Revealing Local Structures through Machine-Learning- Fused Multimodal Spectroscopy

TL;DR

This paper presents a machine learning framework that integrates multimodal spectroscopy data to accurately determine local atomic structures and defects in materials, surpassing limitations of single data stream methods.

Contribution

The work introduces a novel multimodal spectroscopy and ML approach for detailed local structure and defect analysis in materials, especially for complex systems like battery cathodes.

Findings

Successfully inferred local element content with quantitative agreement to experiments.

Determined presence of local defects like oxygen vacancies and antisites, impossible with single-mode spectra.

Provided physical interpretability linking spectroscopy data to atomic structures.

Abstract

Atomistic structures of materials offer valuable insights into their functionality. Determining these structures remains a fundamental challenge in materials science, especially for systems with defects. While both experimental and computational methods exist, each has limitations in resolving nanoscale structures. Core-level spectroscopies, such as x-ray absorption (XAS) or electron energy-loss spectroscopies (EELS), have been used to determine the local bonding environment and structure of materials. Recently, machine learning (ML) methods have been applied to extract structural and bonding information from XAS/EELS, but most of these frameworks rely on a single data stream, which is often insufficient. In this work, we address this challenge by integrating multimodal ab initio simulations, experimental data acquisition, and ML techniques for structure characterization. Our goal is to determine local structures and properties using EELS and XAS data from multiple elements and edges. To showcase our approach, we use various lithium nickel manganese cobalt (NMC) oxide compounds which are used for lithium ion batteries, including those with oxygen vacancies and antisite defects, as the sample material system. We successfully inferred local element content, ranging from lithium to transition metals, with quantitative agreement with experimental data. Beyond improving prediction accuracy, we find that ML model based on multimodal spectroscopic data is able to determine whether local defects such as oxygen vacancy and antisites are present, a task which is impossible for single mode spectra or other experimental techniques. Furthermore, our framework is able to provide physical interpretability, bridging spectroscopy with the local atomic and electronic structures.