CITRINE RESEARCH COMMUNITY

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...

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...

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...