Materials Discovery and Properties Prediction in Thermal Transport via Materials Informatics: A Mini Review
- 15 May 2019
- journal article
- review article
- Published by American Chemical Society (ACS) in Nano Letters
- Vol. 19 (6), 3387-3395
- https://doi.org/10.1021/acs.nanolett.8b05196
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, etc. 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. Based on the review of past research, perspectives are discussed and future directions for studying functional thermal materials by materials informatics are given.Keywords
Funding Information
- Ministry of Education of the People's Republic of China (2019kfyRCPY045)
- Natural Science Foundation of Hubei Province (2017CFA046)
- National Natural Science Foundation of China (51576076, 51711540031, 51606072)
This publication has 70 references indexed in Scilit:
- Thermal conductivity of isotopically modified grapheneNature Materials, 2012
- Combinatorial Materials Sciences: Experimental Strategies for Accelerated Knowledge DiscoveryAnnual Review of Materials Research, 2008
- New Directions for Low‐Dimensional Thermoelectric MaterialsAdvanced Materials, 2007
- Artificial neural network approach for evaluation of temperature and density profiles of salt gradient solar pondJournal of the Energy Institute, 2007
- Materials informaticsMaterials Today, 2005
- The Bayesian approach to global optimizationPublished by Springer Science and Business Media LLC ,2005
- Thermoelectricity in Semiconductor NanostructuresScience, 2004
- Random Forest: A Classification and Regression Tool for Compound Classification and QSAR ModelingJournal of Chemical Information and Computer Sciences, 2003
- Application of artificial neural network to predict the optimal start time for heating system in buildingEnergy Conversion and Management, 2003
- Growth of nanowire superlattice structures for nanoscale photonics and electronicsNature, 2002