FRG: Predictive Computational Modeling of Two-Dimensional Materials Beyond Graphene: Defects and Morphologies

FRG:石墨烯以外的二维材料的预测计算模型:缺陷和形态

基本信息

项目摘要

NONTECHNICAL SUMMARYThe Division of Materials Research and the Division of Mathematical Sciences contribute funds to this award. It supports interdisciplinary research and educational activities in computational materials science, with a focus on the growth of two-dimensional (2D) semiconducting materials. While graphene is the best-known 2D material, it is limited in device application due to its high conductivity. More recent research has focused on 2D semiconducting materials, which can be manipulated to block or permit current flow. These research activities have been primarily based on experimental explorations due to a gap in the fundamental understanding in what determines their structures and properties. This project aims to help fill this gap by developing a model for the growth of these systems based on the phase field crystal modeling approach. This approach allows researchers to study materials involving tiny structures as small as atoms. The team of researchers will develop, parameterize, and validate a phase field crystal model for 2D semiconducting materials, which can be used improve the synthesis process of 2D materials and their assembly. Simulations will also be used to examine the structure of these materials and associated defects at the atomic level. Educational activities include courses on crystal growth for high school students, proposed as part of the California State Summer School for Mathematics and Science at UC Irvine, engaging with Science Olympiad, and other STEM events. These activities will help develop the future generation of mathematicians, scientists and engineers. Graduate students will receive interdisciplinary training and will present their findings at conferences, which will enhance their educational experience. The team shall also act as a resource for the research community by distributing the codes that are developed and by organizing symposia on phase field crystal models at national meetings.TECHNICAL SUMMARYThe Division of Materials Research and the Division of Mathematical Sciences contribute funds to this award. It supports interdisciplinary research and educational activities in computational materials science, with a focus on the growth of two-dimensional (2D) semiconducting materials. The recent discovery of two-dimensional (2D) semiconducting materials such as MoS2 and MoSe2 has intensified the research efforts in these materials. These materials exhibit unique properties due to the 2D confinement, but they, unlike graphene, also possess the ability to switch between conducting and insulating states, offering a potential to yield revolutionary new technologies. The research efforts have been primarily based on experimental exploration based on various synthetic routes, and significant gaps exist in the fundamental understanding of what determine their bulk and defect structures and morphologies, as well as how they influence their properties. Due to the small length scale involved, computational modeling is essential for developing such understanding. Phase field crystal (PFC) modeling is uniquely suited for this problem because of