Revealing the Evolution of Order in Materials Microstructures Using Multi-Modal Computer Vision

TL;DR

This paper introduces a multi-modal machine learning approach to analyze and describe microstructural order in complex oxide materials using electron microscopy data, improving understanding of material properties.

Contribution

The study presents a hybrid ML pipeline combining fully and semi-supervised classification to evaluate multi-modal data for describing crystal order.

Findings

Multi-modal models outperform uni-modal models in describing crystal order.

Distinct differences observed in the performance of uni- and multi-modal models.

Lessons learned on the effectiveness of combining data modalities for material analysis.

Abstract

The development of high-performance materials for microelectronics, energy storage, and extreme environments depends on our ability to describe and direct property-defining microstructural order. Our present understanding is typically derived from laborious manual analysis of imaging and spectroscopy data, which is difficult to scale, challenging to reproduce, and lacks the ability to reveal latent associations needed for mechanistic models. Here, we demonstrate a multi-modal machine learning (ML) approach to describe order from electron microscopy analysis of the complex oxide La$_{1-x}$Sr$_x$FeO$_3$. We construct a hybrid pipeline based on fully and semi-supervised classification, allowing us to evaluate both the characteristics of each data modality and the value each modality adds to the ensemble. We observe distinct differences in the performance of uni- and multi-modal models, from which we draw general lessons in describing crystal order using computer vision.