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Liquid metal material genome： Initiation of a new research track towards discovery of advanced energy materials

Frontiers in Energy 2013年第3期7卷 317-332页

作者： Lei WANG Jing LIU Key Laboratory of Cryogenics and Beijing Key Laboratory ofCryoBiomedical Engineering Technical Institute of Physics andChemistry Chinese Academy of Sciences Beijing 100190 China Department of Biomedical Engineering School of Medicine Tsinghua University Beijing 100084 China

As the basis of modern industry, the roles materials play are becoming increasingly vital in this day and age. With many superior physical properties over conventional fluids, the low melting point liquid metal material, especially room-temperature liquid metal, is recently found to be uniquely useful in a wide variety of emerging areas from energy, electronics to medical sciences. However, with the coming enormous utilization of such materials, serious issues also arise which urgently need to be addressed. A biggest concern to impede the large scale application of room-temperature liquid metal technologies is that there is currently a strong shortage of the materials and species available to meet the tough requirements such as cost, melting point, electrical and thermal conductivity, etc. Inspired by the material Genome Initiative as issued in 2011 by the United States of America, a more specific and focused project initiative was proposed in this paper--the liquid metal material genome aimed to discover advanced new functional alloys with low melting point so as to fulfill various increasing needs. The basic schemes and road map for this new research program, which is expected to have a worldwide significance, were outlined. The theoretical strategies and experimental methods in the research and development of liquid metal material genome were introduced. Particularly, the calculation of phase diagram （CALPHAD） approach as a highly effective way for material design was discussed. Further, the first-principles （FP） calculation was suggested to combine with the statistical thermo- dynamics to calculate the thermodynamic functions so as to enrich the CALPHAD database of liquid metals. When the experimental data are too scarce to perform a regular treatment, the combination of FP calculation, cluster variation method （CVM） or molecular dynamics （MD）, and CALPHAD, referred to as the mixed FP-CVM- CALPHAD method can be a promising way to solve the problem. Except for th

关键词： liquid metal material genome energy material material discovery advanced material room-tempera- ture liquid alloy thermodynamics phase diagram

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Computational discovery of energy materials in the era of big data and machine learning:A critical review

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materials Reports(Energy) 2021年第3期1卷 2-19页

作者： Ziheng Lu Department of materials Science&Metallurgy University of Cambridge27 Charles Babbage RoadCambridgeCB30FSUK

The discovery of novel materials with desired properties is essential to the advancements of energy-related *** the rapid development of computational infrastructures and theoretical approaches,progress so far has been limited by the empirical and serial nature of experimental ***,the situation is changing thanks to the maturation of theoretical tools such as density functional theory,high-throughput screening,crystal structure prediction,and emerging approaches based on machine *** these recent innovations in computational chemistry,data informatics,and machine learning have acted as catalysts for revolutionizing material design and hopefully will lead to faster kinetics in the development of energy-related *** this report,recent advances in material discovery methods are reviewed for energy *** paradigms based on empiricism-driven experiments,database-driven high-throughput screening,and data informatics-driven machine learning are discussed *** methodological advancements involved are reviewed including high-throughput screening,crystal structure prediction,and generative models for target material *** applications in energy-related devices such as batteries,catalysts,and photovoltaics are selectively showcased.

关键词： Machine learning material discovery Crystal structure prediction Deep learning Generative model Inverse material design High throughput screening Density functional theory

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