Content Library

Discovering novel materials can be greatly accelerated by iterative machine learning-informed proposal of candidates—active learning. However, standard global error metrics for model quality are not predictive of discovery performance,...

The Data-Driven Material Science & Chemistry newsletter provides weekly scholarly and community news and updates on data science and physical science.

Advances in renewable and sustainable energy technologies critically depend on our ability to design and realize materials with optimal properties. Materials discovery and design efforts ideally involve close coupling...

Ongoing, rapid innovations in fields ranging from microelectronics, aerospace, and automotive to defense, energy, and health demand new advanced materials at even greater rates and lower costs. Traditional materials...

Traditional machine learning (ML) metrics overestimate model performance for materials discovery. We introduce (1) leave-one-cluster-out cross-validation (LOCO CV) and (2) a simple nearest-neighbor benchmark to show that model performance...

Data-driven materials research requires two key supporting components: data infrastructure and informatics. In this chapter, we review the state of the art in materials data infrastructure, focusing in detail...

In this paper, we describe and compare three techniques for transfer learning: multi-task, difference, and explicit latent variable architectures.

This paper explores the use of convolutional neural networks for classifying microstructure.