AutoML-accelerated EELS/XAS as an advanced structure characterization tool

Atomistic structures of materials provide great insights of the functionality of the materials. Determining atomic structures is a fundamental problem in materials science. Although there are both experimental and computational methods to determine these nanoscale structures, they both possess limitations which demonstrate the difficulty of nanoscale structural determination, especially for systems with defects. We aim to tackle this problem and conduct structure characterization by combining ab initio simulations, experimental acquisition, and machine learning (ML) techniques. While ML methods have been widely applied in materials characterization, these frameworks crucially rely on human machine learning experts to perform manual tasks. In this work, we seek to predict the local structures and properties from real spectroscopic data (EELS/XAS) in an automated fashion based on multi-fidelity Bayesian optimization, which includes the conversion between simulation data to and experimental data, feature engineering, hyperparameter tuning and ML model selection. For the material system, we use a variety of lithium nickel manganese cobalt oxides (NMC) compounds as the test cases, including those with oxygen vacancies and antisite defects. We will demonstrate that our framework can not only accurately predict the target information, but also provide the interpretability which quantitatively bridges the spectroscopy with the local atomic and electronic structures around the adsorbing atom.