Developing an atomic-scale computational framework to gain insights about the electrochemical double-layer for applications to renewable energy

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Coupling renewably generated electricity with inert starting materials to produce value-added chemicals and fuels could have potentially transformative benefits for society. However, current state-of-the-art energy storage and conversion devices such as batteries for electric vehicles and electrochemically generating carbon-based fuels are not yet efficient enough to justify the widespread implementation of these systems from an economic perspective. In order to design improved energy storage and conversion devices, researchers need to understand the fundamental mechanisms that govern energy transformation, yet this is still a challenge due to the complexity of these multi-component systems. Accurate computational simulations can give insight into chemical mechanisms where intermediates species are difficult to detect (for example, being too transient or too dilute) via current experimental techniques. Without a rigourous set of experimental data on reaction intermediates to guide computational chemists, the current challenge is to design computational simulations to understand the observables that are known-such as product formation rates-with the ultimate goal being making reliable predictions about how to improve the efficiency of a given energy storage or catalytic system. Hence, these simulations need to be extremely accurate both in terms of the fundamental interactions between individual atoms, and the model itself needs to be a detailed enough representation of reality. To meet the requirement of chemical accuracy, my research program will utilize quantum mechanical methods (which resolve the chemical system to the level of electrons) to map out reaction pathways in three different energy transformation applications: (i) aqueous metal-air batteries which could extend the range of electric vehicles to be on par with that of internal combustion engine vehicles, (ii) electrochemically converting CO2 to carbon-based fuels such as methane and ethanol to both close the carbon cycle and mitigate the effects of climate change, and (iii) applying statistical mechanical techniques to the cathodic half-reaction for water splitting (hydrogen evolution). The results from all three projects will be used to develop a more complete computational model for studying electrochemical processes. The long-term vision of my research program will provide researchers with a framework of computational simulations to study generalized electrochemical reactions, and take advantage of the efficiency offered by machine-learning approaches. The ultimate goal from developing this framework is to provide researchers with a comprehensive electrochemical model as well as a means to carry out efficient calculations that are of high enough quality such that theory/computation alone could make quantitative predictions of improved electrochemical energy storage systems.