Our group’s focus is to use computational approaches to mathematically model and simulate the atomic and molecular behavior of materials. In particular, we study semiconductor materials.
Our toolkit: We develop, extend and use a broad range of molecular-scale simulation tools, especially Ab initio Density Functional Theory, Molecular Dynamics (all-atom and coarse-grained), kinetic Monte Carlo, free energy calculations (Steered MD and Thermodynamic Integration), Nudged Elastic Band.
We also develop and use a growing array of machine learning tools for accelerated searches of complex parameter spaces, especially materials discovery and optimizing materials processing.
We currently have four main application areas:
(1) Advanced Organic Materials (Covalent organic frameworks and Antibacterial oligomers)
(2) Algorithm Development (Force field development, Nudged Elastic Band (NEB), large combinatorial space optimization, and molecular simulation Python module)
(3) Machine Learning (Bayesian optimization, Generative Adversarial Network (GAN), and fingerprinting)
(4) Renewable Energy Materials (Hybrid organic/inorganic perovskites, quantum dot nanocrystals, lithium extraction, and phase change materials)
We enjoy a virtually unique focus on studies of advanced materials processing and nucleation. In particular, we are very interested in the links between processing to structure to function. Take a look at our research overviews on these projects for a glimpse at the wide variety of fields we are studying.
Interested in this research? We are always looking for new undergraduate, masters and PhD researchers. Direct all inquiries to Prof. Paulette Clancy, at pclancy3@jhu.edu.