Dr. Rafael Gomez-Bombarelli, Department of Materials Science and Engineering, MIT

Host: Dr. Potoyan

Designing new materials is vital for addressing pressing societal challenges in health, energy, and sustainability. The computational techniques of atomistic simulation and machine learning (ML) offer an avenue to rapidly invent new materials and navigate this enormous space. By populating the continuum between physics-based simulations and machine learning, the Learning Matter Lab seeks to enable rapid, computation-first design of materials that accelerate the materials discovery cycle.

Atomistic simulations, using techniques from quantum mechanics or statistical mechanics can predict the properties of hypothetical materials, and by engineering high-throughput simulation pipelines, Gomez-Bombarelli can evaluate millions of candidates and find compositions or structures that optimize a given property. Simulations are, nevertheless, relatively costly, and may lack accuracy compared to experiment. This is where the synergy with ML enables a new paradigm: surrogate models bypass simulations by interpolating among pre-existing calculations at a fraction of the cost, while embedding physics-based priors in ML ensures robustness and transferability.

We will present our current progress in enabling end-to-end materials design for multiple materials classes and applications, from heterogeneous nanoporous catalysts to polymer electrolytes for batteries or therapeutic peptide macromolecules.

Rafael Gomez-Bombarelli (Rafa) is the Jeffrey Cheah Career Development Professor at MIT’s Department of Materials Science and Engineering since 2018. His works aims to fuse machine learning and atomistic simulations for designing materials and their transformations. Through collaborations at MIT and beyond, they develop new practical materials such as heterogeneous thermal catalysts (zeolites), transition metal oxide electrocatalysts, therapeutic peptides, organic electronics for displays, electrolytes for batteries. By embedding domain expertise and experimental results into their models, alongside physics-based knowledge, the Learning Matter Lab designs materials than can be realized in the lab and scaled to practical applications.

Rafa received BS, MS, and PhD (2011) degrees in chemistry from Universidad de Salamanca (Spain), followed by postdoctoral work at Heriot-Watt (UK) and Harvard Universities, and a stint in industry at Kyulux North America. He has been awarded the Camille and Henry Dreyfus Foundation "Machine Learning in the Chemical Sciences and Engineering Awards" in 2021 and the Google Faculty Research Award in 2019. He was co-founder of Calculario a Harvard spinout company, was Chief Learning Officer of ZebiAI, a drug discovery startup acquired by Relay Therapeutics in 2022 and serves as consultant and scientific advisor to multiple startups.