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.

非技术摘要这一职业奖支持理论和计算研究，以促进对固体相过渡的基本理解。大多数材料具有多种稳定的晶体结构，每种结构都有一组特征性的物理，化学和机械性能。碳是一个众所周知的例子。同一化合物的不同晶体形式之间发生的固相变过是无处不在的现象。它们可以导致多种技术上重要的应用，例如钻石和钢的生产，陶瓷材料的合成，热能收集和存储，可重写的光学数据存储以及非挥发性电子记忆。从历史上看，无论初始结构和最终结构之间的过渡路径如何，从热力学的相对相位稳定性（相对相变的“驱动力”）中得出了相当大的进步。但是，在给定的环境条件下以及过渡可能发生的道路下，动力学决定了是否可以在实践中进行过渡。该项目将提高对固体固体过渡基础动力学的原子水平的理解，而无需使用经验数据，并开发了一种先进的人工智能方法，以快速准确地预测各种环境中固体过渡的动力学障碍。所获得的数据和方法将通过开源分布和出版物将广泛传播给科学界和公众。教育和外展活动已整合到该项目中，目的是激发和发展多样性，全球竞争性的下一代STEM劳动力在计算材料科学中，这将受益于缅因州和国家。研究小组将（i）与缅因州STEM教育研究中心合作，为高中生开发“通过设计驱动的相位变化材料”模块，（ii）开发了“计算材料物理和建模”领域的高级课程，以为缅因州大学的科学和伙伴关系提供优势，（iii II），（iii II）在科学和工程学方面提供优势，（iii）。国家实验室中的设施和计算资源的资源，以将其经验扩展到传统的大学环境之外，并创建一个夏季研究奖学金计划，为有才华的本科生提供机会，专业的科学，工程和数学主修计算材料。固相过渡是无处不在的现象，在物理，化学，生物学，材料科学和工程学的潜水技术中起着关键作用。尽管已经研究了一个多世纪，但对相过渡动力学的基本理解仍然在很大程度上定性或现象学。这种过渡过程的原子机制和控制动力学的设计规则仍然完全缺失。该项目将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来提高原子级对固体相变的动力学的理解。具体目标是（i）确定从第一原理控制多态性过渡的动态障碍的物理原理和结构基序，以及（ii）为快速准确预测过渡屏障的自下而上的物理驱动的机器学习方法。该研究将进行一组精选的众所周知的相转换材料，这些材料在技术上对能源和电子应用很重要。这项研究将加快动力学至关重要的新功能相变材料的设计和发现。教育和外展活动是在该项目中整合的，其目标是激发和发展多样性，全球竞争性的下一代STEM劳动力在计算材料科学中，这将使缅因州和国家受益。研究小组将（i）与缅因州STEM教育研究中心合作，为高中生开发“通过设计驱动的相位变化材料”模块，（ii）开发了“计算材料物理和建模”领域的高级课程，以为缅因州大学的科学和伙伴关系提供优势，（iii II），（iii II）在科学和工程学方面提供优势，（iii）。国家实验室中的设施和计算资源，以将其经验扩展到传统的大学环境之外，并（iv）创建一个夏季研究奖学金计划，为有才华的本科生提供机会，专业的科学，工程和数学主修计算材料研究。该项目是由材料研究的材料研究委员会共同资助的。法定使命，并使用基金会的知识分子优点和更广泛的影响标准通过评估被认为是宝贵的支持。