About
Prof. Tonio Buonassisi combines machine learning and high-throughput experiments to create new materials & systems with societally beneficial applications. His early-career research in solar energy and technoeconomic analysis assisted dozens of companies. In 2018 he served as founding director of the Accelerated Materials Development for Manufacturing (AMDM) programme in Singapore, a S$24.7M effort to accelerate the rate of novel materials development by >10x. He co-founded Xinterra, which is accelerating the development of carbon-capture materials. He returned to MIT full time in December 2021, where he leads the Accelerated Materials Laboratory for Sustainability. He is director of the ADDEPT Center, focused on developing durable perovskite-silicon tandem modules with industry and academic partners.
Awards
- Presidential Early Career Award for Scientists and Engineers (PECASE), 2016
- Everett Moore Baker Memorial Award for Excellence in Undergraduate Teaching, MIT, 2015
- Google Faculty Award, 2015
- NSF CAREER Award, 2012
Relevant Publications
- Hippalgaonkar, K., Li, Q., Wang, X., Fisher III, J.W., Kirkpatrick, J., Buonassisi, T. (2023). Knowledge-integrated machine learning for materials: lessons from gameplaying and robotics. Nature Reviews Materials, 8(4), 241-260. https://doi.org/10.1038/s41578-022-00513-1
- Sun, S., Tiihonen, A., Oviedo, F., Liu, Z., Thapa, J., Zhao, Y., Hartono, N.T.P., Goyal, A., Heumueller, T., Batali, C., Encinas, A., Yoo, J.J., Li, R., Ren, Z., Peters, I.M., Brabec, C.J., Bawendi, M.G., Stevanovic, V., Fisher, J., Buonassisi, T. (2021). A data fusion approach to optimize compositional stability of halide perovskites. Matter, 4(4), 1305-1322. https://doi.org/10.1016/j.matt.2021.01.008
- Sun, S., Hartono, N.T.P., Ren, Z.D., Oviedo, F. Buscemi, A.M., Layurova, M., Chen, D.X., Ogunfunmi, T., Thapa, J., Ramasamy, S., Settens, C., DeCost, B.L., Kusne, A.G., Liu, Z., Tian, S.I.P., Peters, I.M., Correa-Baena, J.-P., Buonassisi T. (2019). “Accelerated development of perovskite-inspired materials via high-throughput synthesis and machine-learning diagnosis,” Joule 3(6), 1437-1451.