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。
[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.
作者
钱旭
田子奇
Qian Xu;Tian Ziqi(Ningbo Institute of Materials Technology&Engineering,Chinese Academy of Sciences,Ningbo,Zhejiang 315201,China)
出处
《数据与计算发展前沿》
2020年第1期128-141,共14页
Frontiers of Data & Computing
基金
国家自然科学基金青年科学基金项目(21803074)。
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
high-throughput calculations
materials genome approach
catalysis
thermoelectric materials
metal-organic frameworks
lithium battery
perovskites
machine learning