摘要
X-ray diffraction(XRD)data acquisition and analysis is among the most time-consuming steps in the development cycle of novel thin-film materials.We propose a machine learning-enabled approach to predict crystallographic dimensionality and space group from a limited number of thin-film XRD patterns.We overcome the scarce data problem intrinsic to novel materials development by coupling a supervised machine learning approach with a model-agnostic,physics-informed data augmentation strategy using simulated data from the Inorganic Crystal Structure Database(ICSD)and experimental data.As a test case,115 thin-film metalhalides spanning three dimensionalities and seven space groups are synthesized and classified.After testing various algorithms,we develop and implement an all convolutional neural network,with cross-validated accuracies for dimensionality and space group classification of 93 and 89%,respectively.We propose average class activation maps,computed from a global average pooling layer,to allow high model interpretability by human experimentalists,elucidating the root causes of misclassification.Finally,we systematically evaluate the maximum XRD pattern step size(data acquisition rate)before loss of predictive accuracy occurs,and determine it to be 0.16°2θ,which enables an XRD pattern to be obtained and classified in 5.5 min or less.
基金
This work was supported by a TOTAL SA research grant funded through MITei(supporting the experimental XRD),the National Research Foundation(NRF),Singapore through the Singapore Massachusetts Institute of Technology(MIT)Alliance for Research and Technology’s Low Energy Electronic Systems research program(supporting the machine learning algorithm development),the Accelerated Materials Development for Manufacturing Program at A*STAR via the AME Programmatic Fund by the Agency for Science,Technology and Research under Grant No.A1898b0043(for ML algorithm error analysis)
by the U.S.Department of Energy under the Photovoltaic Research and Development program under Award DE-EE0007535(for code framework development)
This work made use of the CMSE at MIT,which is supported by NSF award DMR-0819762.
