Heecheol Jang

Heecheol Jang is a graduate student in chemical engineering developing computational models to quantify the force-responsiveness of mechanophores, molecules whose mechanically labile bonds cleave under lower force than typical covalent bonds. Discovering novel mechanophores could enable next-generation polymers with enhanced toughness and self-healing properties, yet the underlying principles remain poorly understood. He develops a generalized tensioned model for bond activation using force-free reaction descriptors and effective spring constants to predict experimentally measured force responsiveness with significantly lower computational cost than direct force simulations. Heecheol extends this framework beyond carbon-carbon bond cleavage to organic mechanophores with non-CC bonds, incorporating electrostatic and steric descriptors, and develops a machine learning framework for high-throughput discovery of transition metal complex mechanophores. Using MATLAB’s Curve Fitting and Symbolic Math Toolboxes, he converts discrete density functional theory outputs into continuous representations to generate machine learning descriptors. As a MathWorks Fellow, Heecheol will expand the discovery framework and conduct benchmark calculations, establishing foundations for the de novo design of novel polymers with enhanced toughness and self-healing capabilities.