About the event
The Gene and Linda Voiland School of Chemical Engineering and Bioengineering is hosting a seminar presented by Heather Kulik, Assistant Professor, Department of Chemical Engineering, Massachusetts Institute of Technology.
Professor Heather J. Kulik is an Assistant Professor in the Department of Chemical Engineering at MIT. She received her B.E. in Chemical Engineering from the Cooper Union for the Advancement of Science and Art in 2004 and her Ph.D. from the Department of Materials Science and Engineering at MIT in the group of Nicola Marzari in 2009. She completed postdoctoral training in the group of Felice Lightstone at Lawrence Livermore (2010) and Todd J. Martı́nez at Stanford (2010−2013), prior to joining MIT as a faculty member in November 2013. Her research in accelerating computational modeling in inorganic chemistry and catalysis has been recognized by a Burroughs Wellcome Fund Career Award at the Scientific Interface, Office of Naval Research Young Investigator Award, DARPA Young Faculty Award, and the AAAS Marion Milligan Mason Award, among other awards.
Designing in the face of uncertainty: exploiting electronic structure and machine learning models for discovery in transition metal chemistry
Recent transformative advances in computing power and algorithms have made computational chemistry central to the discovery and design of new molecules and materials. First-principles simulations are increasingly accurate and applicable to large systems with the speed needed for high-throughput computational screening. Despite these strides, the combinatorial challenges associated with the vastness of chemical space mean that more than just fast and accurate computational tools are needed for accelerated chemical discovery. In transition metal chemistry and catalysis, unique challenges arise. The variable spin, oxidation state, and coordination environments favored by elements with well-localized d or f electrons provide great opportunity for tailoring properties in catalytic or functional (e.g., magnetic) materials but also add layers of uncertainty to any design strategy. I’ll outline five key mandates for realizing computationally-driven accelerated discovery in inorganic chemistry: i) fully automated simulation of new compounds, ii) faster-than-fast property prediction methods, iii) maps for rapid chemical space traversal, iv) a means to reveal design rules on the kilocompound scale, and v) knowledge of both simulation prediction sensitivity and machine learning model uncertainty. Through case studies in materials synthesis, functional spin crossover materials, and methane-to-methanol conversion, I will describe how advances in methodology and software in each of these areas brings about new chemical insights. I’ll conclude with the outlook on the next steps in this process toward realizing fully autonomous discovery in inorganic chemistry using computational chemistry.