Jeffrey Cheah Career Development Chair and Associate Professor of Materials Science and Engineering, Department of Materials Science and Engineering
Rafael Gomez-Bombarelli is the Associate Professor of Materials Science and Engineering and the Jeffrey Cheah Career Development Chair in MIT’s Department of Materials Science and Engineering. Gomez-Bombarelli is interested in fusing machine learning and atomistic simulations for designing materials and their transformations. His group works across molecular, crystalline and polymer matter, combining novel computational tools in optimization, inverse design, surrogate modeling and active learning with simulation approaches like quantum chemistry and molecular dynamics. Through collaborations at MIT and beyond, his group helps develop new practical materials such as therapeutic peptides, organic electronics for displays, electrolytes for batteries, and oxides for sustainably catalysis. Gomez-Bombarelli’s work has been featured in the MIT Technology Review and Wall Street Journal. He also co-founded Calculario, a materials discovery company that leverages quantum chemistry and machine learning to target advanced materials in high-value markets. He earned a BS, MS, and PhD in chemistry from Universidad de Salamanca, followed by postdoctoral work at Heriot-Watt University and Harvard University.
- Axelrod, S., Shakhnovich, E. & Gómez-Bombarelli, R. Excited state non-adiabatic dynamics of large photoswitchable molecules using a chemically transferable machine learning potential. Nat Commun 13, 3440 (2022). https://doi.org/10.1038/s41467-022-30999-w
- Axelrod, S., Gómez-Bombarelli, R. GEOM, energy-annotated molecular conformations for property prediction and molecular generation. Sci Data 9, 185 (2022). https://doi.org/10.1038/s41597-022-01288-4
- Schissel, C. K. et al. (2020). Interpretable Deep Learning for De Novo Design of Cell-Penetrating Abiotic Polymers. bioRxiv 2020.04.10.036566
- Wang, W., Axelrod, S., Gómez-Bombarelli, R. (2020). Differentiable Molecular Simulations for Control and Learning. arXiv:2003.00868 [physics.comp-ph]
- Schwalbe-Koda, D., Jensen, Z., Olivetti, E., Gómez-Bombarelli, R. (2019). Graph similarity drives zeolite diffusionless transformations and intergrowth. Nature Materials 18, 1177–1181.
- Coarse-graining auto-encoders for molecular dynamics. NPJ Computational Materials. 5(1):125. . (2019).
- Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules. ACS Central Science. 4(2):268 – 276. . (2018).
- November 5, 2019: MIT News, Materials Day talks examine the promises and challenges of AI and machine learning.
- October 7, 2019: MIT News, A new mathematical approach to understanding zeolites.
- February 21, 2019: MIT News, Exploring the nature of intelligence.
- December 14, 2018: MIT News, Opportunities for materials innovation abound.