Research Overview
My research focuses on computational chemistry approaches to understand nanoscale phenomena. Combining molecular dynamics simulations with machine learning techniques, I investigate self-assembly processes, biomolecular interactions, and catalytic mechanisms at atomic resolution.
Machine Learning for Molecular Systems
I am interested in developing and applying machine learning methods to model complex molecular systems with greater accuracy and efficiency. My work focuses on machine-learned interatomic potentials, data-driven molecular representations, and AI-assisted simulation strategies to bridge the gap between quantum-mechanical accuracy and large-scale molecular simulations.
Nanocluster Self-Assembly
Investigating the thermodynamic drivers of metal nanocluster assembly using enhanced sampling techniques. Developing machine-learned potentials to simulate assembly pathways over extended timescales. Current work focuses on ligand-mediated assembly mechanisms and their response to environmental stimuli.
Biomolecule-Nanocluster Interactions
Characterizing interaction dynamics between proteins and monolayer-protected clusters through all-atom MD simulations. Developing collective variables to study binding thermodynamics and kinetics. Recent findings reveal sequence-dependent binding affinities with implications for biosensing applications.
Catalytic Reaction Mechanisms
Elucidating reaction pathways in heterogeneous catalysis using metadynamics. Current focus on copper-iron bimetallic systems for C-H activation. Developing hybrid QM/ML approaches to achieve DFT-level accuracy in large-scale simulations of catalytic cycles.