Student Hosted Colloquia Seminar: Professor Heather Kulik, MIT
About the Seminar
Leveraging experimental data in machine learning models to accelerate the discovery of new materials
I will discuss our efforts to use machine learning (ML) to accelerate the computational tailoring and design of complex materials by leveraging experimental datasets. First, I will discuss metal-organic framework (MOF) materials and their application for catalysis as well as gas separations and storage. One limitation in a challenging materials space such as open shell transition metal chemistry present in the open metal sites of most catalytically active MOFs is that ML models and ML-accelerated high-throughput screening traditionally rely on density functional theory (DFT) for data generation, but DFT is both computationally demanding and prone to errors that limit its accuracy in predicting new MOFs. I will describe how we have curated a dataset of thousands of MOFs that have been experimentally synthesized and used this data to train ML models to predict experimentally reported measures of stability. These models are able to predict experimental thermal, activation, water, and acid/base stability, which would be extremely difficult to predict using computational modeling. I will describe how we have leveraged these models to then screen for mechanically stable materials as well as stable catalysts in the direct conversion of methane to methanol. I will also describe how we have used these models to accelerate the discovery of novel stable MOFs, creating a dataset of transition metal complexes enriched with stability and diversity 1-2 orders of magnitude beyond what is typically included in most hypothetical MOF datasets. In the second half of my talk, I will discuss ways we have leveraged experimental data to build models on smaller data sets of molecular properties. Specifically, I will describe how we have aimed to develop a tool to design novel mechanophore constituents to mechanochemically reactive polymers. We have built an intuitive physical organic model that captures C–C bond reactivities under tensile force, by leveraging easy-to-compute molecular features in terms of force constants and reaction energies. I will describe how this model can accurately predict experimental transition forces. Finally, time-permitting, I will describe models and descriptors we have built for photochemical properties of transition metal complexes that are attractive design targets but challenging to compute with traditional modeling techniques.
About the Speaker
Professor Heather J. Kulik is a tenured Professor in the Departments of Chemical Engineering and Chemistry at MIT. She received her B.E. in Chemical Engineering from the Cooper Union in 2004 and her Ph.D. from the Department of Materials Science and Engineering at MIT in 2009. She completed postdoctoral training at Lawrence Livermore and Stanford, prior to joining MIT as a faculty member in November 2013. Her research has been recognized by an Office of Naval Research Young Investigator Award, DARPA Young Faculty Award and Director’s fellowship, NSF CAREER Award, a Sloan Fellowship in chemistry, an AIChE Computational and Molecular Simulation Engineering Forum Impact Award, and a Hans Fischer Senior Fellowship from the Technical University of Munich, among others.