Machine learning for materials discovery and optimization

Discovering new materials with customizable and optimized properties, driven either by specific application needs or by fundamental scientific interest, is a primary goal of materials science. Conventionally, the search for new materials is a lengthy and expensive manual process, frequently based on trial and error, requiring the synthesis and characterization of multiple compositions before a desired material can be found. In recent years this process has been greatly improved by high-throughput approaches. Advances in artificial intelligence, such as machine learning for materials science, data-driven materials prediction, automated or autonomous combinatorial synthesis, and data-guided high-throughput characterization, can now significantly accelerate materials discovery.

This collection brings together the latest computational and experimental advances in machine learning and big data-driven approaches for high-throughput prediction, synthesis, characterization and optimization of new materials.

Artist's impression of a neuron map


Reviews and Perspectives

Materials synthesis

Materials design and characterization

Automated and autonomous experimentation