CAREER: Advancing Atomic-Level Understanding of Kinetically Driven Solid-Solid Phase Transitions from First Principles and Machine Learning

职业：从第一原理和机器学习推进对动力学驱动的固-固相变的原子级理解

• 批准号: 2238516
• 负责人: Liping Yu
• 金额: $ 52.83万
• 依托单位: University of Maine
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2238516&HistoricalAwards=false
• 关键词: CAREER; Advancing; Atomic; Level; Understanding

NONTECHNICAL SUMMARYThis CAREER award supports theoretical and computational research to advance the fundamental understanding of solid-solid phase transitions. Most materials have several different stable crystalline structures, each with a characteristic set of physical, chemical, and mechanical properties. Carbon, which can form graphite (flaky, black material used in pencils) or diamond (hard, colorless gemstone) structures, is a well-known example. Solid-solid transitions that occur between different crystalline forms of the same compound are ubiquitous and important phenomena. They can lead to a wide variety of technologically important applications such as diamond and steel production, synthesis of ceramic materials, thermal energy harvesting and storage, rewritable optical data storage, and nonvolatile electronic memories. Historically, considerable progress has been made in understanding solid-solid transitions from thermodynamics concerning the relative phase stability (the “driving force” for the phase transition), regardless of transition paths between the initial and final structures. However, the kinetics that dictates whether or not the transition can occur in practice under given environmental conditions and which path the transition likely takes place remain poorly understood. This project will advance the atomic-level understanding of kinetics underlying solid-solid transitions without using empirical data and develop an advanced artificial intelligence method for the fast and accurate prediction of kinetic barriers that control solid-solid transition in various environments. The data and methods acquired will be broadly disseminated to the scientific community and the general public through open-source distributions and publications.Education and outreach activities are integrated in this project with the goal to inspire and develop a diverse, globally competitive next generation STEM workforce in computational materials science that will benefit the State of Maine as well as the nation. The research team will (i) develop a “kinetics-driven phase-change materials by design” module for high school students in collaboration with the Maine Center for Research in STEM Education, (ii) develop an advanced courses in “computational materials physics and modeling” for seniors and graduate students in science and engineering departments at the University of Maine, (iii) expand the partnership between the University of Maine and Oak Ridge National Laboratory to provide students the opportunity to take advantage of facilities and computational resources in the national laboratory to expand their experiences beyond the traditional university setting, and (iv) create a summer research fellowship program to provide opportunities for talented undergraduates majoring in science, engineering, and mathematics to conduct computational materials research.TECHNICAL SUMMARYThis CAREER award supports theoretical and computational research to advance atomic level understanding of solid-solid phase transitions. Solid-solid phase transitions are ubiquitous phenomena that play key roles in diverse technologies across physics, chemistry, biology, materials science and engineering. Despite having been studied for over a century, the fundamental understanding of phase transition kinetics remains largely qualitative or phenomenological; the atomistic mechanism of such transition processes and design rules for controlling kinetics are still crucially missing. This project will advance atomic-level understanding of kinetics of solid-solid phase transitions using a combined method of modern first-principles electronic structure theory calculations, quantitative chemical bond analysis, and machine learning. The specific objectives are to (i) identify physical principles and structural motifs that control kinetic barriers of polymorphic transitions from first principles, and (ii) develop a bottom-up physics-driven machine learning method for the fast and accurate prediction of transition barriers. The study will be carried on a set of select well-known phase-transition materials that are technologically important for energy and electronic applications. The research will accelerate the design and discovery of new functional phase-change materials where kinetics is essential.Education and outreach activities are integrated in this project with the goal to inspire and develop a diverse, globally competitive next-generation STEM workforce in computational materials science that will benefit the State of Maine as well as the nation. The research team will (i) develop a “kinetics-driven phase-change materials by design” module for high school students in collaboration with the Maine Center for Research in STEM Education, (ii) develop an advanced courses in “computational materials physics and modeling” for seniors and graduate students in science and engineering departments at the University of Maine, (iii) expand the partnership between the University of Maine and Oak Ridge National Laboratory to provide students the opportunity to take advantage of facilities and computational resources in the national laboratory to expand their experiences beyond the traditional university setting, and (iv) create a summer research fellowship program to provide opportunities for talented undergraduates majoring in science, engineering, and mathematics to conduct computational materials research.This project is jointly funded by the Division of Materials Research through the Condensed Matter and Materials Theory program, and the Established Program to Stimulate Competitive Research (EPSCoR).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

