ABOUT THE PROJECT
Advances in computational materials design have enabled rapid screening for desirable properties of both real and virtual compounds. However, the pace of commercially-realized advanced materials may now be limited by trial-and-error synthesis techniques.
The goal of this research project is to advance computational learning around materials synthesis approaches by creating a predictive synthesis system for advanced materials design and processing — to do for materials synthesis what modern computational methods have done for materials properties.
This predictive synthesis system has been applied to several materials science domains including metallic oxides, titania, perovskites, zeolites, metal-insulator transition materials, solid-state electrolytes, alloys, and alternative cements.
This project is closely aligned with the Materials Genome Initiative (MGI) for Global Competitiveness, a multi-agency effort for deploying advanced materials. We are supported by funding from the National Science Foundation Award DMREF Awards #1922311, #1922372, and #1922090, the Office of Naval Research (ONR) under Contract No. N00014-20-1-2280, and the MIT Energy Initiative. Early work on the project was also collaborative under the Department of Energy’s Basic Energy Science Program through the Materials Project under Grant No. EDCBEE.
More information about the project and related research can be found at http://olivetti.mit.edu/.