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Design of (De)polymerizable Polymers using Machine Learning-Based Predictive Models and Generative Algorithms
Design of (De)polymerizable Polymers using Machine Learning-Based Predictive Models and Generative Algorithms
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202105504
- ISBN
- 9798263325879
- DDC
- 547.14
- 저자명
- Kern, Joseph.
- 서명/저자
- Design of (De)polymerizable Polymers using Machine Learning-Based Predictive Models and Generative Algorithms
- 발행사항
- [Sl] : Georgia Institute of Technology, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 271 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-05, Section: A.
- 주기사항
- Advisor: Ramprasad, Rampi.
- 학위논문주기
- Thesis (Ph.D.)--Georgia Institute of Technology, 2024.
- 초록/해제
- 요약Plastics stand as one of the most ubiquitous materials in modern society, with production exceeding a staggering 400 million metric tons in 2022 alone [1]. Regrettably, the inherent thermodynamic stability of the current generation of plastics poses a significant challenge, rendering many incapable of effective recycling. Consequently, these plastics persist as waste, potentially lingering for hundreds of years. Thus, there arises an urgent need for the development of novel plastics that not only satisfy the demands of diverse applications but also possess the crucial capability to be readily recycled. It is within this context that the primary objective of this research is situated: to identify promising candidates capable of replacing contemporary commodity plastics with chemically recyclable (depolymerizable) alternatives. To achieve this overarching goal, my work focused on several critical components:1.The development of an AI-enabled Virtual Forward Synthesis (VFS) platform aimed at generating novel plastics from existing molecules:This platform enables automated searches for molecules capable of facilitating ring-opening polymerization (ROP), as ROP polymers are recognized as promising candidates for depolymerizable designs due to their unique thermodynamics. Leveraging the power of machine learning (ML), the platform further predicts polymer properties and identifies promising candidate molecules, thus paving the way for the discovery of recyclable plastic alternatives.2.The creation of a genetic algorithm to rapidly explore polymer design spaces:This algorithm was crafted to specifically cater to ROP chemistries, marking a significant advancement over previous versions. It is capable of swiftly identifying promising candidates from innumerable search spaces, accomplishing this task with remarkable efficiency compared to enumerative design approaches.3.Advancement of best-in-class ML models for predicting solubility and toxicity:These factors wield substantial influence over polymer processing and the environmental toxicity associated with them. By harnessing the predictive capabilities of these models, promising polymer candidates can be further refined, thus honing in on better polymer designs.Employing these developed methods, a myriad of candidate polymers emerged as promising alternatives to one of the most ubiquitous commodity plastics, polystyrene (PS). Among these candidates, one is currently undergoing synthesis exploration by polymer chemists. Furthermore, invaluable insights were gleaned on the development of thermally and mechanically resilient polymers. Additionally, a plethora of software packages and models have been made readily accessible for use.
- 일반주제명
- Polymer solubility
- 일반주제명
- Recycling
- 일반주제명
- Toxicity
- 일반주제명
- Informatics
- 일반주제명
- Genetic algorithms
- 일반주제명
- Polymerization
- 일반주제명
- Polymer chemistry
- 일반주제명
- Sustainability
- 일반주제명
- Toxicology
- 기본자료저록
- Dissertations Abstracts International. 87-05A.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798263325879
■035 ▼a(MiAaPQ)AAI32307972
■035 ▼a(MiAaPQ)GeorgiaTech78557
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a547.14
■1001 ▼aKern, Joseph.
■24510▼aDesign of (De)polymerizable Polymers using Machine Learning-Based Predictive Models and Generative Algorithms
■260 ▼a[Sl]▼bGeorgia Institute of Technology▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a271 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-05, Section: A.
■500 ▼aAdvisor: Ramprasad, Rampi.
■5021 ▼aThesis (Ph.D.)--Georgia Institute of Technology, 2024.
■520 ▼aPlastics stand as one of the most ubiquitous materials in modern society, with production exceeding a staggering 400 million metric tons in 2022 alone [1]. Regrettably, the inherent thermodynamic stability of the current generation of plastics poses a significant challenge, rendering many incapable of effective recycling. Consequently, these plastics persist as waste, potentially lingering for hundreds of years. Thus, there arises an urgent need for the development of novel plastics that not only satisfy the demands of diverse applications but also possess the crucial capability to be readily recycled. It is within this context that the primary objective of this research is situated: to identify promising candidates capable of replacing contemporary commodity plastics with chemically recyclable (depolymerizable) alternatives. To achieve this overarching goal, my work focused on several critical components:1.The development of an AI-enabled Virtual Forward Synthesis (VFS) platform aimed at generating novel plastics from existing molecules:This platform enables automated searches for molecules capable of facilitating ring-opening polymerization (ROP), as ROP polymers are recognized as promising candidates for depolymerizable designs due to their unique thermodynamics. Leveraging the power of machine learning (ML), the platform further predicts polymer properties and identifies promising candidate molecules, thus paving the way for the discovery of recyclable plastic alternatives.2.The creation of a genetic algorithm to rapidly explore polymer design spaces:This algorithm was crafted to specifically cater to ROP chemistries, marking a significant advancement over previous versions. It is capable of swiftly identifying promising candidates from innumerable search spaces, accomplishing this task with remarkable efficiency compared to enumerative design approaches.3.Advancement of best-in-class ML models for predicting solubility and toxicity:These factors wield substantial influence over polymer processing and the environmental toxicity associated with them. By harnessing the predictive capabilities of these models, promising polymer candidates can be further refined, thus honing in on better polymer designs.Employing these developed methods, a myriad of candidate polymers emerged as promising alternatives to one of the most ubiquitous commodity plastics, polystyrene (PS). Among these candidates, one is currently undergoing synthesis exploration by polymer chemists. Furthermore, invaluable insights were gleaned on the development of thermally and mechanically resilient polymers. Additionally, a plethora of software packages and models have been made readily accessible for use.
■590 ▼aSchool code: 0078.
■650 4▼aPolymer solubility
■650 4▼aRecycling
■650 4▼aToxicity
■650 4▼aInformatics
■650 4▼aGenetic algorithms
■650 4▼aPolymerization
■650 4▼aPolymer chemistry
■650 4▼aSustainability
■650 4▼aToxicology
■690 ▼a0800
■690 ▼a0495
■690 ▼a0640
■690 ▼a0383
■71020▼aGeorgia Institute of Technology.
■7730 ▼tDissertations Abstracts International▼g87-05A.
■790 ▼a0078
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17360304▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
