서브메뉴
검색
Accelerating Electronic Structure Calculations With Machine Learning- [electronic resource]
Accelerating Electronic Structure Calculations With Machine Learning- [electronic resource]
상세정보
- 자료유형
- 학위논문파일 국외
- 최종처리일시
- 20240214101658
- ISBN
- 9798380366595
- DDC
- 004
- 서명/저자
- Accelerating Electronic Structure Calculations With Machine Learning - [electronic resource]
- 발행사항
- [S.l.]: : University of California, Berkeley., 2023
- 발행사항
- Ann Arbor : : ProQuest Dissertations & Theses,, 2023
- 형태사항
- 1 online resource(54 p.)
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-03, Section: B.
- 주기사항
- Advisor: Gonzalez, Joseph.
- 학위논문주기
- Thesis (Ph.D.)--University of California, Berkeley, 2023.
- 사용제한주기
- This item must not be sold to any third party vendors.
- 초록/해제
- 요약New chemicals and new materials have transformed modern life: pharmaceuticals, pesticides, surfactants, alloys, catalysts, polymers, battery electrodes, and countless other materials play critical roles in healthcare, construction, energy, and other wide-ranging industries. New materials are not generally stumbled upon by happenstance, but rather are discovered through a long process that involves extensive physics-based computer simulations at the atomic level. Electronic structure calculations play an important role in the discovery process, but they can be extremely computationally expensive. As such, there is a long history of approximation methods that trade off speed and accuracy.Machine learning has the potential to open a new frontier on this speed-accuracy trade-off, and in doing so, significantly accelerate discovery of new materials. In this dissertation, we first cover the quantum mechanical background necessary to understand the problem setting, written with the machine learning community in mind as the audience. Next, we survey the learning-based methods that are pushing the speed-accuracy frontier, along with some foundational non-learning-based methods. Lastly, we investigate self-supervised learning as a mechanism for understanding the shape of the potential energy surface without expensive-to-obtain supervision on energies and forces.
- 일반주제명
- Computer science.
- 키워드
- Machine learning
- 키워드
- New chemicals
- 키워드
- New materials
- 기타저자
- University of California, Berkeley Electrical Engineering & Computer Sciences
- 기본자료저록
- Dissertations Abstracts International. 85-03B.
- 기본자료저록
- Dissertation Abstract International
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
008240612s2023 us |||||||||||||||c||eng d■001000016934823
■00520240214101658
■006m o d
■007cr#unu||||||||
■020 ▼a9798380366595
■035 ▼a(MiAaPQ)AAI30635217
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a004
■1001 ▼aRothchild, Daniel.
■24510▼aAccelerating Electronic Structure Calculations With Machine Learning▼h[electronic resource]
■260 ▼a[S.l.]:▼bUniversity of California, Berkeley. ▼c2023
■260 1▼aAnn Arbor :▼bProQuest Dissertations & Theses, ▼c2023
■300 ▼a1 online resource(54 p.)
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-03, Section: B.
■500 ▼aAdvisor: Gonzalez, Joseph.
■5021 ▼aThesis (Ph.D.)--University of California, Berkeley, 2023.
■506 ▼aThis item must not be sold to any third party vendors.
■520 ▼aNew chemicals and new materials have transformed modern life: pharmaceuticals, pesticides, surfactants, alloys, catalysts, polymers, battery electrodes, and countless other materials play critical roles in healthcare, construction, energy, and other wide-ranging industries. New materials are not generally stumbled upon by happenstance, but rather are discovered through a long process that involves extensive physics-based computer simulations at the atomic level. Electronic structure calculations play an important role in the discovery process, but they can be extremely computationally expensive. As such, there is a long history of approximation methods that trade off speed and accuracy.Machine learning has the potential to open a new frontier on this speed-accuracy trade-off, and in doing so, significantly accelerate discovery of new materials. In this dissertation, we first cover the quantum mechanical background necessary to understand the problem setting, written with the machine learning community in mind as the audience. Next, we survey the learning-based methods that are pushing the speed-accuracy frontier, along with some foundational non-learning-based methods. Lastly, we investigate self-supervised learning as a mechanism for understanding the shape of the potential energy surface without expensive-to-obtain supervision on energies and forces.
■590 ▼aSchool code: 0028.
■650 4▼aComputer science.
■653 ▼aMachine learning
■653 ▼aNew chemicals
■653 ▼aNew materials
■653 ▼aElectronic structure
■690 ▼a0984
■690 ▼a0800
■71020▼aUniversity of California, Berkeley▼bElectrical Engineering & Computer Sciences.
■7730 ▼tDissertations Abstracts International▼g85-03B.
■773 ▼tDissertation Abstract International
■790 ▼a0028
■791 ▼aPh.D.
■792 ▼a2023
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T16934823▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
■980 ▼a202402▼f2024
