Literature DB >> 31090428

Materials Discovery and Properties Prediction in Thermal Transport via Materials Informatics: A Mini Review.

Xiao Wan, Wentao Feng, Yunpeng Wang, Haidong Wang1, Xing Zhang1, Chengcheng Deng, Nuo Yang.   

Abstract

There has been increasing demand for materials with functional thermal properties, but traditional experiments and simulations are high-cost and time-consuming. The emerging discipline, materials informatics, is an effective approach that can accelerate materials development by combining material science and big data techniques. Recently, materials informatics has been successfully applied to designing thermal materials, such as thermal interface materials for heat-dissipation, thermoelectric materials for power generation, and so forth. This Mini Review summarizes the research progress associated with studies regarding the prediction and discovery of materials with desirable thermal transport properties by using materials informatics. On the basis of the review of past research, perspectives are discussed and future directions for studying functional thermal materials by materials informatics are given.

Keywords:  Materials informatics; interfacial thermal conductance; machine learning; material discovery; thermal conductivity; thermoelectric properties

Year:  2019        PMID: 31090428     DOI: 10.1021/acs.nanolett.8b05196

Source DB:  PubMed          Journal:  Nano Lett        ISSN: 1530-6984            Impact factor:   11.189


  4 in total

1.  Using Open Data to Rapidly Benchmark Biomolecular Simulations: Phospholipid Conformational Dynamics.

Authors:  Hanne S Antila; Tiago M Ferreira; O H Samuli Ollila; Markus S Miettinen
Journal:  J Chem Inf Model       Date:  2021-01-26       Impact factor: 4.956

Review 2.  Machine-Learning-Assisted De Novo Design of Organic Molecules and Polymers: Opportunities and Challenges.

Authors:  Guang Chen; Zhiqiang Shen; Akshay Iyer; Umar Farooq Ghumman; Shan Tang; Jinbo Bi; Wei Chen; Ying Li
Journal:  Polymers (Basel)       Date:  2020-01-08       Impact factor: 4.329

Review 3.  Review on the Use of Artificial Intelligence to Predict Fire Performance of Construction Materials and Their Flame Retardancy.

Authors:  Hoang T Nguyen; Kate T Q Nguyen; Tu C Le; Guomin Zhang
Journal:  Molecules       Date:  2021-02-15       Impact factor: 4.411

4.  Machine learning based prediction of lattice thermal conductivity for half-Heusler compounds using atomic information.

Authors:  Hidetoshi Miyazaki; Tomoyuki Tamura; Masashi Mikami; Kosuke Watanabe; Naoki Ide; Osman Murat Ozkendir; Yoichi Nishino
Journal:  Sci Rep       Date:  2021-06-28       Impact factor: 4.379

  4 in total

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