Research
Our research interests and current projects.
Research Overview
Our research focuses on developing new mathematical tools, numerical algorithms, and computer software to qualitatively and quantitatively predict the outcome of chemical phenomena using strategies from quantum chemistry and machine learning.
Current Research Directions
1. Machine Learning for Molecular Design
We adapt state-of-the-art machine-learning algorithms to develop rapid, accurate, and efficient techniques for:
- Screening large molecular databases
- Studying molecular dynamics simulations of chemical processes
- Computationally designing molecules with desirable properties
2. Quantum Chemical Concepts & ChemTools Development
We develop rigorous mathematical definitions of empirical chemical concepts within the framework of quantum chemistry. Our group is the lead developer of the free and open-source ChemTools software package—a collection of tools for interpreting the numerical output of quantum chemistry calculations to gain chemical insight.
Research Impact
Our work bridges the gap between quantum mechanical calculations and chemical understanding, making computational chemistry more accessible and interpretable for the broader scientific community. Through ChemTools and our machine learning approaches, we enable researchers to extract meaningful chemical insights from complex quantum mechanical data.