Computational Data-Driven Materials Discovery

被引：21

作者：

Mannodi-Kanakkithodi, Arun ^{[1
,2
]}

Chan, Maria K. Y. ^{[1
]}

机构：

[1] Argonne Natl Lab, Ctr Nanoscale Mat, Argonne, IL 60439 USA

[2] Purdue Univ, Sch Mat Engn, W Lafayette, IN 47907 USA

来源：

TRENDS IN CHEMISTRY | 2021年 / 3卷 / 02期

关键词：

14;

D O I：

10.1016/j.trechm.2020.12.007

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

Machine learning (ML) from large materials datasets enables accelerated materials discovery. Currently, the most accessible way to generate uniform, well-curated, voluminous datasets is by the application of high-throughput first principles computations. Here, we present the guiding principles of using computational data and ML to drive new materials discovery.

引用

页码：79 / 82

页数：4

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