its ability to resolve atomic-scale structure and its extended time-scale comparable to those associated with synthesis. This multidisciplinary project addresses the challenge of understanding multiscale phenomena associated with the formation of nanostructures by exploiting recent developments in PFC models, which follow the dynamics of individual atoms over diffusive time scales. Originating from classical density functional theory, the PFC method naturally incorporates elastic and plastic deformations as well as crystalline defects. The team of materials scientists and a mathematician will develop a new PFC-based computational methodology for modeling the structure and the synthesis of two-dimensional, multicomponent materials. The models will be parameterized and validated with the aid of atomistic simulations and experimental results. Defects such as grain boundaries, which must be controlled in device applications, will be examined. The computational tools will build on the efficient numerical algorithms developed under previous funding and tailor it to the new models and will be disseminated through repositories such as GitHub. These tools will provide a framework for the computational discovery of the fundamental mechanisms underlying synthesis of 2D materials, their assembly, and their atomic-scale structure. Educational activities include courses on crystal growth for high school students, proposed as part of the California State Summer School for Mathematics and Science at UC Irvine, engaging with the Science Olympiad, and other STEM events. These activities will help develop the future generation of mathematicians, scientists and engineers. Graduate students will receive interdisciplinary training and will present their findings at conferences, which would enhance their educational experience. The group shall also act as a resource for the research community by organizing symposia on phase field crystal models at national meetings.
非技术摘要材料研究部和数学科学部为该奖项提供资金。它支持计算材料科学的跨学科研究和教育活动,重点关注二维 (2D) 半导体材料的生长。 虽然石墨烯是最著名的二维材料,但由于其高导电性,其在器件应用中受到限制。 最近的研究主要集中在二维半导体材料上,可以操纵它来阻止或允许电流流动。 由于对决定其结构和性质的基本理解存在差距,这些研究活动主要基于实验探索。 该项目旨在通过开发基于相场晶体建模方法的这些系统的生长模型来帮助填补这一空白。 这种方法使研究人员能够研究涉及小至原子的微小结构的材料。 研究小组将开发、参数化和验证二维半导体材料的相场晶体模型,该模型可用于改进二维材料的合成过程及其组装。 模拟还将用于在原子水平上检查这些材料的结构和相关缺陷。 教育活动包括为高中生开设晶体生长课程,作为加州大学欧文分校加州数学和科学暑期学校的一部分,参与科学奥林匹克竞赛和其他 STEM 活动。 这些活动将有助于培养下一代数学家、科学家和工程师。 研究生将接受跨学科培训,并将在会议上展示他们的研究结果,这将增强他们的教育经验。该团队还应通过分发所开发的代码以及在国家会议上组织有关相场晶体模型的研讨会来充当研究界的资源。技术摘要材料研究部和数学科学部为该奖项提供资金。它支持计算材料科学的跨学科研究和教育活动,重点关注二维 (2D) 半导体材料的生长。最近发现的二维(2D)半导体材料(例如MoS2和MoSe2)加强了这些材料的研究力度。 由于二维限制,这些材料表现出独特的性能,但与石墨烯不同,它们还具有在导电和绝缘状态之间切换的能力,从而提供了产生革命性新技术的潜力。 研究工作主要基于基于各种合成路线的实验探索,对于决定其体积和缺陷结构和形态以及它们如何影响其性质的基本理解存在重大差距。 由于涉及的长度尺度较小,计算建模对于发展这种理解至关重要。 相场晶体(PFC)建模特别适合解决这个问题,因为它能够解析原子尺度结构,并且其扩展的时间尺度与合成相关的时间尺度相当。这个多学科项目通过利用 PFC 模型的最新发展来解决理解与纳米结构形成相关的多尺度现象的挑战,该模型遵循扩散时间尺度上单个原子的动态。 PFC方法源于经典的密度泛函理论,自然地融合了弹性和塑性变形以及晶体缺陷。 由材料科学家和数学家组成的团队将开发一种基于 PFC 的新计算方法,用于对二维多组分材料的结构和合成进行建模。 这些模型将借助原子模拟和实验结果进行参数化和验证。 将检查器件应用中必须控制的晶界等缺陷。 这些计算工具将建立在先前资助下开发的高效数值算法的基础上,并根据新模型进行定制,并将通过 GitHub 等存储库进行传播。 这些工具将为二维材料合成、组装及其原子尺度结构的基本机制的计算发现提供框架。 教育活动包括为高中生开设晶体生长课程,作为加州大学欧文分校加州数学和科学暑期学校的一部分,参与科学奥林匹克竞赛和其他 STEM 活动。 这些活动将有助于培养下一代数学家、科学家和工程师。 研究生将接受跨学科培训,并将在会议上展示他们的研究结果,这将增强他们的教育经验。 该小组还将通过在国家会议上组织相场晶体模型研讨会来充当研究界的资源。

