Recent advances and applications of deep learning methods in materials science

Deep learning(DL)is one of the fastest-growing topics in materials data science,with rapidly emerging applications spanning atomistic,image-based,spectral,and textual data modalities.DL allows analysis of unstructured data and automated identification of features.The recent development of large materials databases has fueled the application of DL methods in atomistic prediction in particular.In contrast,advances in image and spectral data have largely leveraged synthetic data enabled by high-quality forward models as well as by generative unsupervised DL methods.In this article,we present a high-level overview of deep learning methods followed by a detailed discussion of recent developments of deep learning in atomistic simulation,materials imaging,spectral analysis,and natural language processing.For each modality we discuss applications involving both theoretical and experimental data,typical modeling approaches with their strengths and limitations,and relevant publicly available software and datasets.We conclude the review with a discussion of recent cross-cutting work related to uncertainty quantification in this field and a brief perspective on limitations,challenges,and potential growth areas for DL methods in materials science.

Extracting structural motifs from pair distribution function data of nanostructures using explainable machine learning

Characterization of material structure with X-ray or neutron scattering using e.g.Pair Distribution Function(PDF)analysis most often rely on refining a structure model against an experimental dataset.However,identifying a suitable model is often a bottleneck.Recently,automated approaches have made it possible to test thousands of models for each dataset,but these methods are computationally expensive and analysing the output,i.e.extracting structural information from the resulting fits in a meaningful way,is challenging.Our Machine Learning based Motif Extractor(ML-MotEx)trains an ML algorithm on thousands of fits,and uses SHAP(SHapley Additive exPlanation)values to identify which model features are important for the fit quality.We use the method for 4 different chemical systems,including disordered nanomaterials and clusters.ML-MotEx opens for a type of modelling where each feature in a model is assigned an importance value for the fit quality based on explainable ML.