非技术摘要该职业奖支持理论和计算研究，以促进对固固相变的基本理解。大多数材料具有几种不同的稳定晶体结构，每种材料都具有一组特征碳，可以形成石墨。 （铅笔中使用的片状黑色材料）或金刚石（坚硬的无色宝石）结构是同一化合物的不同晶型之间发生的固-固转变普遍存在且重要的例子。它们可以带来各种具有重要技术意义的应用，例如金刚石和钢铁生产、陶瓷材料的合成、热能收集和存储、可重写光学数据存储和非易失性电子存储器。热力学中的固-固转变涉及相对相稳定性（相变的“驱动力”），无论初始结构和最终结构之间的转变路径如何，然而，动力学决定了在实际情况下是否可以发生转变。给定的环境该项目将在不使用经验数据的情况下推进对固体-固体转变动力学的原子水平理解，并开发一种先进的人工智能方法来快速准确地预测动力学障碍。控制各种环境中的固-固转变。所获得的数据和方法将通过开源发行版和出版物广泛传播给社区和公众。该项目整合了教育和推广科学活动，旨在激发和发展。多元化、具有全球竞争力的下一代计算材料科学领域的 STEM 劳动力将使缅因州乃至整个国家受益 该研究团队将 (i) 与缅因州中心合作，为高中生开发“动力学驱动的相变材料设计”模块。用于 STEM 教育研究，(ii) 为缅因大学科学与工程系的高年级学生和研究生开发“计算材料物理和建模”高级课程，(iii) 扩大缅因大学与橡树大学之间的合作关系里奇国家实验室为学生提供利用的机会国家实验室的设施和计算资源，以扩展他们的经验，超越传统的大学环境，以及（iv）创建夏季研究奖学金计划，为主修科学、工程和数学的优秀本科生提供进行计算材料研究的机会。 技术摘要职业奖支持理论和计算研究，以促进对固-固相变的原子水平理解。固-固相变是普遍存在的现象，尽管已经得到研究，但在物理、化学、生物学、材料科学和工程学的多种技术中发挥着关键作用。为了一个多世纪以来，对相变动力学的基本理解仍然主要是定性的或现象学的；这种转变过程的原子机制和控制动力学的设计规则仍然严重缺失，该项目将促进对固-固相动力学的原子级理解。使用现代第一原理电子结构理论计算、定量化学键分析和机器学习的组合方法进行转变具体目标是（i）确定控制多晶型转变的动力学势垒的物理原理和结构基序。从第一原理出发，（ii）开发一种自下而上的物理驱动的机器学习方法，用于快速准确地预测相变势垒。该研究将在一组选定的技术上重要的众所周知的相变材料上进行。该研究将加速动力学至关重要的新型功能性相变材料的设计和发现。该项目整合了教育和推广活动，旨在激发和开发多样化的、具有全球竞争力的下一代材料。计算材料科学领域的 STEM 劳动力将有利于国家研究团队将 (i) 与缅因州 STEM 教育研究中心合作，为高中生开发“动力学驱动的相变材料”模块，(ii) 开发先进的模型。为缅因大学科学和工程系的高年级学生和研究生开设“计算材料物理和建模”课程，(iii) 扩大缅因大学和橡树岭国家实验室之间的合作伙伴关系，为学生提供利用国家实验室的设施和计算资源将他们的经验扩展到其他领域传统大学环境，以及（iv）创建暑期研究奖学金计划，为科学、工程和数学专业的优秀本科生提供进行计算材料研究的机会。该项目由凝聚态材料研究部联合资助该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。