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Katsuyo Thornton其他文献

Teaching Computational Methods for Materials Discovery and Design
  • DOI:
    10.1007/s11837-023-05923-2
  • 发表时间:
    2023-06-02
  • 期刊:
  • 影响因子:
    2.300
  • 作者:
    Timothy Chambers;Katsuyo Thornton;Wenhao Sun
  • 通讯作者:
    Wenhao Sun
The origin of the superior fast-charging performance of hybrid graphite/hard carbon anodes for Li-ion batteries
锂离子电池混合石墨/硬碳负极卓越快充性能的起源
  • DOI:
    10.1016/j.ensm.2025.104053
  • 发表时间:
    2025-03-01
  • 期刊:
  • 影响因子:
    20.200
  • 作者:
    Vishwas Goel;Kevin Masel;Kuan-Hung Chen;Ammar Safdari;Neil P. Dasgupta;Katsuyo Thornton
  • 通讯作者:
    Katsuyo Thornton
New frontiers for the materials genome initiative
材料基因组计划的新前沿
  • DOI:
    10.1038/s41524-019-0173-4
  • 发表时间:
    2019-04-05
  • 期刊:
  • 影响因子:
    11.900
  • 作者:
    Juan J. de Pablo;Nicholas E. Jackson;Michael A. Webb;Long-Qing Chen;Joel E. Moore;Dane Morgan;Ryan Jacobs;Tresa Pollock;Darrell G. Schlom;Eric S. Toberer;James Analytis;Ismaila Dabo;Dean M. DeLongchamp;Gregory A. Fiete;Gregory M. Grason;Geoffroy Hautier;Yifei Mo;Krishna Rajan;Evan J. Reed;Efrain Rodriguez;Vladan Stevanovic;Jin Suntivich;Katsuyo Thornton;Ji-Cheng Zhao
  • 通讯作者:
    Ji-Cheng Zhao
Enhancing polycrystalline-microstructure reconstruction from X-ray diffraction microscopy with phase-field post-processing
  • DOI:
    10.1016/j.scriptamat.2024.116228
  • 发表时间:
    2024-11-01
  • 期刊:
  • 影响因子:
  • 作者:
    Marcel Chlupsa;Zachary Croft;Katsuyo Thornton;Ashwin J. Shahani
  • 通讯作者:
    Ashwin J. Shahani
Phase-Field Modeling and Simulations of Lipid Membranes Coupling Composition with Membrane Mechanical Properties
  • DOI:
    10.1016/j.bpj.2009.12.1536
  • 发表时间:
    2010-01-01
  • 期刊:
  • 影响因子:
  • 作者:
    Chloe M. Funkhouser;Francisco J. Solis;Katsuyo Thornton
  • 通讯作者:
    Katsuyo Thornton

Katsuyo Thornton的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Katsuyo Thornton', 18)}}的其他基金

Summer School for Integrated Computational Materials Education
综合计算材料教育暑期学校
  • 批准号:
    2213806
  • 财政年份:
    2022
  • 资助金额:
    $ 109.74万
  • 项目类别:
    Standard Grant
Elements: Data Driven Autonomous Thermodynamic and Kinetic Model Builder for Microstructural Simulations
元素:用于微观结构模拟的数据驱动自主热力学和动力学模型构建器
  • 批准号:
    2209423
  • 财政年份:
    2022
  • 资助金额:
    $ 109.74万
  • 项目类别:
    Standard Grant
Probing the Evolution of Granular Microstructures during Dynamic Annealing via Integrated Three-Dimensional Experiments and Simulations
通过集成三维实验和模拟探讨动态退火过程中颗粒微观结构的演变
  • 批准号:
    2104786
  • 财政年份:
    2021
  • 资助金额:
    $ 109.74万
  • 项目类别:
    Continuing Grant
Harnessing Abnormal Grain Growth for the Production of Single Crystals
利用异常晶粒生长来生产单晶
  • 批准号:
    2003719
  • 财政年份:
    2020
  • 资助金额:
    $ 109.74万
  • 项目类别:
    Standard Grant
GOALI: Collaborative Research: An Experimental and Theoretical Study of the Microstructural and Electrochemical Stability of Solid Oxide Cells
GOALI:协作研究:固体氧化物电池微观结构和电化学稳定性的实验和理论研究
  • 批准号:
    1912151
  • 财政年份:
    2019
  • 资助金额:
    $ 109.74万
  • 项目类别:
    Continuing Grant
Collaborative Research: Integrated Computational and Experimental Studies of Solid Oxide Fuel Cell Electrode Structural Evolution and Electrochemical Characteristics
合作研究:固体氧化物燃料电池电极结构演化和电化学特性的综合计算和实验研究
  • 批准号:
    1506055
  • 财政年份:
    2015
  • 资助金额:
    $ 109.74万
  • 项目类别:
    Standard Grant
Collaborative Research: Summer School for Integrated Computational Materials Education
合作研究:综合计算材料教育暑期学校
  • 批准号:
    1410461
  • 财政年份:
    2014
  • 资助金额:
    $ 109.74万
  • 项目类别:
    Continuing Grant
FRG: Development and Validation of Novel Computational Tools for Modeling the Growth and Self-Assembly of Crystalline Nanostructures
FRG:用于模拟晶体纳米结构的生长和自组装的新型计算工具的开发和验证
  • 批准号:
    1105409
  • 财政年份:
    2011
  • 资助金额:
    $ 109.74万
  • 项目类别:
    Standard Grant
Summer School for Integrated Computational Materials Education
综合计算材料教育暑期学校
  • 批准号:
    1058314
  • 财政年份:
    2010
  • 资助金额:
    $ 109.74万
  • 项目类别:
    Standard Grant
Collaborative Research: Three-Dimensional Microstructural and Chemical Mapping of Solid Oxide Fuel Cell Electrodes: Processing, Structure, Stability, and Electrochemistry
合作研究:固体氧化物燃料电池电极的三维微观结构和化学测绘:加工、结构、稳定性和电化学
  • 批准号:
    0907030
  • 财政年份:
    2009
  • 资助金额:
    $ 109.74万
  • 项目类别:
    Standard Grant

