{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/141157"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/141157","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Automated Implementation of Advanced Electronic Structure Methods","abstract":"The continuous demand for higher accuracy in computational chemistry necessitates the development of advanced many-body electronic structure methods. However, the derivation and efficient implementation of these theories constitute a significant bottleneck. As the rank of the associated tensors increases, the governing equations explode in complexity, rendering manual implementation labor-intensive, error-prone, and difficult to optimize for modern hardware. To address this challenge, this dissertation presents SeQuant, a comprehensive framework for the automated derivation and parallel implementation of many-body quantum chemistry methods. Built upon a robust symbolic algebra engine, SeQuant allows for the expression of theories in the natural language of second quantization. It automates the transformation of high-level theoretical ansatzes into explicit tensor contraction expressions and subsequently generates optimized, high-performance C++ code. A central innovation of this work is the extension of automated implementation to reduced-scaling methods, which exploit the sparsity inherent in electronic correlation. We introduce a novel \"tensor-of-tensors\" data structure designed to manage the irregular sparsity patterns of Pair Natural Orbital (PNO) formulations. This development enables the first fully automated implementation of PNO-Coupled Cluster (PNO-CC) methods, bridging the gap between symbolic abstraction and the runtime requirements of sparse tensor algebra. The results demonstrate that SeQuant not only reproduces established dense methods (such as CCSD and CCSDT) with high fidelity but also effectively handles the complexity of sparse, local correlation approaches. By decoupling the complexity of the physics from the details of the implementation, this framework establishes a new paradigm for method development, dramatically accelerating the translation of theoretical insights into computational reality.","abstract_html":"The continuous demand for higher accuracy in computational chemistry necessitates the development of advanced many-body electronic structure methods. However, the derivation and efficient implementation of these theories constitute a significant bottleneck. As the rank of the associated tensors increases, the governing equations explode in complexity, rendering manual implementation labor-intensive, error-prone, and difficult to optimize for modern hardware. To address this challenge, this dissertation presents SeQuant, a comprehensive framework for the automated derivation and parallel implementation of many-body quantum chemistry methods. Built upon a robust symbolic algebra engine, SeQuant allows for the expression of theories in the natural language of second quantization. It automates the transformation of high-level theoretical ansatzes into explicit tensor contraction expressions and subsequently generates optimized, high-performance C++ code. A central innovation of this work is the extension of automated implementation to reduced-scaling methods, which exploit the sparsity inherent in electronic correlation. We introduce a novel "tensor-of-tensors" data structure designed to manage the irregular sparsity patterns of Pair Natural Orbital (PNO) formulations. This development enables the first fully automated implementation of PNO-Coupled Cluster (PNO-CC) methods, bridging the gap between symbolic abstraction and the runtime requirements of sparse tensor algebra. The results demonstrate that SeQuant not only reproduces established dense methods (such as CCSD and CCSDT) with high fidelity but also effectively handles the complexity of sparse, local correlation approaches. By decoupling the complexity of the physics from the details of the implementation, this framework establishes a new paradigm for method development, dramatically accelerating the translation of theoretical insights into computational reality.","abstract_has_math":false,"creators":["Gaudel, Bimal"],"institution":"Virginia Tech","degree_name":"Doctor of Philosophy","degree_level":"doctoral","degree_discipline":"Chemistry","degree_department":"Chemistry","school":null,"contributors":[],"advisors":[],"committee_chairs":["Valeyev, Eduard Faritovich"],"committee_members":["Troya, Diego","Mayhall, Nicholas","Crawford, Thomas Daniel"],"year":2026,"date_issued":"2026-02-04","date_published":"2026-02-04","updated_at":"2026-07-22T22:19:07Z","subjects":["automated method development","electronic structure"],"languages":["en"],"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:45611"],"render_values":[{"text":"vt_gsexam:45611","href":null,"code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10919/141157","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Valeyev, Eduard Faritovich"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Troya, Diego","Mayhall, Nicholas","Crawford, Thomas Daniel"]},{"key":"dc:contributor.department","label":"Department","values":["Chemistry"]},{"key":"dc:creator","label":"Author","values":["Gaudel, Bimal"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-02-05T09:00:12Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2026-02-05T09:00:12Z"]},{"key":"dc:date.issued","label":"Date","values":["2026-02-04"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Chemistry"]},{"key":"thesis:degree_level","label":"Degree Level","values":["doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["automated method development","electronic structure"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:45611"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10919/141157"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The continuous demand for higher accuracy in computational chemistry necessitates the development of advanced many-body electronic structure methods. However, the derivation and efficient implementation of these theories constitute a significant bottleneck. As the rank of the associated tensors increases, the governing equations explode in complexity, rendering manual implementation labor-intensive, error-prone, and difficult to optimize for modern hardware. To address this challenge, this dissertation presents SeQuant, a comprehensive framework for the automated derivation and parallel implementation of many-body quantum chemistry methods. Built upon a robust symbolic algebra engine, SeQuant allows for the expression of theories in the natural language of second quantization. It automates the transformation of high-level theoretical ansatzes into explicit tensor contraction expressions and subsequently generates optimized, high-performance C++ code. A central innovation of this work is the extension of automated implementation to reduced-scaling methods, which exploit the sparsity inherent in electronic correlation. We introduce a novel \"tensor-of-tensors\" data structure designed to manage the irregular sparsity patterns of Pair Natural Orbital (PNO) formulations. This development enables the first fully automated implementation of PNO-Coupled Cluster (PNO-CC) methods, bridging the gap between symbolic abstraction and the runtime requirements of sparse tensor algebra. The results demonstrate that SeQuant not only reproduces established dense methods (such as CCSD and CCSDT) with high fidelity but also effectively handles the complexity of sparse, local correlation approaches. By decoupling the complexity of the physics from the details of the implementation, this framework establishes a new paradigm for method development, dramatically accelerating the translation of theoretical insights into computational reality."