Innovations in Materials R&D via Experimentation-Computation Integration and Interdisciplinary Collaborations

This lecture will showcase selected examples of innovations in materials R&D through integration of innovative experimental techniques with computational results for accelerated research and insertion of new materials. On the experimental side, examples include earlier high-throughput experimental measurements of phase diagrams and materials properties to recent developments in ultrafast syntheses and autonomous experiments. On the computation side, examples include computational thermodynamic predictions, density functional theory calculations, and holistic approaches to integrate both experimental and computational data. These examples showcase the close integration of experimentation, modeling, and artificial intelligence (AI) for accelerated materials R&D to meet the increasing needs for advanced materials. They also illustrate the significance and impact of interdisciplinary collaborations in bringing new approaches to metals research.