材料科学
原子间势
纳米技术
密度泛函理论
电子结构
原子单位
超级电容器
比例(比率)
纳米颗粒
工作(物理)
计算机科学
分子动力学
电极
计算化学
物理
热力学
凝聚态物理
化学
量子力学
电化学
作者
Volker L. Deringer,A. Miguel,Gábor Cśanyi
标识
DOI:10.1002/adma.201902765
摘要
Abstract Atomic‐scale modeling and understanding of materials have made remarkable progress, but they are still fundamentally limited by the large computational cost of explicit electronic‐structure methods such as density‐functional theory. This Progress Report shows how machine learning (ML) is currently enabling a new degree of realism in materials modeling: by “learning” electronic‐structure data, ML‐based interatomic potentials give access to atomistic simulations that reach similar accuracy levels but are orders of magnitude faster. A brief introduction to the new tools is given, and then, applications to some select problems in materials science are highlighted: phase‐change materials for memory devices; nanoparticle catalysts; and carbon‐based electrodes for chemical sensing, supercapacitors, and batteries. It is hoped that the present work will inspire the development and wider use of ML‐based interatomic potentials in diverse areas of materials research.
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