ChemBE Seminar: Heather Kulik

Description

Heather Kulik, an associate professor in the Departments of Chemical Engineering and Chemistry at the Massachusetts Institute of Technology (MIT), will give a talk titled "Leveraging Community Knowledge in Machine Learning Models to Accelerate the Discovery of New Catalysts and Materials" for the Department of Chemical and Biomolecular Engineering.

Abstract:

I will discuss our efforts to use machine learning (ML) to accelerate the computational tailoring and design of metal-organic framework (MOF) materials for catalysis. 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 properties of 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 stability, activation, and water stability, which would be extremely difficult to predict using computational modeling. I will describe how we have leveraged these models to then screen for 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. Time permitting, I will also describe how we have sought to overcome limitations in DFT in the discovery of novel transition metal complexes with light harvesting properties and spin crossover behavior.

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