Data and physics driven atomic modeling of electrochemical interfaces
VISION
Formulate innovative design principles to overcome parasitic side reactions and overpotential loses to develop scalable, efficient, and selective electrochemical technologies.
Utilize novel workflows, incorporating graph-theory based data mining and machine learning algorithms along with advanced atomistic simulation techniques, to discover governing interactions and prevalent reactions at the electrode-electrolyte interface.
Thrust 1: Data driven methods for atomic modeling
Elucidate experimentally relevant atomic configurations
Effectively navigate large phase space of possible configurations.
Develop efficient workflows:
Systematic data sampling
Estimate important interactions
Model range of materials
Thrust 2: Capturing dynamics at electrocatalytic interfaces
Identify novel electrocatalysts for carbon-neutral reactants
Benchmark subset of atomic configurations to experimental characterization data.
Identify descriptors to inverse design and experimentally probe promising electrocatalyst.
Thrust 3: Atomic modeling at battery electrodes
Identify solvents and electrodes for metal-chalcogen batteries
Algorithmic approaches to identify selectivity and activity descriptors.
Generate and gather descriptor-based information for materials.
Pareto analysis to identify promising materials