Physical and Computational Chemistry and Machine Learning
Physical, computational and machine learning includes both experimental and theoretical chemistry.
It involves understanding the physical properties of atoms, clusters, molecules and chemical systems with a particular interest in interactions and behavior at the atomic level.
Research Topics
- Statistical mechanics and molecular simulations
- Machine learning techniques
- Gas phase and intracluster ion-molecule reactions
- Ultrafast dissociation dynamics of polyatomic molecules
- Molecules, clusters and condensed phase systems
- Design and molecular level studies of nanomaterials
- Calculations of intermolecular forces
- Laser synthesis of nanomaterials
- Metal ion reduction kinetics
- Electronic structure theory and quantum mechanics
Faculty
Mary Eugenia Kapp Chair in Chemistry
and Professor
Nanoscience
molecular clusters
graphene and carbon nanotubes
Assistant Professor
Electronic structure theory
quantum chemistry
noncovalent interactions
Katharine Moore Tibbetts, Ph.D.
Associate Professor
Nanoscience
laser synthesis
ultrafast dissociation dynamics