서브메뉴
검색
Machine Learning for Material Property Prediction and Analysis- [electronic resource]
![Machine Learning for Material Property Prediction and Analysis - [electronic resource]](/Users/Baul/Images/book.png)
Machine Learning for Material Property Prediction and Analysis- [electronic resource]
상세정보
- 자료유형
- 학위논문파일 국외
- 최종처리일시
- 20240214101242
- ISBN
- 9798380092876
- DDC
- 621
- 서명/저자
- Machine Learning for Material Property Prediction and Analysis - [electronic resource]
- 발행사항
- [S.l.]: : Carnegie Mellon University., 2023
- 발행사항
- Ann Arbor : : ProQuest Dissertations & Theses,, 2023
- 형태사항
- 1 online resource(153 p.)
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-02, Section: B.
- 주기사항
- Advisor: Barati Farimani, Amir.
- 학위논문주기
- Thesis (Ph.D.)--Carnegie Mellon University, 2023.
- 사용제한주기
- This item must not be sold to any third party vendors.
- 초록/해제
- 요약The discovery of novel materials can significantly impact the quality of human life on the planet and make it more sustainable. Furthermore, novel materials can play a crucial role in technological advancements and enable innovations in industries such as healthcare and energy. However, the quest for new material discovery involves parsing the vast chemical space of materials. Navigating this vast chemical landscape to identify and synthesize materials with desirable properties is a big challenge. Considering the large scale of chemical space, developing advanced computational methods that can access this space effectively is very important. Computational methods that can accurately predict material properties can especially be leveraged for new material development. Accurate predictions can lead to the discovery of new materials with desirable properties for specific applications and reduce the time and cost associated with new material development. This dissertation investigates the potential of leveraging machine learning (ML) models as a more efficient computational tool for material property prediction and discovery.Traditionally, computational methods like Density Functional Theory (DFT) simulations have been used for high throughput screening and rapid prediction of material properties. Advancements in computational capabilities have enabled the large-scale usage of DFT simulations, yet parsing the entire chemical space and predicting the properties for all the possible materials is practically impossible due to the computational demands of these methods. Data-driven methods such as Machine Learning(ML) can learn patterns in the data and can be a viable alternative for DFT simulations. In general, ML models learn the material representations which can be leveraged to predict material properties at a fractional computational cost compared to DFT. Consequently, these models can be used as a screening tool to identify promising novel materials in the enormous chemical space which can then be further validated through experiments or DFT simulations.This dissertation explores various ML paradigms for material property prediction, encompassing Graph Neural Networks, Self-Supervised Representation Learning, Pretraining Strategies for Structure Agnostic Representation Learning, and Large Language Models. The primary aim of this work is to demonstrate the potential of ML models in accurate material property prediction and, as a result, catalyze novel material discovery through the application of machine learning.
- 일반주제명
- Mechanical engineering.
- 일반주제명
- Materials science.
- 키워드
- Machine Learning
- 기타저자
- Carnegie Mellon University Mechanical Engineering
- 기본자료저록
- Dissertations Abstracts International. 85-02B.
- 기본자료저록
- Dissertation Abstract International
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
008240612s2023 us |||||||||||||||c||eng d■001000016933400
■00520240214101242
■006m o d
■007cr#unu||||||||
■020 ▼a9798380092876
■035 ▼a(MiAaPQ)AAI30528636
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a621
■1001 ▼aMagar, Rishikesh.▼0(orcid)0000-0001-6216-0518
■24510▼aMachine Learning for Material Property Prediction and Analysis▼h[electronic resource]
■260 ▼a[S.l.]:▼bCarnegie Mellon University. ▼c2023
■260 1▼aAnn Arbor :▼bProQuest Dissertations & Theses, ▼c2023
■300 ▼a1 online resource(153 p.)
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-02, Section: B.
■500 ▼aAdvisor: Barati Farimani, Amir.
■5021 ▼aThesis (Ph.D.)--Carnegie Mellon University, 2023.
■506 ▼aThis item must not be sold to any third party vendors.
■520 ▼aThe discovery of novel materials can significantly impact the quality of human life on the planet and make it more sustainable. Furthermore, novel materials can play a crucial role in technological advancements and enable innovations in industries such as healthcare and energy. However, the quest for new material discovery involves parsing the vast chemical space of materials. Navigating this vast chemical landscape to identify and synthesize materials with desirable properties is a big challenge. Considering the large scale of chemical space, developing advanced computational methods that can access this space effectively is very important. Computational methods that can accurately predict material properties can especially be leveraged for new material development. Accurate predictions can lead to the discovery of new materials with desirable properties for specific applications and reduce the time and cost associated with new material development. This dissertation investigates the potential of leveraging machine learning (ML) models as a more efficient computational tool for material property prediction and discovery.Traditionally, computational methods like Density Functional Theory (DFT) simulations have been used for high throughput screening and rapid prediction of material properties. Advancements in computational capabilities have enabled the large-scale usage of DFT simulations, yet parsing the entire chemical space and predicting the properties for all the possible materials is practically impossible due to the computational demands of these methods. Data-driven methods such as Machine Learning(ML) can learn patterns in the data and can be a viable alternative for DFT simulations. In general, ML models learn the material representations which can be leveraged to predict material properties at a fractional computational cost compared to DFT. Consequently, these models can be used as a screening tool to identify promising novel materials in the enormous chemical space which can then be further validated through experiments or DFT simulations.This dissertation explores various ML paradigms for material property prediction, encompassing Graph Neural Networks, Self-Supervised Representation Learning, Pretraining Strategies for Structure Agnostic Representation Learning, and Large Language Models. The primary aim of this work is to demonstrate the potential of ML models in accurate material property prediction and, as a result, catalyze novel material discovery through the application of machine learning.
■590 ▼aSchool code: 0041.
■650 4▼aMechanical engineering.
■650 4▼aMaterials science.
■653 ▼aMachine Learning
■653 ▼aMaterial property prediction
■653 ▼aSelf-supervised learning
■653 ▼aDensity Functional Theory
■653 ▼aComputational methods
■653 ▼aComputational tool
■690 ▼a0800
■690 ▼a0794
■690 ▼a0548
■71020▼aCarnegie Mellon University▼bMechanical Engineering.
■7730 ▼tDissertations Abstracts International▼g85-02B.
■773 ▼tDissertation Abstract International
■790 ▼a0041
■791 ▼aPh.D.
■792 ▼a2023
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T16933400▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
■980 ▼a202402▼f2024
