Boosting Density Embedding with Machine Learning and Nonstandard Workflows

点击翻译按钮获取中文摘要

Professor Michele Pavanello of Rutgers University-Newark is supported by an award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry to model molecular condensed phases and materials from first principles. Pavanello and his research group will develop open-source software encoding first-principles Quantum Mechanics for the prediction of materials properties and their rational design. The methods will exploit state-of-the-art machine learning and divide-and-conquer algorithms to speed up the computational time without compromising on the accuracy and rigorousness of the approaches. Pavanello will apply the newly developed methods to open problems in catalysis by computationally predicting reaction paths and in materials engineering by predicting charge mobilities and polymorphism. The scientific advances will be complemented by a strong outreach program aimed at training high school students from unrepresented minority school districts in computer coding (Python bootcamps) building on already-established programs between Rutgers-Newark and nearby school districts.Professor Michele Pavanello and his research group will develop density functional theory embedding methods aimed at computing the electronic structure of materials and molecular condensed phases tackling open problems ranging from catalysis to charge mobility in molecular semiconductors. The project will consist of: (1) boost DFT embedding employing machine learned Kohn-Sham one-electron reduced density matrices to access system sizes beyond the nanoscale; (2) develop an adaptive embedding method that defines in real time the most accurate subsystem topology for the simulation and is capable of running ab initio dynamics for applications to catalysis and other chemical processes; and (3) develop nonstandard workflows for “hybrid” embedding schemes, such as Kohn-Sham DFT embedded in orbital-free DFT to tackle nanoscale metallic subsystems, wavefunction theory in DFT for applications to charged condensed-phase systems and prediction of crystal polymorphs.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.