4$ spinel
with a particular focus on its temperature-dependent behavior, including the
thermal expansion coefficient.