Electrical engineering

Machine learning

Data mining

Engineering

Variety (cybernetics)

Public health

Health informatics

Artificial intelligence

Engineering informatics

Nursing

Informatics

Core (optical fiber)

Data science

Common core

Computer science

Materials informatics

Medicine

Telecommunications

Annual Review of Materials Research

The rapidly growing interest in machine learning (ML) for materials discovery has resulted in a large body of published work. However, only a small fraction of these publications includes confirmation of ML predictions, either via experiment or via physics-based simulations. In this review, we first identify the core components common to materials informatics discovery pipelines, such as training data, choice of ML algorithm, and measurement of model performance. Then we discuss some prominent examples of validated ML-driven materials discovery across a wide variety of materials classes, with special attention to methodological considerations and advances. Across these case studies, we identify several common themes, such as the use of domain knowledge to inform ML models.

Machine Learning in Materials Discovery: Confirmed Predictions and Their Underlying Approaches