Enabling Quantum Chemical Studies of Reactive MoleculesHigh-throughput density functional theory (DFT) studies of molecules have traditionally been limited to relatively stable species - neutral, closed-shell species, often in gas phase. However, reactions in electrochemical systems often involve radical, charged, and metal-coordinated species in a solvent environment. To facilitate studies of such complex and reactive systems, I have been developing methods to automate calculations of reactive ground-state molecules as well as reaction energy barriers (based on transition-state theory and Marcus theory), allowing for the accurate prediction of reaction thermodynamics and kinetics from first-principles.
Using the workflows that I have devised and implemented, it is possible to generate large datasets containing the properties of reactive molecules. Such datasets have already been used by myself and my colleagues to train neural networks, for instance for the prediction of bond dissociation energies.
I am working to develop similar datasets of reaction pathways, with the eventual goal of machine learning energy barriers for arbitrary (electro)chemical reactions with DFT-level accuracy.
Developing a New Approach to Exploring ReactivityUnderstanding and controlling chemical reactivity is key to a range of technological applications, from manufacturing to transportation and electronics. Typical theoretical studies of reactivity involve low-throughput molecular simulations, using some combination of DFT and reactive or ab initio molecular dynamics. Recently, there has been growing interest in chemical reaction networks (CRNs), which abstract away the complexity of the quantum chemical potential energy surface, allowing for efficient exploration of even very complex reactive spaces.
My colleagues and I develop new methods for constructing and analyzing CRNs, with the goal of automatically revealing the inner workings of complex chemical processes without prior domain knowledge. We have developed an approach that uses graph theory to rigorously identify low-cost reaction pathways in CRNs. More recently, we have developed stochastic methods to not only identify pathways to known species of interest, but also to automatically identify the natural products of CRNs using simple heuristics. With this method, it is now possible to easily and rapidly generate hypotheses for experimental characterization and in-depth mechanistic studies using only computed reaction thermodynamics.
As part of ongoing collaborations within JCESR and with the Blau Group at LBNL, I continue to build on the successes of these methods and devise new ways to efficiently explore reactive spaces.
Revealing the mechanistic origins of SEI formationThe solid electrolyte interphase (SEI) is critically important for metal-ion battery function - preventing electrolyte degradation and allowing reversible cycling - but it is also notoriously difficult to study. I aim to take a new approach to understand how the SEI forms. Using CRNs, one can automatically identify thermodynamically and kinetically favorable reaction pathways to SEI products of interest. My colleagues and I have, for instance, recently taken this approach to understand the formation of the newly proposed organic SEI component lithium ethylene monocarbonate (LEMC). With sufficiently comprehensive collection of reaction mechanisms, I then construct microkinetic models of SEI formation. Such models allow me to observe competition between SEI products and formation pathways and can provide mechanistic explanations for structural and compositional trends observed in experiment.
To date, most of my research on SEI formation has focused on lithium-ion batteries, but I am currently working to expand my research and explore SEI formation mechanisms in magnesium-ion batteries.