DeepXRD, a Deep Learning Model for Predicting XRD spectrum from Material Composition

Abstract

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 prediction. Benchmark studies on two data sets 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 materials discovery.

A Deep Learning Approach to Powder X‐Ray Diffraction Pattern Analysis: Addressing Generalizability and Perturbation Issues Simultaneously

“…FCN has been very recently employed for DL-driven XRD analysis, [26] whereas conventional CNNs, including fully connected layers (FCLs), have been more widely used for XRD analysis. [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29] The FCL in the CNN has a disadvantage in that image (or peak) location information is extinguished, that is, the receptive field concept disappears after the FCL. [47] The FCN is constituted by eliminating the FCLs from the conventional CNN.…”

“…[1][2][3][4][5][6][7][8][9][10][11][12][13] In particular, special attention has been focused on the deep learning (DL)-driven X-ray diffraction (XRD) analysis that has been realized in various specific material systems. [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29] Of course, these types of DL-driven XRD approaches are, in fact, inspired by a number of prior successful machine learning (ML)-based XRD analyses. [30][31][32][33][34][35] On this ground, data-driven (DL-driven) approaches for XRD analysis deserve to be equally spotlighted in parallel with conventional knowledgebased, expertise-dependent XRD analysis.…”

“…[24,26,[41][42][43] This 1D XRD imperfection still applies for the DL-based XRD analysis case. Nonetheless, there has recently been a growing number of DL-driven 1D powder XRD analysis studies for phase identification (demixing), [14][15][16][17][18][19][20] symmetry classification, [21][22][23][24][25][26] unsupervised clustering and XRD generation, [26][27][28][29] etc. Almost all the previously reported DL models for XRD analysis demonstrated outstanding performances for their own specific material systems.…”

“…real-world and synthetic XRD patterns because all the DL approaches reported thus far have incorporated synthetic XRD patterns for model training due to the dearth of available real world experimental XRD data. [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29] The perturbation that originates from several well-known artifacts, such as strain-induced peak jittering (or shift), particle size-induced peak broadening, preferred orientation-induced peak intensity variation (texture), sample-or apparatus-induced background and noise, etc., matters when constructing synthetic XRD patterns for use in DL model training. There have been several methodologies to take this sort of perturbation into consideration for XRD analysis.…”

Introduction to Materials Informatics

“…In parallel, there is a growing number of important studies involving machine learning applied to experimentally derived data, and/or simulation or prediction of experimental observables. 1,2 When working with experimental data and/or the simulation of experimental data, one must account for the nature of precision and accuracy in the measurement itself (e.g., specimen geometry, instrumental parameters, and the conditions of observation). 3 Incorporating all these factors can make evaluation and interpretation of spectroscopic profiles based on human-identifiable peaks difficult and convoluted.…”