Generating and leveraging explanations of AI/ML models in materials and manufacturing research.

Abstract

In some technical domains, machine learning (ML) tools, typically used with large datasets, must be adapted to small datasets, opaque design spaces, and expensive data generation. Specifically, generating data in many materials or manufacturing contexts can be expensive in time, materials, and expertise. Additionally, the "thought process" of complex "black box" ML models is often obscure to key stakeholders. This limitation can result in inefficient or dangerous predictions when errors in data processing or model training go unnoticed. Methods of generating human-interpretable explanations of complex models, called explainable artificial intelligence (XAI), can provide the insight needed to prevent these problems. In this review, we briefly present XAI methods and outline how XAI can also inform future behavior. These examples illustrate how XAI can improve manufacturing output, physical understanding, and feature engineering. We present guidance on using XAI in materials science and manufacturing research with the aid of demonstrative examples from literature.