数据与计算发展前沿 ›› 2020, Vol. 2 ›› Issue (1): 128-141.

doi: 10.11871/jfdc.issn.2096-742X.2020.01.011

所属专题： “高性能与高通量计算及应用”专刊

• 专刊:高性能与高通量计算及应用 • 上一篇    

材料基因方法在材料设计中的应用

钱旭,田子奇()   

  1. 中国科学院宁波材料技术与工程研究所,浙江 宁波 315201
  • 收稿日期:2019-11-29 出版日期:2020-02-20 发布日期:2020-03-28
  • 通讯作者: 田子奇
  • 作者简介:钱旭,中国科学院宁波材料技术与工程研究所,硕士在读,就读于中国科学技术大学纳米科学技术学院,现在中国科学院宁波材料技术与工程研究所联合培养。目前主要从事催化模拟方面的高通量计算研究工作。
    本文负责检索文献并完成文章写作。
    Qian Xu, is a master candidate studying at the School of Nanoscience and Technology of the University of Science and Technology of China. He is currently co-cultivated by the Ningbo Institute of Materials Technology and Engineering of the Chinese Academy of Sciences. He is mainly engaged in high-throughput calculation research in catalytic simulation.
    In this paper, he is responsible for completing the analysis of both domestic and international research review and finishing the manuscript.
    E-mail: qianxu@nimte.ac.cn|田子奇,中国科学院宁波材料技术与工程研究所,副研究员,分别于2009年和2014年在南京大学获得物理化学学士和博士学位。之后在加州大学河滨分校从事了三年博士后研究。现在在中国科学院宁波材料技术与工程研究所任副研究员。主要从事气体分离与转化材料的理论研究工作。
    本文指导钱旭完成了论文的写作。
    Tian Ziqi received his B. S. and Ph. D. in physical chemistry from Nanjing University in 2009 and 2014, respectively. Then he worked as a postdoctoral researcher at University of California, Riverside for three years. Currently he is an associate professor in Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Science. His research mainly focuses on theoretical investigation of novel material for gas separation and conversion.
    In this paper, he is responsible for the manuscript writing instruction.
  • 基金资助:
    国家自然科学基金青年科学基金项目(21803074)

The Application of Materials Genome Approach in Materials Design

Qian Xu,Tian Ziqi()   

  1. Ningbo Institute of Materials Technology & Engineering, Chinese Academy of Sciences, Ningbo, Zhejiang 315201, China
  • Received:2019-11-29 Online:2020-02-20 Published:2020-03-28
  • Contact: Tian Ziqi

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摘要：

【目的】本文主要介绍材料基因方法在一系列材料设计中的应用,如开发高性能催化材料、热电材料、金属有机框架(MOFs)材料、锂电池材料以及钙钛矿型光伏材料。【方法】将高通量计算与机器学习等数据挖掘技术结合,通过高通量计算产生一定规模的数据库,进而对材料数据库进行数据挖掘和分析。【结果】利用数据内在规律发现并筛选出潜在的新材料。【局限】目前,很多理论预测的材料在实验中合成制备还比较困难,因此理论与实验还需要更加深入地结合。【结论】随着计算机数据技术以及实验合成方法的进一步发展,材料基因方法将会在材料开发方面展现出更显著的作用。

关键词: 高通量计算, 材料基因方法, 催化材料, 热电材料, 金属有机框架, 锂电池, 钙钛矿, 机器学习

Abstract:

[Objective] In this paper, we introduce the application of materials genome approach on materials design, including explorations of catalytic materials, thermoelectric materials, metal organic framework (MOF) materials, lithium battery materials and perovskite photovoltaic materials. [Methods] High-throughput computing is combined with data mining techniques, machine learning for instance. Database is generated from the high-throughput computing, and then data mining and deep analysis are performed. [Results] Potential novel materials are screened and discovered based on the data analysis. [Limitations] Currently, some hypothetical materials are hardly realized in experiments. Thus, the theoretical predictions and experiments need to be integrated more deeply. [Conclusion] With the further development of computational and experimental technology, materials genetic approach will perform a more significant role in materials development.

Key words: high-throughput calculations, materials genome approach, catalysis, thermoelectric materials, metal-organic frameworks, lithium battery, perovskites, machine learning