Computational and Data-Driven Design of Perturbed Metal Sites for Catalytic Transformations

We integrate theoretical, computational and data-driven approaches for the sake of understanding, design and discovery of metal based catalysts. Firstly, we develop theoretical frameworks for predicting electronic descriptors of transition and noble metal alloys, including a physics model of d-band center, and a tight-binding theory of d-band moments to systematically elucidate the distinct electronic structures of novel catalysts. Within this framework, the hybridization of semi-empirical theories with graph neural network and attribution analysis enables accurate prediction equipped with mechanistic insights. In addition, novel physics effect controlling surface reactivity beyond conventional understanding is uncovered. Secondly, we develop a computational and data-driven framework to model high entropy alloy (HEA) catalysis, incorporating thermodynamic descriptor-based phase stability evaluation, surface segregation modeling by deep learning potential-driven molecular simulation and activity prediction through machine learning-embedded electrokinetic model. With this framework, we successfully elucidate the experimentally observed improved activity of PtPdCuNiCo HEA in oxygen reduction reaction. Thirdly, a Bayesian optimization framework is employed to optimize racemic lactide polymerization by searching for stereoselective aluminum (Al) -complex catalysts. We identified multiple new Al-complex molecules that catalyzed either isoselective or heteroselective polymerization. In addition, feature attribution analysis uncovered mechanistically meaningful ligand descriptors that can access quantitative and predictive models for catalyst development.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Chemical Engineering

Department dc:contributor.department

Chemical Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2024

Author dc:creator

• Huang, Yang

Chair dc:contributor.committeechair

• Xin, Hongliang

Committee members dc:contributor.committeemember

• Achenie, Luke E. K.
• Tong, Rong
• Bai, Xianming

• density functional theory
• tight binding theory
• interpretable deep learning
• Bayesian optimization
• catalysis

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:40441

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/119075