{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/95915"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/95915","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Accelerating Catalyst Discovery via Ab Initio Machine Learning","abstract":"In recent decades, machine learning techniques have received an explosion of interest in the domain of high-throughput materials discovery, which is largely attributed to the fastgrowing development of quantum-chemical methods and learning algorithms. Nevertheless, machine learning for catalysis is still at its initial stage due to our insufficient knowledge of the structure-property relationships. In this regard, we demonstrate a holistic machine-learning framework as surrogate models for the expensive density functional theory to facilitate the discovery of high-performance catalysts. The framework, which integrates the descriptor-based kinetic analysis, material fingerprinting and machine learning algorithms, can rapidly explore a broad range of materials space with enormous compositional and configurational degrees of freedom prior to the expensive quantum-chemical calculations and/or experimental testing. Importantly, advanced machine learning approaches (e.g., global sensitivity analysis, principal component analysis, and exploratory analysis) can be utilized to shed light on the underlying physical factors governing the catalytic activity on a diverse type of catalytic materials with different applications. Chapter 1 introduces some basic concepts and knowledge relating to the computational catalyst design. Chapter 2 and Chapter 3 demonstrate the methodology to construct the machine-learning models for bimetallic catalysts. In Chapter 4, the multi-functionality of the machine-learning models is illustrated to understand the metalloporphyrin's underlying structure-property relationships. In Chapter 5, an uncertainty-guided machine learning strategy is introduced to tackle the challenge of data deficiency for perovskite electrode materials design in the electrochemical water splitting cell.","abstract_html":"In recent decades, machine learning techniques have received an explosion of interest in the domain of high-throughput materials discovery, which is largely attributed to the fastgrowing development of quantum-chemical methods and learning algorithms. Nevertheless, machine learning for catalysis is still at its initial stage due to our insufficient knowledge of the structure-property relationships. In this regard, we demonstrate a holistic machine-learning framework as surrogate models for the expensive density functional theory to facilitate the discovery of high-performance catalysts. The framework, which integrates the descriptor-based kinetic analysis, material fingerprinting and machine learning algorithms, can rapidly explore a broad range of materials space with enormous compositional and configurational degrees of freedom prior to the expensive quantum-chemical calculations and/or experimental testing. Importantly, advanced machine learning approaches (e.g., global sensitivity analysis, principal component analysis, and exploratory analysis) can be utilized to shed light on the underlying physical factors governing the catalytic activity on a diverse type of catalytic materials with different applications. Chapter 1 introduces some basic concepts and knowledge relating to the computational catalyst design. Chapter 2 and Chapter 3 demonstrate the methodology to construct the machine-learning models for bimetallic catalysts. In Chapter 4, the multi-functionality of the machine-learning models is illustrated to understand the metalloporphyrin's underlying structure-property relationships. In Chapter 5, an uncertainty-guided machine learning strategy is introduced to tackle the challenge of data deficiency for perovskite electrode materials design in the electrochemical water splitting cell.","abstract_has_math":false,"creators":["Li, Zheng"],"institution":"Virginia Tech","degree_name":"Doctor of Philosophy","degree_level":"doctoral","degree_discipline":"Chemical Engineering","degree_department":"Chemical Engineering","school":null,"contributors":[],"advisors":[],"committee_chairs":["Xin, Hongliang","Achenie, Luke E. K."],"committee_members":["Deshmukh, Sanket A.","Morris, Amanda J."],"year":2019,"date_issued":"2019-12-03","date_published":"2019-12-03","updated_at":"2026-07-22T22:20:31Z","subjects":["Computational catalysis","Density functional theory","Machine learning"],"languages":[],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:23045"],"render_values":[{"text":"vt_gsexam:23045","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10919/95915","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Xin, Hongliang","Achenie, Luke E. 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Nevertheless, machine learning for catalysis is still at its initial stage due to our insufficient knowledge of the structure-property relationships. In this regard, we demonstrate a holistic machine-learning framework as surrogate models for the expensive density functional theory to facilitate the discovery of high-performance catalysts. The framework, which integrates the descriptor-based kinetic analysis, material fingerprinting and machine learning algorithms, can rapidly explore a broad range of materials space with enormous compositional and configurational degrees of freedom prior to the expensive quantum-chemical calculations and/or experimental testing. Importantly, advanced machine learning approaches (e.g., global sensitivity analysis, principal component analysis, and exploratory analysis) can be utilized to shed light on the underlying physical factors governing the catalytic activity on a diverse type of catalytic materials with different applications. Chapter 1 introduces some basic concepts and knowledge relating to the computational catalyst design. Chapter 2 and Chapter 3 demonstrate the methodology to construct the machine-learning models for bimetallic catalysts. In Chapter 4, the multi-functionality of the machine-learning models is illustrated to understand the metalloporphyrin's underlying structure-property relationships. In Chapter 5, an uncertainty-guided machine learning strategy is introduced to tackle the challenge of data deficiency for perovskite electrode materials design in the electrochemical water splitting cell."