Our research advances AI-enabled molecular simulation and data-driven modeling to accelerate the design of materials, chemical processes, and functional systems across scales, from atoms to micrometers. We integrate physics- and chemistry-based simulation with machine learning, graph neural networks, and emerging AI agents to deliver mechanistic insight, predictive accuracy, and actionable design rules—bridging fundamental science and industrial deployment. A core thrust is the development of the INTERFACE Force Field (IFF) and a unified surface-modeling platform that enables predictive simulations of metals, oxides, 2D materials, minerals, polymers, gases, biomolecules, and complex interfaces within a single, interoperable framework. This capability supports rapid virtual prototyping of catalysts, battery and energy materials, polymer composites, biomaterials, and construction materials, dramatically reducing reliance on costly trial-and-error experimentation. Working closely with experimental partners and industry, we translate theory-grounded models and AI workflows into scalable tools for materials discovery, optimization, and decision-making. Our methods are actively used by multinational companies and startups to shorten development cycles, de-risk innovation, and enable next-generation products in energy, chemicals, advanced manufacturing, and sustainable materials.
keywords
materials design, simulation methods, force fields, artificial intelligence, biophysics, computational materials science, computational biology, biomaterials, catalysis, computational chemistry, inorganic-organic interfaces, nanocomposites, self-assembly, corrosion, soft matter, clay minerals, integrated team research, convergent research, science leadership, corporate research and development, fundamental chemistry knowledge generation and application across scales
