{"title":"A high-dimensional neural network potential for Co<sub>3</sub>O<sub>4</sub>.","authors":"Amir Omranpour, Jörg Behler","doi":"10.1088/1361-648X/ad9f09","DOIUrl":null,"url":null,"abstract":"<p><p>The Co3O4 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 Co3O4, which is related to the presence of multiple oxidation states of the cobalt ions, to date 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 Co3O4 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 Co3O4 spinel based on density functional theory calculations. After a careful validation of the potential, we compute various structural, vibrational, and dynamical properties of the Co3O4 spinel with a particular focus on its temperature-dependent behavior, including the thermal expansion coefficient.</p>","PeriodicalId":16776,"journal":{"name":"Journal of Physics: Condensed Matter","volume":" ","pages":""},"PeriodicalIF":2.3000,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Physics: Condensed Matter","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.1088/1361-648X/ad9f09","RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"PHYSICS, CONDENSED MATTER","Score":null,"Total":0}
引用次数: 0
Abstract
The Co3O4 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 Co3O4, which is related to the presence of multiple oxidation states of the cobalt ions, to date 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 Co3O4 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 Co3O4 spinel based on density functional theory calculations. After a careful validation of the potential, we compute various structural, vibrational, and dynamical properties of the Co3O4 spinel with a particular focus on its temperature-dependent behavior, including the thermal expansion coefficient.
期刊介绍:
Journal of Physics: Condensed Matter covers the whole of condensed matter physics including soft condensed matter and nanostructures. Papers may report experimental, theoretical and simulation studies. Note that papers must contain fundamental condensed matter science: papers reporting methods of materials preparation or properties of materials without novel condensed matter content will not be accepted.