In order to discover new aluminum- and magnesium-based bulk metallic glasses with superior glass-forming ability, the team will execute a dual-loop iterative materials design approach. A rapid materials design loop will provide high-throughput materials discovery by integrating experimental and simulated data with machine learning methods. An unprecedented body of experimental data on glass forming ability and basic mechanical properties will be generated by combinatorial 3D printing synthesis, followed by rapid optical, microscopy, thermal, and nanomechanical characterization. A similarly unique database of liquid and glass thermodynamic, kinetic, and structural properties will be determined by automated, high-throughput ab initio molecular dynamics. Machine-learning methods, trained on the data and physically motivated descriptors from existing experiments and the ab initio molecular dynamics simulation, will search a space of up to hundreds of thousands of potential alloys for the most promising candidates, which will then be synthesized, characterized and used to refine the models. Slower descriptor design loop studies will study select alloys in detail with fluctuation electron microscopy and extensive simulations to develop improved descriptors, which will then be incorporated into the rapid materials design loop and further validated by their predictive ability. This work will produce the first set of large-scale databases with both true measures of glass forming ability and extensive thermophysical data from simulations, and integrate them to generate physical descriptor driven machine-learning models for iterative new metallic glass search and discovery. The PIs also plan to release the MAterials Simulation Toolkit Machine Learning (MASTML) as open source and build a user community around the language by ensuring that interested researchers are able to contribute to the MASTML codebase. This will allow a wider growth of the project. This aspect is of special interest to the software cluster in the Office of Advanced Cyberinfrastructure, which has provided co-funding for this award.