Theoretical and computational science and engineering are important components of research in the department. The continuing advances in computational capabilities have made it possible to study phenomena in unprecedented detail, over large length scales and long timescales. Princeton University has an active research-computing community that provides advanced hardware and software capabilities and educational opportunities for students and researchers at all levels.
Computational statistical mechanics plays a key role within the department is, typically done through classical Monte Carlo or molecular-dynamics simulations of proteins, complex fluids, polymers, or colloidal particles, frequently under non-equilibrium conditions.
Another focus area for theoretical and computational work involves quantum mechanical calculations (density functional theory and ab initio molecular dynamics). These are used to predict electronic properties of catalysts and to generate accurate force fields for use in larger-scale (classical) calculations. Machine learning and other big-data methods play an increasingly important role in this area.
In both classical and quantum calculations, at Princeton we strive to develop new theoretical methods and efficient computational algorithms, which are then shared with the broader research community as open-source software.
Patterning in Developing Embryos; Physical Properties and Function of RNA/Protein Bodies; Architecture and Dynamics of the Cytoskeleton
Dynamics of Fluids and Flexible Solids; Interfacial Phenomena; Pattern Forming Instabilities; Dynamics of Living Systems
Liquid State Theory; Glass Transition; Nucleation Theory; Protein Thermodynamics; Molecular Simulation; Biopreservation
Theory and Simulation of Electrochemical Processes; Electrochemical Reaction and Transport Mechanisms; Dynamic Electrochemical Interfaces; Disordered Electrochemical Systems
Ionized Gas Plasma-Aided Nanofabrication; Plasma Catalysis; Molecular Simulations of Plasma-Surface Interactions
Theory and simulation of biomolecular self-assembly; Design and bioengineering of protein/RNA compartments; Multiscale computational models
Process Systems Engineering; Modeling, Synthesis and Analysis of Renewable Energy Systems; Optimization Methods and Algorithms
Molecular simulation of fluids, materials and biological systems; Thermodynamic analysis of processes; Ionic liquids and their applications
Computational Materials Discovery; High-throughput Quantum-chemical Modeling; Machine Learning for Solid-state and Nanoporous Energy Materials
Active Site Engineering; Kinetic, Synthetic and Theoretical Techniques; Reaction Mechanisms of Heterogeneous Catalysts
Granular and Multiphase Flow; Chemical Reactor Design, Stability, and Dynamics
Theory and simulation of soft/polymeric materials; computational materials design; multiscale simulation; machine-learning in molecular modeling
Clay Mineral Surface Geochemistry; Geologic Carbon Sequestration; Kinetic Isotope Effects in Aqueous Systems; Liquid Water at Interfaces
Artificial Intelligence; Statistical Machine Learning; Chemical Physical; Physical Chemistry; Biology; Materials Science; Simulations; Deep Learning; Generative Models
Theory and simulation of molecular self-assembly; multicomponent fluids; biomolecules; design of soft materials
Structural Topology Optimization; Reliability Based Design and Topology Optimization; Topological Data Structures
Quantitative Analysis of Pattern Formation and Morphogenesis in Developing Tissues; Genetics, Genomics and Computation of Signaling Pathways
Fluid Dynamics and Transport Processes; Complex Fluids; Colloidal Hydrodynamics; Microfluidics; Cellular-scale Hydrodynamics; Biofilms
Durability of Alkali-Activated Cements; Atomic and Nanoscale Morphology of Cementitious Materials; Reaction Kinetics of Cement Formation