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["Machine learning and deep learning techniques have revolutionized a range of industries in recent years and have huge potential to improve every aspect of our daily lives. Essentially, machine-learning provides algorithms the ability to automatically discover the hidden patterns of data without being explicitly programmed. Because of this, machine learning models have gained huge successes in applications such as website recommendation systems, online fraud detection, robotic technologies, image recognition, etc. Nevertheless, implementing machine-learning techniques in the field of catalyst design remains difficult due to 2 primary challenges. The first challenge is our insufficient knowledge about the structure-property relationships for diverse material systems. Typically, developing a physically intuitive material feature method requests in-depth expert knowledge about the underlying physics of the material system and it is always an active field. The second challenge is the lack of training data in academic research. In many cases, collecting a sufficient amount of training data is not always feasible due to the limitation of computational/experimental resources. Subsequently, the machine learning model optimized with small data tends to be over-fitted and could provide biased predictions with huge uncertainties. To address the above-mentioned challenges, this thesis focus on the development of robust feature methods and strategies for a variety of catalyst systems using the density functional theory (DFT) calculations. Through the case studies in the chapters, we show that the bulk electronic structure characteristics are successful features for capturing the adsorption properties of metal alloys and metal oxides. While molecular graphs are robust features for the molecular property, e.g., energy gap, of metal-organics compounds. Besides, we demonstrate that the adaptive machine learning workflow is an effective strategy to tackle the data deficiency issue in search of perovskite catalysts for the oxygen evolution reaction."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Doctor of Philosophy"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["ETD"]},{"key":"dc:title","label":"Title","values":["Accelerating Catalyst Discovery via Ab Initio Machine Learning"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Xin, Hongliang","Achenie, Luke E. K."],"dc:contributor.committeemember":["Deshmukh, Sanket A.","Morris, Amanda J."],"dc:contributor.department":["Chemical Engineering"],"dc:creator":["Li, Zheng"],"dc:date.accessioned":["2019-12-04T09:00:23Z"],"dc:date.available":["2019-12-04T09:00:23Z"],"dc:date.issued":["2019-12-03"],"dc:description.abstract":["In recent decades, machine learning techniques have received an explosion of interest in the domain of high-throughput materials discovery, which is largely attributed to the fastgrowing development of quantum-chemical methods and learning algorithms. Nevertheless, machine learning for catalysis is still at its initial stage due to our insufficient knowledge of the structure-property relationships. In this regard, we demonstrate a holistic machine-learning framework as surrogate models for the expensive density functional theory to facilitate the discovery of high-performance catalysts. The framework, which integrates the descriptor-based kinetic analysis, material fingerprinting and machine learning algorithms, can rapidly explore a broad range of materials space with enormous compositional and configurational degrees of freedom prior to the expensive quantum-chemical calculations and/or experimental testing. Importantly, advanced machine learning approaches (e.g., global sensitivity analysis, principal component analysis, and exploratory analysis) can be utilized to shed light on the underlying physical factors governing the catalytic activity on a diverse type of catalytic materials with different applications. Chapter 1 introduces some basic concepts and knowledge relating to the computational catalyst design. Chapter 2 and Chapter 3 demonstrate the methodology to construct the machine-learning models for bimetallic catalysts. In Chapter 4, the multi-functionality of the machine-learning models is illustrated to understand the metalloporphyrin's underlying structure-property relationships. In Chapter 5, an uncertainty-guided machine learning strategy is introduced to tackle the challenge of data deficiency for perovskite electrode materials design in the electrochemical water splitting cell."],"dc:description.abstractgeneral":["Machine learning and deep learning techniques have revolutionized a range of industries in recent years and have huge potential to improve every aspect of our daily lives. Essentially, machine-learning provides algorithms the ability to automatically discover the hidden patterns of data without being explicitly programmed. Because of this, machine learning models have gained huge successes in applications such as website recommendation systems, online fraud detection, robotic technologies, image recognition, etc. Nevertheless, implementing machine-learning techniques in the field of catalyst design remains difficult due to 2 primary challenges. The first challenge is our insufficient knowledge about the structure-property relationships for diverse material systems. Typically, developing a physically intuitive material feature method requests in-depth expert knowledge about the underlying physics of the material system and it is always an active field. The second challenge is the lack of training data in academic research. In many cases, collecting a sufficient amount of training data is not always feasible due to the limitation of computational/experimental resources. Subsequently, the machine learning model optimized with small data tends to be over-fitted and could provide biased predictions with huge uncertainties. To address the above-mentioned challenges, this thesis focus on the development of robust feature methods and strategies for a variety of catalyst systems using the density functional theory (DFT) calculations. Through the case studies in the chapters, we show that the bulk electronic structure characteristics are successful features for capturing the adsorption properties of metal alloys and metal oxides. While molecular graphs are robust features for the molecular property, e.g., energy gap, of metal-organics compounds. Besides, we demonstrate that the adaptive machine learning workflow is an effective strategy to tackle the data deficiency issue in search of perovskite catalysts for the oxygen evolution reaction."],"dc:description.degree":["Doctor of Philosophy"],"dc:format.medium":["ETD"],"dc:identifier.other":["vt_gsexam:23045"],"dc:identifier.uri":["http://hdl.handle.net/10919/95915"],"dc:publisher":["Virginia Tech"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Computational catalysis","Density functional theory","Machine learning"],"dc:title":["Accelerating Catalyst Discovery via Ab Initio Machine Learning"],"dc:type":["Dissertation"],"thesis:degree_discipline":["Chemical Engineering"],"thesis:degree_level":["doctoral"],"thesis:degree_name":["Doctor of Philosophy"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:20:31Z"}