Big Data Creates New Opportunities for Materials Research: A Review on Methods and Applications of Machine Learning for Materials Design

作     者：Teng Zhou Zhen Song Kai Sundmacher 

作者机构：Process Systems EngineeringMax Planck Institute for Dynamics of Complex Technical SystemsMagdeburg 39106Germany Process Systems EngineeringOtto-von-Guericke University MagdeburgMagdeburg 39106Germany 

基　　金：Max-Planck-Gesellschaft  MPG 

出 版 物：《Engineering》 (工程（英文）)

年 卷 期：2019年第5卷第6期

页      码：1017-1026页

摘      要：Materials development has historically been driven by human needs and desires, and this is likely to con- tinue in the foreseeable future. The global population is expected to reach ten billion by 2050, which will promote increasingly large demands for clean and high-ef ciency energy, personalized consumer prod- ucts, secure food supplies, and professional healthcare. New functional materials that are made and tai- lored for targeted properties or behaviors will be the key to tackling this challenge. Traditionally, advanced materials are found empirically or through experimental trial-and-error approaches. As big data generated by modern experimental and computational techniques is becoming more readily avail- able, data-driven or machine learning (ML) methods have opened new paradigms for the discovery and rational design of materials. In this review article, we provide a brief introduction on various ML methods and related software or tools. Main ideas and basic procedures for employing ML approaches in materials research are highlighted. We then summarize recent important applications of ML for the large-scale screening and optimal design of polymer and porous materials, catalytic materials, and energetic mate- rials. Finally, concluding remarks and an outlook are provided.

主 题 词：Big data Data-driven Machine learning Materials screening Materials design 

学科分类：0810[工学-土木类] 0830[工学-生物工程类] 0808[工学-自动化类] 0817[工学-轻工类] 08[工学] 0807[工学-电子信息类] 0805[工学-能源动力学] 0703[理学-化学类] 0812[工学-测绘类] 

核心收录：

D　O　I：10.1016/j.eng.2019.02.011

馆 藏 号：203861139...