| Abstract Scope |
Traditional material discovery and development typically relies on an Edisonian trial-and-error approach, resulting in sporadic and infrequent advancements. This research aims to leverage machine learning (ML) to enhance the material development process, particularly by employing computational techniques to investigate complex multifactorial experiments, alongside data collection and analysis. We will showcase high-throughput material science methods applied to the development of complex concentrated alloys (CCAs). By utilizing computational modeling and automating processes, the discovery, synthesis, characterization, and testing of materials can be significantly expedited. |