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High-Throughput Computational Search for Li-Free Li-Ion Battery Cathodes
High-Throughput Computational Search for Li-Free Li-Ion Battery Cathodes
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202103540
- ISBN
- 9798288863646
- DDC
- 620.11
- 서명/저자
- High-Throughput Computational Search for Li-Free Li-Ion Battery Cathodes
- 발행사항
- [Sl] : University of California, Berkeley, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 55 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-01, Section: B.
- 주기사항
- Advisor: Persson, Kristin A.
- 학위논문주기
- Thesis (Ph.D.)--University of California, Berkeley, 2025.
- 초록/해제
- 요약As Li-metal anodes become more readily available, next-gen Li-ion battery cathodes are no longer required to contain Li in their as-synthesized state, vastly expanding the materials search space. In order to identify potential cathode materials that do not necessarily contain Li in their native state, I here present a series of tools and guidelines to carry out computational screening in this search space.Firstly, a pipeline for rapid cathode discovery has been established. This pipeline operates on any database of inorganic materials without a priori information on Li sites and performs screening based on computed voltage, capacity from sequential insertions of Li ions and most importantly, mobility built upon the graph-based migration network obtained through site connectivity. A preliminary application of the pipeline was carried out on a subset of the Materials Project database, and one particular polymorph of MnP2O7 is shown here as an an example of a new candidate compound which completed the pipeline and was selected for further, detailed analysis. The compound is shown to present a 2D ion migration topology, consisting of two separate intercalation pathways where the corresponding energy landscapes are calculated with the nudged-elastic band formalism. Acceptable energy barriers are found in the dilute (highly charged) limit, however the material is expected to exhibit slower kinetics in the vacancy (highly discharged) limit.Furthermore, a set of design principles are derived from data mining on voltage information from two classes of Li-ion battery cathodes. Most of Li-free cathodes are natively found in their charged state, in contrast to today's commercial lithium-ion battery cathodes, which are more stable in their discharged state. In this work, calculated cathode voltage information from both categories of cathode materials are combined, covering 5577 and 2423 total unique structure pairs, respectively. The resulting voltage distributions with respect to the redox pairs and anion types for both classes of compounds emphasize design principles for high-voltage cathodes, which favor later Period 4 transition metals in their higher oxidation states and more electronegative anions like fluorine or polyanion groups. Generally, cathodes that are found in their charged, delithiated state are shown to exhibit voltages lower than those that are most stable in their lithiated state, in agreement with thermodynamic expectations. Deviations from this trend are found to originate from different anion distributions between redox pairs.Lastly, a machine learning model for voltage prediction based on chemical formulae is trained on the data set obtained from the voltage distribution. This model displays sound connections to physical characteristics that impact cathode voltage and shows state-of-the-art performance when compared to two established composition-based ML models for materials properties predictions, Roost and CrabNet. The screening workflow, chemical design principles and the machine learning voltage model together compose a data-driven methodology for effectively executing high throughput materials discovery for practical and high-performance next-gen non-Li-containing cathodes.
- 일반주제명
- Materials science
- 일반주제명
- Engineering
- 일반주제명
- Physical chemistry
- 일반주제명
- Computational physics
- 키워드
- Li-metal anodes
- 키워드
- Machine learning
- 키워드
- Polyanion groups
- 키워드
- CrabNet
- 기타저자
- University of California, Berkeley Materials Science & Engineering
- 기본자료저록
- Dissertations Abstracts International. 87-01B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520260202103540
■006m o d
■007cr#unu||||||||
■020 ▼a9798288863646
■035 ▼a(MiAaPQ)AAI32040807
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a620.11
■1001 ▼aLi, Haoming Howard.
■24510▼aHigh-Throughput Computational Search for Li-Free Li-Ion Battery Cathodes
■260 ▼a[Sl]▼bUniversity of California, Berkeley▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a55 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-01, Section: B.
■500 ▼aAdvisor: Persson, Kristin A.
■5021 ▼aThesis (Ph.D.)--University of California, Berkeley, 2025.
■520 ▼aAs Li-metal anodes become more readily available, next-gen Li-ion battery cathodes are no longer required to contain Li in their as-synthesized state, vastly expanding the materials search space. In order to identify potential cathode materials that do not necessarily contain Li in their native state, I here present a series of tools and guidelines to carry out computational screening in this search space.Firstly, a pipeline for rapid cathode discovery has been established. This pipeline operates on any database of inorganic materials without a priori information on Li sites and performs screening based on computed voltage, capacity from sequential insertions of Li ions and most importantly, mobility built upon the graph-based migration network obtained through site connectivity. A preliminary application of the pipeline was carried out on a subset of the Materials Project database, and one particular polymorph of MnP2O7 is shown here as an an example of a new candidate compound which completed the pipeline and was selected for further, detailed analysis. The compound is shown to present a 2D ion migration topology, consisting of two separate intercalation pathways where the corresponding energy landscapes are calculated with the nudged-elastic band formalism. Acceptable energy barriers are found in the dilute (highly charged) limit, however the material is expected to exhibit slower kinetics in the vacancy (highly discharged) limit.Furthermore, a set of design principles are derived from data mining on voltage information from two classes of Li-ion battery cathodes. Most of Li-free cathodes are natively found in their charged state, in contrast to today's commercial lithium-ion battery cathodes, which are more stable in their discharged state. In this work, calculated cathode voltage information from both categories of cathode materials are combined, covering 5577 and 2423 total unique structure pairs, respectively. The resulting voltage distributions with respect to the redox pairs and anion types for both classes of compounds emphasize design principles for high-voltage cathodes, which favor later Period 4 transition metals in their higher oxidation states and more electronegative anions like fluorine or polyanion groups. Generally, cathodes that are found in their charged, delithiated state are shown to exhibit voltages lower than those that are most stable in their lithiated state, in agreement with thermodynamic expectations. Deviations from this trend are found to originate from different anion distributions between redox pairs.Lastly, a machine learning model for voltage prediction based on chemical formulae is trained on the data set obtained from the voltage distribution. This model displays sound connections to physical characteristics that impact cathode voltage and shows state-of-the-art performance when compared to two established composition-based ML models for materials properties predictions, Roost and CrabNet. The screening workflow, chemical design principles and the machine learning voltage model together compose a data-driven methodology for effectively executing high throughput materials discovery for practical and high-performance next-gen non-Li-containing cathodes.
■590 ▼aSchool code: 0028.
■650 4▼aMaterials science
■650 4▼aEngineering
■650 4▼aPhysical chemistry
■650 4▼aComputational physics
■653 ▼aLi-metal anodes
■653 ▼aLi-ion battery cathodes
■653 ▼aMachine learning
■653 ▼aPolyanion groups
■653 ▼aCrabNet
■690 ▼a0794
■690 ▼a0537
■690 ▼a0216
■690 ▼a0494
■71020▼aUniversity of California, Berkeley▼bMaterials Science & Engineering.
■7730 ▼tDissertations Abstracts International▼g87-01B.
■790 ▼a0028
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17357636▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
