Interpretable and Explainable Machine Learning for Materials Science and Chemistry

Abstract

Conspectus Machine learning has become a common and powerful tool in materials research. As more data become available, with the use of high-performance computing and high-throughput experimentation, machine learning has proven potential to accelerate scientific research and technology development. Though the uptake of data-driven approaches for materials science is at an exciting, early stage, to realize the true potential of machine learning models for successful scientific discovery, they must have qualities beyond purely predictive power. The predictions and inner workings of models should provide a certain degree of explainability by human experts, permitting the identification of potential model issues or limitations, building trust in model predictions, and unveiling unexpected correlations that may lead to scientific insights. In this work, we summarize applications of interpretability and explainability techniques for materials science and chemistry and discuss how these techniques can improve the outcome of scientific studies. We start by defining the fundamental concepts of interpretability and explainability in machine learning and making them less abstract by providing examples in the field. We show how interpretability in scientific machine learning has additional constraints compared to general applications. Building upon formal definitions in machine learning, we formulate the basic trade-offs among the explainability, completeness, and scientific validity of model explanations in scientific problems. In the context of these trade-offs, we discuss how interpretable models can be constructed, what insights they provide, and what drawbacks they have. We present numerous examples of the application of interpretable machine learning in a variety of experimental and simulation studies, encompassing first-principles calculations, physicochemical characterization, materials development, and integration into complex systems. We discuss the varied impacts and uses of interpretabiltiy in these cases according to the nature and constraints of the scientific study of interest. We discuss various challenges for interpretable machine learning in materials science and, more broadly, in scientific settings. In particular, we emphasize the risks of inferring causation or reaching generalization by purely interpreting machine learning models and the need for uncertainty estimates for model explanations. Finally, we showcase a number of exciting developments in other fields that could benefit interpretability in material science problems. Adding interpretability to a machine learning model often requires no more technical know-how than building the model itself. By providing concrete examples of studies (many with associated open source code and data), we hope that this Account will encourage all practitioners of machine learning in materials science to look deeper into their models.

High-throughput computational screening of nanoporous materials in targeted applications

“…The descriptors exhaustively describe the physicochemical properties, while the ML algorithms set rules for learning patterns in the data. At the end, the ML model needs to be predictive while maintaining a high level of interpretability 68 and reproducibility. 69 To illustrate this approach, a few studies of such ML-assisted high-throughput screenings and their particular contributions to the eld are presented below.…”

“…The laws of physics are not explicitly included in an ML model, interpretability and exploitability methods can help cover these aws by identifying potential nonphysical behaviours, or conrming its consistency in describing known physical behaviours, or unveiling unexpected scientic insights. 68 If the model fails to meet some standards, further developments are needed for the descriptors to contain all relevant information, or to draw a more consistent relationship between the descriptors and the desired metric. Without a well-designed (containing all physical information) set of descriptors, an ML approach cannot make reliable predictions.…”

Elucidating Precipitation in FeCrAl Alloys through Explainable AI: A Case Study

“…There are two major classes of approaches in XAI: Intrinsic and Extrinsic. Intrinsic approaches provide explainability and interpretability as an inherit part of the models [39]. Extrinsic approaches rely on external methods built upon queries of the trained models to provide insight on the working of the model.…”

Explainable machine learning in materials science

“…Some XAI techniques are also being applied in materials ML studies [20][21][22] but XAI as a comprehensive field remains unfamiliar to the mainstream materials science community. Oviedo et al 23 recently presented a materials science and chemistry XAI review, but their examples are heavy on chemistry and model evaluation is not discussed in detail. Here we present a systematic review of important XAI concepts and useful techniques with representative materials science application examples.…”