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Towards Realizing Computationally Driven Experimental Discovery of Heterogeneous Catalysts
Towards Realizing Computationally Driven Experimental Discovery of Heterogeneous Catalysts
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211153031
- ISBN
- 9798346762744
- DDC
- 660
- 저자명
- Wander, Brook.
- 서명/저자
- Towards Realizing Computationally Driven Experimental Discovery of Heterogeneous Catalysts
- 발행사항
- [Sl] : Carnegie Mellon University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 148 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-06, Section: B.
- 주기사항
- Advisor: Kitchin, John R.
- 학위논문주기
- Thesis (Ph.D.)--Carnegie Mellon University, 2024.
- 초록/해제
- 요약The vast amount of anthropogenic carbon dioxide emissions have pushed our planet into an era of climate instability. As we look to recast our chemical and energy infrastructures to mitigate further problems, there is a need to accelerate the discovery of novel catalysts. Catalysts enable the efficient transformation of energy and chemicals, so as we redesign the methods by which energy and chemicals are accessed, we also need to redesign the catalysts that support these processes. Today, the use of computational approaches to aid in the discovery of catalysts is primarily limited to explaining trends and phenomena observed experimentally. This is because there are many gaps between what is treated computationally and what is realized experimentally. Although large gaps still exist, over the course of this dissertation four projects are described which seek to make computationally driven experimental catalyst discovery a reality. Firstly, we present catlas, an open-source framework that automates the process of performing computational materials screenings using machine learning. Secondly, we present CatTSunami, which allows access to transition states at high accuracy with significantly reduced cost. Transition state energies determine the rate of reaction, which is paramount for catalysis. Next, we explored the usefulness of machine learned potentials for determining the potential energy Hessian. Hessians enable the determination of Gibbs free energies, which are of consequence when considering thermodynamic favorability and reaction rate. Finally, as the culmination of these efforts, we sought to build models capable of predicting experimental production rates using computational features for the electrochemical reduction of CO2 and the hydrogen evolution reaction, important green chemistries.
- 일반주제명
- Chemical engineering
- 일반주제명
- Materials science
- 일반주제명
- Computational chemistry
- 키워드
- Carbon dioxide
- 키워드
- Machine learning
- 키워드
- Energy Hessian
- 기타저자
- Carnegie Mellon University Chemical Engineering
- 기본자료저록
- Dissertations Abstracts International. 86-06B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798346762744
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■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a660
■1001 ▼aWander, Brook.▼0(orcid)0000-0001-5028-5479
■24510▼aTowards Realizing Computationally Driven Experimental Discovery of Heterogeneous Catalysts
■260 ▼a[Sl]▼bCarnegie Mellon University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a148 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-06, Section: B.
■500 ▼aAdvisor: Kitchin, John R.
■5021 ▼aThesis (Ph.D.)--Carnegie Mellon University, 2024.
■520 ▼aThe vast amount of anthropogenic carbon dioxide emissions have pushed our planet into an era of climate instability. As we look to recast our chemical and energy infrastructures to mitigate further problems, there is a need to accelerate the discovery of novel catalysts. Catalysts enable the efficient transformation of energy and chemicals, so as we redesign the methods by which energy and chemicals are accessed, we also need to redesign the catalysts that support these processes. Today, the use of computational approaches to aid in the discovery of catalysts is primarily limited to explaining trends and phenomena observed experimentally. This is because there are many gaps between what is treated computationally and what is realized experimentally. Although large gaps still exist, over the course of this dissertation four projects are described which seek to make computationally driven experimental catalyst discovery a reality. Firstly, we present catlas, an open-source framework that automates the process of performing computational materials screenings using machine learning. Secondly, we present CatTSunami, which allows access to transition states at high accuracy with significantly reduced cost. Transition state energies determine the rate of reaction, which is paramount for catalysis. Next, we explored the usefulness of machine learned potentials for determining the potential energy Hessian. Hessians enable the determination of Gibbs free energies, which are of consequence when considering thermodynamic favorability and reaction rate. Finally, as the culmination of these efforts, we sought to build models capable of predicting experimental production rates using computational features for the electrochemical reduction of CO2 and the hydrogen evolution reaction, important green chemistries.
■590 ▼aSchool code: 0041.
■650 4▼aChemical engineering
■650 4▼aMaterials science
■650 4▼aComputational chemistry
■653 ▼aCarbon dioxide
■653 ▼aMachine learning
■653 ▼aElectrochemical reduction
■653 ▼aEnergy Hessian
■653 ▼aComputational materials
■690 ▼a0542
■690 ▼a0794
■690 ▼a0800
■690 ▼a0219
■71020▼aCarnegie Mellon University▼bChemical Engineering.
■7730 ▼tDissertations Abstracts International▼g86-06B.
■790 ▼a0041
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17164685▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
