High-throughput quantum chemistry for reactive molecules
High-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.
A new approach to exploring (electro)chemical reactivity
Understanding and controlling reactivity is key to a range of technological applications, from manufacturing to transportation and electronics. Typical theoretical studies of reactions 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.However, CRNs have not previously been applied to study electrochemistry. In part, this is because electrochemical reaction mechanisms are not well understood, making methods based on chemical intuition or reaction templates intractable. My colleagues and I develop new methods for constructing and analyzing (electro)chemical CRNs, with the goal of automatically revealing the inner workings of complex chemical processes without prior domain knowledge and without relying heavily on chemical intuition. Most 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 of complex reactive processes (such as those in Li-ion batteries) 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 electrolyte degradation
The solid electrolyte interphase (SEI), which forms as a result of electrolyte decomposition in metal-ion batteries, is critically important for allowing reversible cycling and preserving battery capacity. However, it is also notoriously difficult to study. I aim to take a new approach to understand how battery electrolytes react and how those reactions lead to SEI formation. My colleagues and I can identify individual reaction mechanisms automatically, using CRNs and using more traditional low-throughput DFT calculations. 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.
