计算机科学
吞吐量
鉴定(生物学)
比例(比率)
数据科学
封面(代数)
机器学习
人工智能
系统工程
工程类
机械工程
物理
无线
生物
电信
量子力学
植物
作者
Chenru Duan,Aditya Nandy,Heather J. Kulik
标识
DOI:10.1146/annurev-chembioeng-092320-120230
摘要
Machine learning (ML) has become a part of the fabric of high-throughput screening and computational discovery of materials. Despite its increasingly central role, challenges remain in fully realizing the promise of ML. This is especially true for the practical acceleration of the engineering of robust materials and the development of design strategies that surpass trial and error or high-throughput screening alone. Depending on the quantity being predicted and the experimental data available, ML can either outperform physics-based models, be used to accelerate such models, or be integrated with them to improve their performance. We cover recent advances in algorithms and in their application that are starting to make inroads toward (a) the discovery of new materials through large-scale enumerative screening, (b) the design of materials through identification of rules and principles that govern materials properties, and (c) the engineering of practical materials by satisfying multiple objectives. We conclude with opportunities for further advancement to realize ML as a widespread tool for practical computational materials design.
科研通智能强力驱动
Strongly Powered by AbleSci AI