Hongxia Li , Lang Wu , Changshun Xia , Shuiqing Huang , Meiqin Ni , Chunlin Huang , Ming Xu , Zhaohui Ruan
{"title":"利用分子动力学理论预测多层石墨氮化碳热传输和力学性能的深度神经网络势能模型","authors":"Hongxia Li , Lang Wu , Changshun Xia , Shuiqing Huang , Meiqin Ni , Chunlin Huang , Ming Xu , Zhaohui Ruan","doi":"10.1016/j.icheatmasstransfer.2024.108354","DOIUrl":null,"url":null,"abstract":"<div><div>Graphitic carbon nitride ( <figure><img></figure> ) as a kind of important 2D materials, shows great potential applications as electric and photoelectric devices. However, most reported works mainly focus on the thermal transport characteristics of monolayer <figure><img></figure> , with bulk <figure><img></figure> ignored, which also has many important applications. Herein, anisotropic thermal conductivity of bulk <figure><img></figure> is investigated with the help of molecular dynamics. Considering the unique layer-by-layer structural characteristics, a deep neural network potential (DNNP) is developed, for accurately describing the interlayer vdw interactions among different C-N layers. It has been demonstrated that DNNP can accurately describe the atomic interactions in <figure><img></figure> , which is the weakness of classical potential fields. With the help of DNNP, phonon density of states, thermal conductivities, mechanical properties of monolayer, Bi-layer and Tri-layer <figure><img></figure> are fully investigated, and the relationship among atomic structures, structural symmetry and thermal conductivity is discussed, and mechanisms on vdW interactions making difference to thermal conductivity of <figure><img></figure> are illustrated on. The anisotropic thermal conductivity of bulk <figure><img></figure> is evaluated, showing that the thermal conductivity perpendicular to the C-N planes is 1.40 Wm<sup>−1</sup>K<sup>−1</sup> which is about 1/3 of that along with C-N plane (4.41 and 4.12 Wm<sup>−1</sup>K<sup>−1</sup> in x and y direction, respectively).</div></div>","PeriodicalId":332,"journal":{"name":"International Communications in Heat and Mass Transfer","volume":"160 ","pages":"Article 108354"},"PeriodicalIF":6.4000,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A deep neural network potential model for theoretically predicting thermal transport, mechanical properties of multi-layered graphitic carbon nitride with molecular dynamics\",\"authors\":\"Hongxia Li , Lang Wu , Changshun Xia , Shuiqing Huang , Meiqin Ni , Chunlin Huang , Ming Xu , Zhaohui Ruan\",\"doi\":\"10.1016/j.icheatmasstransfer.2024.108354\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Graphitic carbon nitride ( <figure><img></figure> ) as a kind of important 2D materials, shows great potential applications as electric and photoelectric devices. However, most reported works mainly focus on the thermal transport characteristics of monolayer <figure><img></figure> , with bulk <figure><img></figure> ignored, which also has many important applications. Herein, anisotropic thermal conductivity of bulk <figure><img></figure> is investigated with the help of molecular dynamics. Considering the unique layer-by-layer structural characteristics, a deep neural network potential (DNNP) is developed, for accurately describing the interlayer vdw interactions among different C-N layers. It has been demonstrated that DNNP can accurately describe the atomic interactions in <figure><img></figure> , which is the weakness of classical potential fields. With the help of DNNP, phonon density of states, thermal conductivities, mechanical properties of monolayer, Bi-layer and Tri-layer <figure><img></figure> are fully investigated, and the relationship among atomic structures, structural symmetry and thermal conductivity is discussed, and mechanisms on vdW interactions making difference to thermal conductivity of <figure><img></figure> are illustrated on. The anisotropic thermal conductivity of bulk <figure><img></figure> is evaluated, showing that the thermal conductivity perpendicular to the C-N planes is 1.40 Wm<sup>−1</sup>K<sup>−1</sup> which is about 1/3 of that along with C-N plane (4.41 and 4.12 Wm<sup>−1</sup>K<sup>−1</sup> in x and y direction, respectively).</div></div>\",\"PeriodicalId\":332,\"journal\":{\"name\":\"International Communications in Heat and Mass Transfer\",\"volume\":\"160 \",\"pages\":\"Article 108354\"},\"PeriodicalIF\":6.4000,\"publicationDate\":\"2024-11-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Communications in Heat and Mass Transfer\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0735193324011163\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MECHANICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Communications in Heat and Mass Transfer","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0735193324011163","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MECHANICS","Score":null,"Total":0}
A deep neural network potential model for theoretically predicting thermal transport, mechanical properties of multi-layered graphitic carbon nitride with molecular dynamics
Graphitic carbon nitride ( ) as a kind of important 2D materials, shows great potential applications as electric and photoelectric devices. However, most reported works mainly focus on the thermal transport characteristics of monolayer , with bulk ignored, which also has many important applications. Herein, anisotropic thermal conductivity of bulk is investigated with the help of molecular dynamics. Considering the unique layer-by-layer structural characteristics, a deep neural network potential (DNNP) is developed, for accurately describing the interlayer vdw interactions among different C-N layers. It has been demonstrated that DNNP can accurately describe the atomic interactions in , which is the weakness of classical potential fields. With the help of DNNP, phonon density of states, thermal conductivities, mechanical properties of monolayer, Bi-layer and Tri-layer are fully investigated, and the relationship among atomic structures, structural symmetry and thermal conductivity is discussed, and mechanisms on vdW interactions making difference to thermal conductivity of are illustrated on. The anisotropic thermal conductivity of bulk is evaluated, showing that the thermal conductivity perpendicular to the C-N planes is 1.40 Wm−1K−1 which is about 1/3 of that along with C-N plane (4.41 and 4.12 Wm−1K−1 in x and y direction, respectively).
期刊介绍：
International Communications in Heat and Mass Transfer serves as a world forum for the rapid dissemination of new ideas, new measurement techniques, preliminary findings of ongoing investigations, discussions, and criticisms in the field of heat and mass transfer. Two types of manuscript will be considered for publication: communications (short reports of new work or discussions of work which has already been published) and summaries (abstracts of reports, theses or manuscripts which are too long for publication in full). Together with its companion publication, International Journal of Heat and Mass Transfer, with which it shares the same Board of Editors, this journal is read by research workers and engineers throughout the world.
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