Letting Atomic Simulations Learn From Phase Diagrams

University of Michigan researchers have developed a new machine learning method that links atomic simulations with phase diagrams, improving the efficiency of materials prediction. By training models directly on experimental data, the approach could strengthen how scientists design materials for extreme environments such as turbine engines. Read the full article to learn how this work may lead to the discovery and use of new materials.