11/04/2026
By Kwok Fan Chow

The Kennedy College of Science, Department of Chemistry, invites you to attend a Ph.D. dissertation defense by Mohammad Amin Kiani, titled "Electronic Structure Derived Intermolecular Interaction Maps: Method Development, Software Implementation, and Applications to Molecular and Extended Systems, Reactivity, and Selectivity."

Details

  • Date: Thursday, Nov. 19, 2026
  • Time: 1 p.m.
  • Location: Olney, Room 518

Committee members

  • Advisor: Lawrence M. Wolf, Ph.D., Department of Chemistry, University of Massachusetts Lowell
  • Mingdi Yan, Ph.D., Department of Chemistry, University of Massachusetts Lowell
  • Olof Ramström, Ph.D., Department of Chemistry, University of Massachusetts Lowell
  • Jerome Delhommelle, Ph.D., Department of Chemistry, University of Massachusetts Lowell

Abstract

Intermolecular interactions govern molecular recognition, reactivity, catalysis and materials behavior. While traditional potential maps visualize single properties, such as electrostatics, they fail to quantify how distinct physical components combine to drive interactions. This dissertation develops intermolecular interaction potential maps, an electronic-structure-derived methodology that places chemically selected probes around molecular or periodic hosts to quantify and visualize explicit host-probe interaction energies. By integrating energy decomposition analysis, these maps resolve total energies into electrostatic, orbital, exchange repulsion and dispersion contributions.

The first part of this work establishes an initial isodensity surface framework, demonstrating through applications to aromatic systems, organometallics and organic reactions how these maps reveal chemical insights missed by conventional methods. The methodology is then generalized to include trajectory-based and iso-minimum approaches, where probe placement is optimized along density-gradient paths utilizing Quantum Theory of Atoms in Molecules topology. This framework is further extended to periodic chemical systems via unit-cell workflows to map extended environments. Finally, the dissertation connects these potential maps to quantitative structure-selectivity relationship modeling by using interaction field descriptors for machine-learning analysis to successfully interpret and predict catalytic selectivity, and ultimately extends beyond energy decomposition analysis to time-dependent density functional theory for mapping transitions in spectral properties for PFAS and methyl violet systems.

All interested students and faculty members are invited to attend.