Towards end-to-end structure determination from x-ray diffraction data using deep learning  

作　　者：Gabe Guo Judah Goldfeder Ling Lan Aniv Ray Albert Hanming Yang Boyuan Chen Simon J.L.Billinge Hod Lipson 

机构地区：[1]Department of Computer Science,Columbia University,New York,NY,10027,USA [2]Department of Applied Physics and Applied Mathematics,Columbia University,New York,NY,10027,USA [3]Department of Mechanical Engineering and Materials Science,Duke University,Durham,NC,27708,USA [4]Department of Mechanical Engineering,Columbia University,New York,NY,10027,USA

出　　处：《npj Computational Materials》2024年第1期1048-1059,共12页计算材料学(英文)

基　　金：supported by U.S.National Science Foundation under AI Institute for Dynamical Systems grant 2112085;supported by the U.S.Department of Energy,Office of Science,Office of Basic Energy Sciences(DOE-BES)undercontract No.DESC0024141.

摘　　要：Powder crystallography is the experimental science of determining the structure of molecules provided in crystalline-powder form,by analyzing their x-ray diffraction(XRD)patterns.Since many materials are readily available as crystalline powder,powder crystallography is of growing usefulness to many fields.However,powder crystallography does not have an analytically known solution,and therefore the structural inference typically involves a laborious process of iterative design,structural refinement,and domain knowledge of skilled experts.A key obstacle to fully automating the inference process computationally has been formulating the problem in an end-to-end quantitative form that is suitable for machine learning,while capturing the ambiguities around molecule orientation,symmetries,and reconstruction resolution.Here we present an ML approach for structure determination from powder diffraction data.It works by estimating the electron density in a unit cell using a variational coordinate-based deep neural network.We demonstrate the approach on computed powder x-ray diffraction(PXRD),along with partial chemical composition information,as input.When evaluated on theoretically simulated data for the cubic and trigonal crystal systems,the system achieves up to 93.4%average similarity(as measured by structural similarity index)with the ground truth on unseen materials,both with known and partially-known chemical composition information,showing great promise for successful structure solution even from degraded and incomplete input data.The approach does not presuppose a crystalline structure and the approach are readily extended to other situations such as nanomaterials and textured samples,paving the way to reconstruction of yet unresolved nanostructures.

关 键 词：STRUCTURE POWDER CRYSTALLINE 

分 类 号：TG1[金属学及工艺—金属学]