{"id":{"repo_id":"penn","oai_identifier":"oai:repository.upenn.edu:20.500.14332/62791"},"canonical_url":"https://search.dev.ndltd.org/etd/penn/oai:repository.upenn.edu:20.500.14332/62791","repository":{"repo_id":"penn","name":"University of Pennsylvania","base_url":"https://repository.upenn.edu/server/oai/request"},"display":{"title":"UNCOVERING FUNDAMENTAL PHYSICS OF ENERGY MATERIALS WITH QUANTUM MECHANICS, MOLECULAR DYNAMICS, AND APPLIED MACHINE LEARNING","abstract":"The preservation, manipulation, and conversion of energy is one of the greatest challenges of the 21st century. One of the most powerful tools we have to control it is to improve electronics and catalysts with materials science and engineering. Experimental techniques alone are not sufficient to reliably and consistently improve modern devices; theoretical modeling is required to establish structure-property relationships in materials to understand how they work and ultimately to find new compositions. In this dissertation, we explore how modern advances in computation enables discovery and understanding of some of the most essential energy materials for future device advancement. We first leverage quantum mechanical simulations and machine learning algorithms to discover new materials for CO2 conversion and for improved sensors and communication devices. Next, we expand on these methods to develop a machine learned force field for large scale simulations of ferroelectric aluminum nitride. Using our state of the art potential, we uncover, for the first time, the mechanism for polarization reversal in this novel next-generation ferroelectric material. We then develop new kinetic theory to describe the uncovered anomalous switching behavior that can be expanded to other similar phenomena across disciplines. Overall, these reports serve to highlight how advancements in computer power and machine learning can advance material discovery and understanding in the pursuit of solving some of humanity's biggest energy challenges.","abstract_html":"The preservation, manipulation, and conversion of energy is one of the greatest challenges of the 21st century. One of the most powerful tools we have to control it is to improve electronics and catalysts with materials science and engineering. Experimental techniques alone are not sufficient to reliably and consistently improve modern devices; theoretical modeling is required to establish structure-property relationships in materials to understand how they work and ultimately to find new compositions. In this dissertation, we explore how modern advances in computation enables discovery and understanding of some of the most essential energy materials for future device advancement. We first leverage quantum mechanical simulations and machine learning algorithms to discover new materials for CO2 conversion and for improved sensors and communication devices. Next, we expand on these methods to develop a machine learned force field for large scale simulations of ferroelectric aluminum nitride. Using our state of the art potential, we uncover, for the first time, the mechanism for polarization reversal in this novel next-generation ferroelectric material. We then develop new kinetic theory to describe the uncovered anomalous switching behavior that can be expanded to other similar phenomena across disciplines. Overall, these reports serve to highlight how advancements in computer power and machine learning can advance material discovery and understanding in the pursuit of solving some of humanity's biggest energy challenges.","abstract_has_math":false,"creators":["Behrendt, Drew"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Rappe, Andrew, M"],"committee_chairs":[],"committee_members":[],"year":2026,"date_issued":"2026","date_published":"2026","updated_at":"2026-07-24T03:47:06Z","subjects":["Chemistry","Materials Engineering"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://repository.upenn.edu/handle/20.500.14332/62791","outbound_label":"Repository record","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Rappe, Andrew, M"]},{"key":"dc:creator","label":"Author","values":["Behrendt, Drew"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-06-05T16:15:26Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2026-06-05T16:15:26Z"]},{"key":"dc:date.issued","label":"Date","values":["2026"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation/Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Chemistry","Materials Engineering"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://repository.upenn.edu/handle/20.500.14332/62791"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["2026"]},{"key":"dc:description.abstract","label":"Abstract","values":["The preservation, manipulation, and conversion of energy is one of the greatest challenges of the 21st century. One of the most powerful tools we have to control it is to improve electronics and catalysts with materials science and engineering. Experimental techniques alone are not sufficient to reliably and consistently improve modern devices; theoretical modeling is required to establish structure-property relationships in materials to understand how they work and ultimately to find new compositions. In this dissertation, we explore how modern advances in computation enables discovery and understanding of some of the most essential energy materials for future device advancement. We first leverage quantum mechanical simulations and machine learning algorithms to discover new materials for CO2 conversion and for improved sensors and communication devices. Next, we expand on these methods to develop a machine learned force field for large scale simulations of ferroelectric aluminum nitride. Using our state of the art potential, we uncover, for the first time, the mechanism for polarization reversal in this novel next-generation ferroelectric material. We then develop new kinetic theory to describe the uncovered anomalous switching behavior that can be expanded to other similar phenomena across disciplines. Overall, these reports serve to highlight how advancements in computer power and machine learning can advance material discovery and understanding in the pursuit of solving some of humanity's biggest energy challenges."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["PhD"]},{"key":"dc:title","label":"Title","values":["UNCOVERING FUNDAMENTAL PHYSICS OF ENERGY MATERIALS WITH QUANTUM MECHANICS, MOLECULAR DYNAMICS, AND APPLIED MACHINE LEARNING"]}]}],"canonical_facts":{"dc:contributor.advisor":["Rappe, Andrew, M"],"dc:creator":["Behrendt, Drew"],"dc:date.accessioned":["2026-06-05T16:15:26Z"],"dc:date.available":["2026-06-05T16:15:26Z"],"dc:date.issued":["2026"],"dc:description":["2026"],"dc:description.abstract":["The preservation, manipulation, and conversion of energy is one of the greatest challenges of the 21st century. One of the most powerful tools we have to control it is to improve electronics and catalysts with materials science and engineering. Experimental techniques alone are not sufficient to reliably and consistently improve modern devices; theoretical modeling is required to establish structure-property relationships in materials to understand how they work and ultimately to find new compositions. In this dissertation, we explore how modern advances in computation enables discovery and understanding of some of the most essential energy materials for future device advancement. We first leverage quantum mechanical simulations and machine learning algorithms to discover new materials for CO2 conversion and for improved sensors and communication devices. Next, we expand on these methods to develop a machine learned force field for large scale simulations of ferroelectric aluminum nitride. Using our state of the art potential, we uncover, for the first time, the mechanism for polarization reversal in this novel next-generation ferroelectric material. We then develop new kinetic theory to describe the uncovered anomalous switching behavior that can be expanded to other similar phenomena across disciplines. Overall, these reports serve to highlight how advancements in computer power and machine learning can advance material discovery and understanding in the pursuit of solving some of humanity's biggest energy challenges."],"dc:description.degree":["PhD"],"dc:identifier.uri":["https://repository.upenn.edu/handle/20.500.14332/62791"],"dc:language.iso":["en"],"dc:subject":["Chemistry","Materials Engineering"],"dc:title":["UNCOVERING FUNDAMENTAL PHYSICS OF ENERGY MATERIALS WITH QUANTUM MECHANICS, MOLECULAR DYNAMICS, AND APPLIED MACHINE LEARNING"],"dc:type":["Dissertation/Thesis"]},"updated_at":"2026-07-24T03:47:06Z"}