Our research focuses on the development and application of computer simulation and integrated machine learning methods, focusing on diverse structures from atoms to micrometers, interpretable property predictions, and guidance in materials selection. We work in integrated teams with other experimental and theoretical groups, explain the function of nanomaterials and biomaterials at the atomic or electronic scale, develop community modeling resources and data science/machine learning tools. Specifically, we develop the Interface force field (IFF) and a surface model database for the simulation of inorganic and organic compounds, solid-electrolyte interfaces, and gases, as well as complex interfaces in one single platform. IFF covers metals, oxides, 2D materials, minerals, polymers, gases, and has compatibility with standard biomolecular force fields. Applications include catalysts for fuel cells and energy conversion, perovskites, battery materials, polymer composites, biominerals, drug delivery, protein function, and building materials. We develop, validate, and deploy methods to improve and expand chemistry-driven and physics-driven models based on fundamental chemical theory, experimentally verified knowledge, and insights into electronic structure via quantum methods. We develop chemistry and physics informed graph neural networks and feature-based machine learning methods, as well as integrated AI agents to solve materials design and property prediction problems orders of magnitude faster than with conventional methods. We have extensive experience working with multinational companies and start-ups who deploy our methods for materials design (Amazon, BASF, P&G, Corning, Sika AG, Goodyear).
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
CHEN 4130 - Chemical Engineering Laboratory
Primary Instructor
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Fall 2018 / Fall 2019 / Spring 2021 / Fall 2021
Involves planning and execution of chemical engineering experiments on mass transfer operations, separations, and chemical reactors. Interprets experimental data with theoretical principles and statistical analysis. Emphasizes communication with written memos, full reports, and oral presentations.
CHEN 4440 - Chemical Engineering Materials
Primary Instructor
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Spring 2018 / Spring 2020 / Spring 2021 / Spring 2022 / Spring 2023 / Spring 2024
Introduces materials engineering, including properties of polymers, metals, ceramics, and semiconductors, especially as related to chemical engineering processes.
CHEN 4460 - Polymer Engineering
Primary Instructor
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Fall 2024
Introductory polymer engineering course reviewing basic terminology and definitions; the properties and synthetic routes of important industrial polymers; and processing of polymers and their applications. Same as CHEN 5460.
CHEN 5460 - Polymer Engineering
Primary Instructor
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Fall 2024
Introductory polymer engineering course reviewing basic terminology and definitions; the properties and synthetic routes of important industrial polymers; and processing of polymers and their applications. Same as CHEN 4460.
