Theoretical & Computational Chemistry
Read about my work in developing data-driven many-body energy
(MB-nrg) potential energy functions (PEFs) to study large molecular systems at the Paesani Group
Data-driven many-body energy (MB-nrg) potential energy functions (PEFs) provide predictive molecular models for large systems with quantum mechanical accuracy, positioning them as a powerful tool in investigating structural, thermodynamic, dynamical, and spectroscopical properties of generic molecular systems from the gas to the condensed phase.
Alongside my mentor Ruihan Zhou, I am developing the first MB-nrg PEF for a ring-molecule, phloroglucinol, a highly effective organic ice nucleator. A thorough understanding of phloroglucinol’s ice-binding mechanism would allow us to harness its natural properties to promote ice crystallization through applications like weather engineering, and more efficient thermal storage techniques.
In our study of ice-nucleating organic crystals, this PEF will be used to develop a comprehensive description of ice-binding molecules’ interaction with water, their ice-binding sites, and the kinetics of ice nucleation and antifreeze behavior via simulations that model the behavior of a monolayer of phloroglucinol molecules at the interface between water and ice.
Current Research
Theory:
Many-Body Potential Energy Function
The many-body expansion (MBE) decomposes the energy of a system of N monomers into a summation over n-body contributions:
MBE converges quickly for nonmetallic systems, so we can approximate the two body PEFs as:
are 2-body electrostatics and dispersion describing the classical 2B effects with simple functional forms.
accounts for the classical many-body polarization.
is machined learned with permutationally invariant polynomials (PIPs) (MB-nrg) trained from ab-initio data to characterize the short-range quantum mechanical many-body effects. A generalized expression for n-Body PIP adopts the following functional form:
The Paesani Group at SoCal TheoChem 6