Poster
in
Workshop: AI for Accelerated Materials Design (AI4Mat)
Extracting Structural Motifs from Pair Distribution Function Data of Nanostructures using Explainable Machine Learning
Andy S. Anker · Emil Thyge Skaaning Kjær · Mikkel Juelsholt · Troels Christiansen · Susanne Skjærvø · Mads Jørgensen · Innokenty Kantor · Daniel Sørensen · Simon J. L. Billinge · Raghavendra Selvan · Kirsten Jensen
Keywords: [ Explainable Machine Learning ] [ Materials Chemistry ] [ X-ray scattering ] [ Nanoparticle characterization ] [ Pair Distribution Function ]
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, new 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 new type of modelling where each feature in a model is assigned an importance value for the fit quality based on explainable ML.