{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/137807"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/137807","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Engineering the future of Hybrid Materials with Coarse-Grained Molecular Dynamics","abstract":"The rapid evolution of hybrid materials has opened new frontiers in materials science by combining the distinct properties of polymers, nanoparticles, ceramics, metal oxides, and biomolecules to create adaptable, high-performance systems tailored to complex applications. These materials are critical in areas such as energy storage, environmental sustainability, and biomedical engineering, where conventional single-component materials often fall short of the required versatility and performance. A key tool for understanding and designing these complex materials is coarse-grained (CG) molecular dynamics (MD) which allows researchers to capture molecular-level interactions while efficiently modeling larger, structurally diverse systems over longer timescales, bridging the gap between atomic-level insights and macroscopic material properties. This work advances CG MD methodologies by developing chemically mapped, transferable models tailored to a broad library of polymers, lipids, and biomolecules. These models provide predictive insights into the behavior of hybrid systems, focusing on critical transitions and structural responses to external stimuli. For example, the simulation of thermosensitive polymers such as poly(N-isopropylacrylamide) (PNIPAM) reveals key mechanisms underlying coil-to-globule transitions and self-assembly behaviors, which are essential for applications in drug delivery and responsive materials. Our studies further address experimentally observed, complex behaviors in hybrid materials, such as carbohydrate-protein binding dynamics in glycopolymers, by using CG MD simulations to decode structure-function relationships that govern molecular recognition and interaction efficiency. These simulations showcase the significance of factors such as glycan density in enhancing solvent accessibility, which directly impacts binding affinities and bioactivity. In parallel, CG model development efforts are directed at physically representing lipid bilayer systems, facilitating the simulation of cellular interfaces and hybrid membrane systems. By optimizing parameters with advanced techniques like particle swarm optimization (PSO), these CG models replicate key experimental properties such as bilayer thickness, area per lipid, and bending rigidity, ensuring transferability across diverse conditions. Collectively, this dissertation showcases the strategic development and application of CG MD models for hybrid materials, highlighting the ability of molecular simulations to guide the rational design of materials with tailored functionalities. The methodologies established here lay the groundwork for next-generation CG MD studies, aiming to bridge experimental findings with computational insights to drive innovation in hybrid material science.","abstract_html":"The rapid evolution of hybrid materials has opened new frontiers in materials science by combining the distinct properties of polymers, nanoparticles, ceramics, metal oxides, and biomolecules to create adaptable, high-performance systems tailored to complex applications. These materials are critical in areas such as energy storage, environmental sustainability, and biomedical engineering, where conventional single-component materials often fall short of the required versatility and performance. A key tool for understanding and designing these complex materials is coarse-grained (CG) molecular dynamics (MD) which allows researchers to capture molecular-level interactions while efficiently modeling larger, structurally diverse systems over longer timescales, bridging the gap between atomic-level insights and macroscopic material properties. This work advances CG MD methodologies by developing chemically mapped, transferable models tailored to a broad library of polymers, lipids, and biomolecules. These models provide predictive insights into the behavior of hybrid systems, focusing on critical transitions and structural responses to external stimuli. For example, the simulation of thermosensitive polymers such as poly(N-isopropylacrylamide) (PNIPAM) reveals key mechanisms underlying coil-to-globule transitions and self-assembly behaviors, which are essential for applications in drug delivery and responsive materials. Our studies further address experimentally observed, complex behaviors in hybrid materials, such as carbohydrate-protein binding dynamics in glycopolymers, by using CG MD simulations to decode structure-function relationships that govern molecular recognition and interaction efficiency. These simulations showcase the significance of factors such as glycan density in enhancing solvent accessibility, which directly impacts binding affinities and bioactivity. In parallel, CG model development efforts are directed at physically representing lipid bilayer systems, facilitating the simulation of cellular interfaces and hybrid membrane systems. By optimizing parameters with advanced techniques like particle swarm optimization (PSO), these CG models replicate key experimental properties such as bilayer thickness, area per lipid, and bending rigidity, ensuring transferability across diverse conditions. Collectively, this dissertation showcases the strategic development and application of CG MD models for hybrid materials, highlighting the ability of molecular simulations to guide the rational design of materials with tailored functionalities. The methodologies established here lay the groundwork for next-generation CG MD studies, aiming to bridge experimental findings with computational insights to drive innovation in hybrid material science.","abstract_has_math":false,"creators":["Joshi, Soumil Yogesh"],"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":["Deshmukh, Sanket A."],"committee_members":["Whittington, Abby Rebecca","Achenie, Luke E. 