.png){kind=icon}
返回
Crystal structure prediction with machine learning-based element substitution
.png){kind=icon}
OA
AI
DOI：10.1016/j.commatsci.2022.111496.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The prediction of energetically stable crystal structures formed by a given chemical composition is a central problem in solid-state physics. In principle, the crystalline state of assembled atoms can be determined by optimizing the energy surface, which in turn can be evaluated using first-principles calculations. However, performing the iterative gradient descent on the potential energy surface using first-principles calculations is prohibitively expensive for complex systems, such as those with many atoms per unit cell. Here, we present a unique methodology for crystal structure prediction (CSP) that relies on a machine learning algorithm called metric learning. It is shown that a binary classifier, trained on a large number of already identified crystal structures, can determine the isomorphism of crystal structures formed by two given chemical compositions with an accuracy of approximately 96.4%. For a given query composition with an unknown crystal structure, the model is used to automatically select from a crystal structure database a set of template crystals with nearly identical stable structures to which element substitution is to be applied. Apart from the local relaxation calculation of the identified templates, the proposed method does not use ab initio calculations. The potential of this substitution-based CSP is demonstrated for a wide variety of crystal systems.
Keyword:
Crystal structure prediction
Metric learning
Crystal structure similarity
Element substitution
Computational material discovery
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.3
论文数:
1.4W
被引数:
3.6W
机构
引用论文
THE MEANING AND USE OF THE AREA UNDER A RECEIVER OPERATING CHARACTERISTIC (ROC) CURVE受试者工作特征 (ROC) 曲线下面积的含义和用途.png)
RADIOLOGY
RADIOLOGYIF15.2
PREAI
分享收藏VESTA 3for three-dimensional visualization of crystal, volumetric and morphology data vesta 3 用于晶体，体积和形态数据的三维可视化
PREAI
分享收藏Commentary: The Materials Project: A materials genome approach to accelerating materials innovation评论: 材料项目: 加速材料创新的材料基因组方法APL MATERIALS
OAAI
APL MATERIALSIF4.5
OAAI分享收藏Crystal structure prediction using ab initio evolutionary techniques:: Principles and applications使用从头算进化技术预测晶体结构: 原理和应用OAAI
OAAI分享收藏Accelerating crystal structure prediction by machine-learning interatomic potentials with active learningPHYSICAL REVIEW BOAAI
PHYSICAL REVIEW BIF3.7
OAAI分享收藏
查看更多内容