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["Computers are essential tools for understanding the microscopic world. By simulating how electrons interact within molecules, scientists can design new drugs, efficient solar cells, and advanced materials without needing to run expensive or dangerous experiments in a laboratory. However, to achieve the high accuracy required for these predictions, researchers must solve incredibly complex mathematical equations. Traditionally, translating these equations into computer software is a slow and painful process. A single new scientific method can take a human researcher years to derive and program manually. The mathematics are so intricate that the risk of human error is high, and the resulting computer programs are often difficult to optimize for modern supercomputers. This \"human bottleneck\" significantly slows down the pace of scientific innovation. In this research, I developed a software framework called `SeQuant` that acts as a \"translator\" between complex physics and high-performance computing. Instead of writing code line-by-line, a researcher simply describes the physical theory, and my software automatically handles the complex mathematics and generates the computer code needed to run the simulation. I successfully used this tool to create advanced simulation methods that are both accurate and efficient, specifically focusing on techniques that ignore irrelevant interactions to save computing power. By automating the most difficult parts of software development, this work allows scientists to test new ideas in days rather than years, accelerating the discovery of solutions to global challenges in chemistry and physics."]},{"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":["Automated Implementation of Advanced Electronic Structure Methods"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Valeyev, Eduard Faritovich"],"dc:contributor.committeemember":["Troya, Diego","Mayhall, Nicholas","Crawford, Thomas Daniel"],"dc:contributor.department":["Chemistry"],"dc:creator":["Gaudel, Bimal"],"dc:date.accessioned":["2026-02-05T09:00:12Z"],"dc:date.available":["2026-02-05T09:00:12Z"],"dc:date.issued":["2026-02-04"],"dc:description.abstract":["The continuous demand for higher accuracy in computational chemistry necessitates the development of advanced many-body electronic structure methods. However, the derivation and efficient implementation of these theories constitute a significant bottleneck. As the rank of the associated tensors increases, the governing equations explode in complexity, rendering manual implementation labor-intensive, error-prone, and difficult to optimize for modern hardware. To address this challenge, this dissertation presents SeQuant, a comprehensive framework for the automated derivation and parallel implementation of many-body quantum chemistry methods. Built upon a robust symbolic algebra engine, SeQuant allows for the expression of theories in the natural language of second quantization. It automates the transformation of high-level theoretical ansatzes into explicit tensor contraction expressions and subsequently generates optimized, high-performance C++ code. A central innovation of this work is the extension of automated implementation to reduced-scaling methods, which exploit the sparsity inherent in electronic correlation. We introduce a novel \"tensor-of-tensors\" data structure designed to manage the irregular sparsity patterns of Pair Natural Orbital (PNO) formulations. This development enables the first fully automated implementation of PNO-Coupled Cluster (PNO-CC) methods, bridging the gap between symbolic abstraction and the runtime requirements of sparse tensor algebra. The results demonstrate that SeQuant not only reproduces established dense methods (such as CCSD and CCSDT) with high fidelity but also effectively handles the complexity of sparse, local correlation approaches. By decoupling the complexity of the physics from the details of the implementation, this framework establishes a new paradigm for method development, dramatically accelerating the translation of theoretical insights into computational reality."],"dc:description.abstractgeneral":["Computers are essential tools for understanding the microscopic world. By simulating how electrons interact within molecules, scientists can design new drugs, efficient solar cells, and advanced materials without needing to run expensive or dangerous experiments in a laboratory. However, to achieve the high accuracy required for these predictions, researchers must solve incredibly complex mathematical equations. Traditionally, translating these equations into computer software is a slow and painful process. A single new scientific method can take a human researcher years to derive and program manually. The mathematics are so intricate that the risk of human error is high, and the resulting computer programs are often difficult to optimize for modern supercomputers. This \"human bottleneck\" significantly slows down the pace of scientific innovation. In this research, I developed a software framework called `SeQuant` that acts as a \"translator\" between complex physics and high-performance computing. Instead of writing code line-by-line, a researcher simply describes the physical theory, and my software automatically handles the complex mathematics and generates the computer code needed to run the simulation. I successfully used this tool to create advanced simulation methods that are both accurate and efficient, specifically focusing on techniques that ignore irrelevant interactions to save computing power. By automating the most difficult parts of software development, this work allows scientists to test new ideas in days rather than years, accelerating the discovery of solutions to global challenges in chemistry and physics."],"dc:description.degree":["Doctor of Philosophy"],"dc:format.medium":["ETD"],"dc:identifier.other":["vt_gsexam:45611"],"dc:identifier.uri":["https://hdl.handle.net/10919/141157"],"dc:language.iso":["en"],"dc:publisher":["Virginia Tech"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["automated method development","electronic structure"],"dc:title":["Automated Implementation of Advanced Electronic Structure Methods"],"dc:type":["Dissertation"],"thesis:degree_discipline":["Chemistry"],"thesis:degree_level":["doctoral"],"thesis:degree_name":["Doctor of Philosophy"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:19:07Z"}