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These materials are critical in areas such as energy storage, environmental sustainability, and biomedical engineering, where conventional single-component materials often fall short of the required versatility and performance. A key tool for understanding and designing these complex materials is coarse-grained (CG) molecular dynamics (MD) which allows researchers to capture molecular-level interactions while efficiently modeling larger, structurally diverse systems over longer timescales, bridging the gap between atomic-level insights and macroscopic material properties. This work advances CG MD methodologies by developing chemically mapped, transferable models tailored to a broad library of polymers, lipids, and biomolecules. These models provide predictive insights into the behavior of hybrid systems, focusing on critical transitions and structural responses to external stimuli. For example, the simulation of thermosensitive polymers such as poly(N-isopropylacrylamide) (PNIPAM) reveals key mechanisms underlying coil-to-globule transitions and self-assembly behaviors, which are essential for applications in drug delivery and responsive materials. Our studies further address experimentally observed, complex behaviors in hybrid materials, such as carbohydrate-protein binding dynamics in glycopolymers, by using CG MD simulations to decode structure-function relationships that govern molecular recognition and interaction efficiency. These simulations showcase the significance of factors such as glycan density in enhancing solvent accessibility, which directly impacts binding affinities and bioactivity. In parallel, CG model development efforts are directed at physically representing lipid bilayer systems, facilitating the simulation of cellular interfaces and hybrid membrane systems. By optimizing parameters with advanced techniques like particle swarm optimization (PSO), these CG models replicate key experimental properties such as bilayer thickness, area per lipid, and bending rigidity, ensuring transferability across diverse conditions. Collectively, this dissertation showcases the strategic development and application of CG MD models for hybrid materials, highlighting the ability of molecular simulations to guide the rational design of materials with tailored functionalities. The methodologies established here lay the groundwork for next-generation CG MD studies, aiming to bridge experimental findings with computational insights to drive innovation in hybrid material science."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["Hybrid materials – systems that combine polymers, biomolecules, and other components – are transforming medicine, clean water technologies, and sustainable materials by offering properties no single material can achieve alone. Designing these systems requires understanding how their molecular components interact and respond to environmental changes, a challenge often difficult to address through experiments alone. This dissertation uses a computational method called \"coarse-graining,\" which simplifies complex molecules into representative models, enabling efficient simulations of their collective behavior over long timescales. Through these coarse-grained simulations, transferable models were developed or applied for key molecular building blocks, including thermoresponsive polymers, glycopolymers, and lipid membranes. These models were used to investigate how polymers change shape with temperature for potential drug delivery applications, how sugar-functionalized polymers engage proteins linked to disease, and how optimized membrane models can support the study of biological interfaces. Together, these studies show how molecular simulations can connect microscopic structure to macroscopic performance, providing predictive insights that guide the design of next-generation materials. The approaches established lay a foundation for innovation in biomedical engineering, environmental sustainability, and advanced materials science, contributing to technologies that improve health, resource management, and quality of life."]},{"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":["Engineering the future of Hybrid Materials with Coarse-Grained Molecular Dynamics"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Deshmukh, Sanket A."],"dc:contributor.committeemember":["Whittington, Abby Rebecca","Achenie, Luke E. 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A key tool for understanding and designing these complex materials is coarse-grained (CG) molecular dynamics (MD) which allows researchers to capture molecular-level interactions while efficiently modeling larger, structurally diverse systems over longer timescales, bridging the gap between atomic-level insights and macroscopic material properties. This work advances CG MD methodologies by developing chemically mapped, transferable models tailored to a broad library of polymers, lipids, and biomolecules. These models provide predictive insights into the behavior of hybrid systems, focusing on critical transitions and structural responses to external stimuli. For example, the simulation of thermosensitive polymers such as poly(N-isopropylacrylamide) (PNIPAM) reveals key mechanisms underlying coil-to-globule transitions and self-assembly behaviors, which are essential for applications in drug delivery and responsive materials. Our studies further address experimentally observed, complex behaviors in hybrid materials, such as carbohydrate-protein binding dynamics in glycopolymers, by using CG MD simulations to decode structure-function relationships that govern molecular recognition and interaction efficiency. These simulations showcase the significance of factors such as glycan density in enhancing solvent accessibility, which directly impacts binding affinities and bioactivity. In parallel, CG model development efforts are directed at physically representing lipid bilayer systems, facilitating the simulation of cellular interfaces and hybrid membrane systems. By optimizing parameters with advanced techniques like particle swarm optimization (PSO), these CG models replicate key experimental properties such as bilayer thickness, area per lipid, and bending rigidity, ensuring transferability across diverse conditions. Collectively, this dissertation showcases the strategic development and application of CG MD models for hybrid materials, highlighting the ability of molecular simulations to guide the rational design of materials with tailored functionalities. The methodologies established here lay the groundwork for next-generation CG MD studies, aiming to bridge experimental findings with computational insights to drive innovation in hybrid material science."],"dc:description.abstractgeneral":["Hybrid materials – systems that combine polymers, biomolecules, and other components – are transforming medicine, clean water technologies, and sustainable materials by offering properties no single material can achieve alone. Designing these systems requires understanding how their molecular components interact and respond to environmental changes, a challenge often difficult to address through experiments alone. This dissertation uses a computational method called \"coarse-graining,\" which simplifies complex molecules into representative models, enabling efficient simulations of their collective behavior over long timescales. Through these coarse-grained simulations, transferable models were developed or applied for key molecular building blocks, including thermoresponsive polymers, glycopolymers, and lipid membranes. These models were used to investigate how polymers change shape with temperature for potential drug delivery applications, how sugar-functionalized polymers engage proteins linked to disease, and how optimized membrane models can support the study of biological interfaces. Together, these studies show how molecular simulations can connect microscopic structure to macroscopic performance, providing predictive insights that guide the design of next-generation materials. 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