Decoding defect statistics from diffractograms via machine learning  

作　　者：Cody Kunka Apaar Shanker Elton Y.Chen Surya R.Kalidindi Rémi Dingreville 

机构地区：[1]Center for Integrated Nanotechnologies,Sandia National Laboratories,Albuquerque,NM,USA.2Georgia Institute of Technology,Atlanta,GA,USA

出　　处：《npj Computational Materials》2021年第1期601-609,共9页计算材料学（英文）

基　　金：This material is based upon work supported by the U.S.Department of Energy,Office of Science,Office of Basic Energy Sciences,Data,Artificial Intelligence and Machine Learning at DOE Scientific User Facilities program under Award Number 34532.A.S.9;S.R.K.acknowledge funding from ONR award N00014-18-1-2879.

摘　　要：Diffraction techniques can powerfully and nondestructively probe materials while maintaining high resolution in both space and time.Unfortunately,these characterizations have been limited and sometimes even erroneous due to the difficulty of decoding the desired material information from features of the diffractograms.Currently,these features are identified non-comprehensively via human intuition,so the resulting models can only predict a subset of the available structural information.In the present work we show(i)how to compute machine-identified features that fully summarize a diffractogram and(ii)how to employ machine learning to reliably connect these features to an expanded set of structural statistics.To exemplify this framework,we assessed virtual electron diffractograms generated from atomistic simulations of irradiated copper.When based on machine-identified features rather than human-identified features,our machine-learning model not only predicted one-point statistics(i.e.density)but also a two-point statistic(i.e.spatial distribution)of the defect population.Hence,this work demonstrates that machine-learning models that input machine-identified features significantly advance the state of the art for accurately and robustly decoding diffractograms.

关 键 词：diff LEARNING DEFECT 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] TP39[自动化与计算机技术—控制科学与工程]