Computing and Data Science
CBE researchers apply computational methods to problems across all length scales, including quantum mechanical modeling of catalytic reactions, bio-inspired and data-driven materials design, interfacial behavior of nanomaterials, and design and optimization of complex systems. Rutgers provides a rich environment for computational research via cross-disciplinary collaborations and high performance computing resources.
· Multiscale methods
· Enhanced sampling techniques
· Computational chemistry
· Statistical mechanics
· Discrete element methods
· Deep learning
· Machine learning
· Data-driven methods
· Systems engineering
Multiscale Simulation and Modeling
Faculty: Dignon, Dutt, Glasser, Guo, Neimark, Tomassone
We apply molecular modeling and statistical mechanics to develop solutions to problems in energy, drug delivery, and catalysis, as well as other biomedical, environmental, and pharmaceutical applications.
Faculty: Dignon, Dutt, Guo
The discovery and design of materials is greatly accelerated by information gained from computational studies at the molecular level, along with high-throughput screening techniques, enhanced sampling, deep learning, and data-driven methods. We use these to explore new materials to meet therapeutic, electronic, environmental, and biopharmaceutical needs.
Faculty: Celik, Hildebrandt, Neimark
We study reaction processes at both the molecular and the systems ends of the spectrum, using quantum mechanical modeling and molecular simulation to understand and design efficient catalytic materials, and reactor modeling to define optimal operating conditions for CO2 reduction in reaction systems.
Process Systems Engineering
Faculty: Androulakis, Hildebrandt, Ramachandran
We use computational modeling and simulation tools to design optimal chemical and pharmaceutical process systems for clean and efficient operation, and to elucidate principles underlying complex biological systems.