Data and physics-driven atomic modeling at interfaces
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VISION
Formulate innovative design principles to overcome parasitic side reactions and thermodynamic losses at heterogeneous interfaces 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 heterogeneous interfaces
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 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
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