Abstract Scope |
One of the biggest challenges facing industry is the identification of new materials to meet emerging industrial demands. However, gaps in advanced materials properties remain due to the intractable challenge of screening a vast material composition search space. To overcome this challenge, TOYOTA Research Institute (TRI) has developed an end-to-end platform that leverages artificial intelligence-based agents to explore high-dimensional search spaces for new materials. The computational autonomy for materials discovery (CAMD) system uses a combination of past knowledge, surrogate models and heuristic rules to promote cost-effective exploration-exploitation strategies that guide which experiments to perform next, aiding the pursuit of new stable compounds. In collaboration with Lucideon we present a complete workflow, from initial discovery campaign to materials synthesis, to demonstrate how new inorganic materials can be identified by CAMD, down selected for further analysis, experimentally validated using appropriate in-situ characterization techniques and subsequently, processed via solid state synthesis routes. |