PICS Seminar: “Fusing machine learning and atomistic simulations for materials design”
October 2 at 2:00 PM - 3:00 PM
Data-driven approaches match or outperform humans at a number of tasks, including pattern recognition in images and text or planning and strategy in rule-based games. The application of machine learning techniques is also promising for accelerating materials design. However, experimental data for training is typically scarce and sparse. The interplay between physics-based simulations and data-driven models is particularly advantageous. It allows relying on transferable laws rather than only fitting data in a black box fashion. Meanwhile, learning from data
provides a unique opportunity to parameterize and augment physics-based models, or completely replace them.
Models can be built that map the structure and composition of materials to their properties. With such models, it is
then possible to rapidly screen libraries of candidate materials for a desired application before going to the lab. Generative models go one step further and allow tackling the inverse problem: given the desired property, automatically suggesting a new optimal material that achieves it.
How to represent matter so that it can be read into or written by a computer program is key for these coupled tasks of property prediction and materials optimization. Strategies are needed to represent materials in a machine-readable way that is data-efficient, expressive, respectful of physical invariants and, ideally, invertible.
Here, we will discuss our current efforts in building bottom-up atom-level representations for materials design. These include variational autoencoders for dimensionality reduction and inverse design in molecules and polymers,
representation and unsupervised learning for graphs and sequences in crystals and polymers, generative models to
accelerate Monte Carlo simulations of alloy phase diagrams or end-to-end differentiable simulations.
Toyota Career Development Assistant Professor, Department of Materials Science and Engineering, Massachusetts Institute of Technology.
Rafael Gomez-Bombarelli joined the MIT faculty in January 2018. He received a B.S., M.S., and Ph.D. in Chemistry from Universidad de Salamanca in Spain, followed by postdoctoral work at Heriot-Watt University and Harvard University after which he was a senior researcher at Kyulux NA applying Harvard-licensed technology to create real-life commercial organic light-emitting diode (OLED) products.
Dr. Gomez-Bombarelli’s research trajectory has evolved from experimental mechanistic studies of organic molecules with emphasis on environmental toxicity to computer-driven design of molecular materials. By combining first-principles simulation with machine learning on theoretical and experimental datasets he aims to accelerate the discovery cycle of novel practical materials.
Through his research at MIT he plans to address the role of molecular transformation in materials discovery, in areas such as catalyst design, the environmentally-minded development of novel and replacement chemicals, and designing for stability in advanced materials.
Rafa’s work has been featured in journals such as Technology Review and the Wall Street Journal. He was also co-founder of Calculario, a materials discovery company that leverages quantum chemistry and machine learning to target advanced materials in a range of high-value markets.