Beyond Combinatorial Materials Science: The 100 Prisoners Problem

Abstract

Advancements in high-throughput data generation and physics-informed artificial intelligence and machine-learning algorithms are rapidly challenging the status quo for how materials data is collected, analyzed, and communicated with the world. Machine-learning algorithms can be executed in just a few lines of code by researchers with minimal data science expertise. This perspective addresses the reality that the ecosystems which have been constructed to nurture new materials discovery and development are not yet well equipped to take advantage of the radically more powerful and accessible computational and algorithmic tools which have the immediate potential to enhance the pace of scientific advancement in this field. A novel architecture for managing materials data is proposed and discussed from the standpoint of how historical and emerging subfields of materials science could have been or might still significantly improve the impact of materials discoveries to the many human societal needs for new materials.