{"title":"Dynamic mesophase transition induces anomalous suppressed and anisotropic phonon thermal transport","authors":"Linfeng Yu, Kexin Dong, Qi Yang, Yi Zhang, Zheyong Fan, Xiong Zheng, Huimin Wang, Zhenzhen Qin, Guangzhao Qin","doi":"10.1038/s41524-024-01442-z","DOIUrl":null,"url":null,"abstract":"<p>The physical/chemical properties undergo significant transformations in the different states arising from phase transition. However, due to the lack of a dynamic perspective, transitional mesophases are largely underexamined, constrained by the high resource burden of first principles. Here, using molecular dynamics (MD) simulations empowered by the machine-learning potential, we proffer an innovative paradigm for phase transition: regulating the thermal transport properties <i>via</i> the transitional mesophase triggered by a uniaxial force field. We investigate the mechanical, electrical, and thermal transport properties of the two-dimensional carbon allotrope of Janus-graphene with strain-engineered phase transition. Notably, we found that the transitional mesophase significantly suppresses the thermal conductivity and induces strong anisotropy near the phase transition point. Through machine-learning-driven MD simulations, we achieved high-precision atomic-level simulations of Janus-graphene. The results show that thermal vibration-induced intermediate amorphous or interfacial phases induce strong and anisotropic interfacial thermal resistance. The investigation not only endows us with a novel perspective on mesophases during phase transitions but also enhances our holistic comprehension of the evolution of material properties.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"25 1","pages":""},"PeriodicalIF":9.4000,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"npj Computational Materials","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1038/s41524-024-01442-z","RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
引用次数: 0
Abstract
The physical/chemical properties undergo significant transformations in the different states arising from phase transition. However, due to the lack of a dynamic perspective, transitional mesophases are largely underexamined, constrained by the high resource burden of first principles. Here, using molecular dynamics (MD) simulations empowered by the machine-learning potential, we proffer an innovative paradigm for phase transition: regulating the thermal transport properties via the transitional mesophase triggered by a uniaxial force field. We investigate the mechanical, electrical, and thermal transport properties of the two-dimensional carbon allotrope of Janus-graphene with strain-engineered phase transition. Notably, we found that the transitional mesophase significantly suppresses the thermal conductivity and induces strong anisotropy near the phase transition point. Through machine-learning-driven MD simulations, we achieved high-precision atomic-level simulations of Janus-graphene. The results show that thermal vibration-induced intermediate amorphous or interfacial phases induce strong and anisotropic interfacial thermal resistance. The investigation not only endows us with a novel perspective on mesophases during phase transitions but also enhances our holistic comprehension of the evolution of material properties.
期刊介绍:
npj Computational Materials is a high-quality open access journal from Nature Research that publishes research papers applying computational approaches for the design of new materials and enhancing our understanding of existing ones. The journal also welcomes papers on new computational techniques and the refinement of current approaches that support these aims, as well as experimental papers that complement computational findings.
Some key features of npj Computational Materials include a 2-year impact factor of 12.241 (2021), article downloads of 1,138,590 (2021), and a fast turnaround time of 11 days from submission to the first editorial decision. The journal is indexed in various databases and services, including Chemical Abstracts Service (ACS), Astrophysics Data System (ADS), Current Contents/Physical, Chemical and Earth Sciences, Journal Citation Reports/Science Edition, SCOPUS, EI Compendex, INSPEC, Google Scholar, SCImago, DOAJ, CNKI, and Science Citation Index Expanded (SCIE), among others.