Heather Kulik, Associate Professor of Chemical Engineering at the Massachusetts Institute of Technology, will deliver a seminar entitled, "Putting density functional theory to the test in machine-learning accelerated discovery for transition metal chemistry." Hosted by Mark Tuckerman.
For more information about Heather Kulik, click here.
Zoom Link: https://nyu.zoom.us/j/94377375456?pwd=aDlZZ0x3K1UvekI1ZjRhZmN5a0F0QT09
Abstract: Many compelling functional materials and highly selective catalysts have been discovered that are defined by their metal-organic bonding. The rational design of de novo transition metal complexes however remains challenging. First-principles (i.e., with density functional theory, or DFT) high-throughput screening is a promising approach but is hampered by high computational cost, particularly in the brute force screening of large numbers of ligand and metal combinations. In this talk, I will describe how automation, machine learning, autonomous tool development, sensitivity analysis, and multireference character detection can all be used to simultaneously overcome limitations in cost and accuracy of DFT-based screening in open shell transition metal chemistry. I will describe the basic approach of our software workflow, molSimplify, for automating calculations as well as recent extensions of machine learning (ML) models that avoid direct calculation of not just energetic or structural properties but also the likelihood of a calculation to succeed or a complex to contain strong correlation. I will describe how unifying databases shed light on the sensitivity of discovery outcomes to underlying DFT functionals. I will discuss possibilities for accelerating discovery with ML with awareness of uncertainty when sampling new regions of chemical space. Time permitting, I will describe how this powerful toolkit has advanced our understanding of metal-organic bonding in materials far-ranging from functional spin crossover complexes to open-shell transition metal catalysts and metal-organic frameworks.