晶体结构
水准点(测量)
表征(材料科学)
计算机科学
作文(语言)
材料科学
Crystal(编程语言)
算法
钥匙(锁)
人工智能
生物系统
机器学习
纳米技术
化学
结晶学
地质学
生物
语言学
哲学
计算机安全
程序设计语言
大地测量学
作者
Rongzhi Dong,Yi Zhao,Yuqi Song,Nihang Fu,Sadman Sadeed Omee,Sourin Dey,Qinyang Li,Lai Wei,Jianjun Hu
出处
期刊:Cornell University - arXiv
日期:2022-03-27
标识
DOI:10.48550/arxiv.2203.14326
摘要
One of the long-standing problems in materials science is how to predict a material's structure and then its properties given only its composition. Experimental characterization of crystal structures has been widely used for structure determination, which is however too expensive for high-throughput screening. At the same time, directly predicting crystal structures from compositions remains a challenging unsolved problem. Herein we propose a deep learning algorithm for predicting the XRD spectrum given only the composition of a material, which can then be used to infer key structural features for downstream structural analysis such as crystal system or space group classification or crystal lattice parameter determination or materials property predictions. Benchmark studies on two datasets show that our DeepXRD algorithm can achieve good performance for XRD prediction as evaluated over our test sets. It can thus be used in high-throughput screening in the huge materials composition space for new materials discovery.
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