Computational and Data-Driven Design of Perturbed Metal Sites for Catalytic Transformations
| dc.contributor.author | Huang, Yang | en |
| dc.contributor.committeechair | Xin, Hongliang | en |
| dc.contributor.committeemember | Achenie, Luke E. | en |
| dc.contributor.committeemember | Tong, Rong | en |
| dc.contributor.committeemember | Bai, Xianming | en |
| dc.contributor.department | Chemical Engineering | en |
| dc.date.accessioned | 2024-05-24T08:01:06Z | en |
| dc.date.available | 2024-05-24T08:01:06Z | en |
| dc.date.issued | 2024-05-23 | en |
| dc.description.abstract | 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. | en |
| dc.description.abstractgeneral | In addressing the critical issues of climate change, energy scarcity, and pollution, the drive towards a sustainable economy has made catalysis a key area of focus. Computational chemistry has revolutionized our understanding of catalysts, especially in identifying and analyzing their active sites. Furthermore, the integration of open-access data and advanced computing has elevated data science as a crucial component in catalysis research. This synergy of computational and data-driven approaches is advancing the development of innovative catalytic materials, marking a significant stride in tackling environmental challenges. In my PhD research, I mainly work on the development of computational and data-driven methods for better understanding, design and discovery of catalytic materials. Firstly, I develop physics models for people to intuitively understand the reactivity of transition and noble metal catalysts. Then I embed the physics models into deep learning models for accurate and insightful predictions. Secondly, for a class of complex metal catalysts called high-entropy alloy (HEA) which is hard to model, I develop a modeling framework by hybridizing computational and data-driven approaches. With this framework, we successfully elucidate the experimentally observed improved activity of PtPdCuNiCo HEA in oxygen reduction reaction which is a key reaction in fuel cell technology. Thirdly, I develop a framework to virtually screen catalyst molecules to optimize polymerization reaction and provide potential candidates to our experimental collaborator to synthesize. Our collaboration leads to the discovery of novel high-performance molecular catalysts. | en |
| dc.description.degree | Doctor of Philosophy | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:40441 | en |
| dc.identifier.uri | https://hdl.handle.net/10919/119075 | en |
| dc.language.iso | en | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | density functional theory | en |
| dc.subject | tight binding theory | en |
| dc.subject | interpretable deep learning | en |
| dc.subject | Bayesian optimization | en |
| dc.subject | catalysis | en |
| dc.title | Computational and Data-Driven Design of Perturbed Metal Sites for Catalytic Transformations | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Chemical Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Doctor of Philosophy | en |