相似海外基金

Computational simulation of the potential improvement in clinical outcomes of cardiovascular diseases with the use of a personalized predictive medicine approach
使用个性化预测医学方法对心血管疾病临床结果的潜在改善进行计算模拟
  • 批准号:
    10580116
  • 财政年份:
    2023
  • 资助金额:
    $ 109.74万
  • 项目类别:
ERI: Computational Investigation of High-Pressure Turbulent Premixed Flames - Physical Insights and Two-Scale Predictive Modeling
ERI:高压湍流预混火焰的计算研究 - 物理见解和两尺度预测建模
  • 批准号:
    2301829
  • 财政年份:
    2023
  • 资助金额:
    $ 109.74万
  • 项目类别:
    Standard Grant
Development of diagnostic and predictive computational mechanics methods for cardiovascular system
心血管系统诊断和预测计算力学方法的开发
  • 批准号:
    RGPIN-2017-05349
  • 财政年份:
    2022
  • 资助金额:
    $ 109.74万
  • 项目类别:
    Discovery Grants Program - Individual
Predictive modeling of dynamic and nonlinear structural behavior by machine learning and its application to computational morphogenesis
通过机器学习对动态和非线性结构行为进行预测建模及其在计算形态发生中的应用
  • 批准号:
    22K04416
  • 财政年份:
    2022
  • 资助金额:
    $ 109.74万
  • 项目类别:
    Grant-in-Aid for Scientific Research (C)
Towards predictive 4D computational models for the heart
迈向心脏的预测 4D 计算模型
  • 批准号:
    DP220103160
  • 财政年份:
    2022
  • 资助金额:
    $ 109.74万
  • 项目类别:
    Discovery Projects
MMN deficits in psychotic disorders: Neurobiological and computational mechanisms and predictive utility
精神障碍中的 MMN 缺陷:神经生物学和计算机制以及预测效用
  • 批准号:
    10311147
  • 财政年份:
    2021
  • 资助金额:
    $ 109.74万
  • 项目类别:
I-Corps: Developing predictive computational methods for drug development
I-Corps:开发药物开发的预测计算方法
  • 批准号:
    2132672
  • 财政年份:
    2021
  • 资助金额:
    $ 109.74万
  • 项目类别:
    Standard Grant
Predictive Bioavailability of Oral Drugs Using Computational Fluid Dynamics
使用计算流体动力学预测口服药物的生物利用度
  • 批准号:
    552128-2020
  • 财政年份:
    2021
  • 资助金额:
    $ 109.74万
  • 项目类别:
    Alliance Grants
MMN deficits in psychotic disorders: Neurobiological and computational mechanisms and predictive utility
精神障碍中的 MMN 缺陷:神经生物学和计算机制以及预测效用
  • 批准号:
    10421053
  • 财政年份:
    2021
  • 资助金额:
    $ 109.74万
  • 项目类别:
Development of diagnostic and predictive computational mechanics methods for cardiovascular system
心血管系统诊断和预测计算力学方法的开发
  • 批准号:
    RGPIN-2017-05349
  • 财政年份:
    2021
  • 资助金额:
    $ 109.74万
  • 项目类别:
    Discovery Grants Program - Individual
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了