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

非技术总结这个职业奖支持理论和计算研究，以推进固-固相变的基本理解。大多数材料都有几种不同的稳定晶体结构，每种结构都有一套独特的物理、化学和机械性能。碳，可以形成石墨（铅笔中使用的黑色材料）或钻石（坚硬，无色宝石）结构，是一个众所周知的例子。在同一化合物的不同晶型之间发生的固-固转变是普遍存在的重要现象。它们可以导致各种各样的技术重要的应用，如金刚石和钢铁生产，陶瓷材料的合成，热能收集和存储，半导体光学数据存储和非易失性电子存储器。从历史上看，从热力学角度理解固-固相变已经取得了相当大的进展，涉及相对相稳定性（相变的“驱动力”），而不管初始和最终结构之间的转变路径。然而，在给定的环境条件下，在实践中是否会发生这种转变，以及转变可能发生的路径仍然知之甚少。该项目将在不使用经验数据的情况下推进对固-固转变动力学的原子级理解，并开发一种先进的人工智能方法，用于快速准确地预测在各种环境中控制固-固转变的动力学障碍。获得的数据和方法将通过开源发行和出版物广泛传播给科学界和公众。教育和外联活动被整合在这个项目中，目标是激励和发展一个多样化的，具有全球竞争力的下一代计算材料科学STEM劳动力，这将有利于缅因州以及国家。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“动力学驱动相变材料设计”模块，（ii）为缅因州大学科学和工程系的高年级和研究生开发“计算材料物理和建模”高级课程，㈢扩大缅因州大学和橡树岭国家实验室之间的伙伴关系，使学生有机会利用国家实验室的设施和计算资源，将他们的经验扩展到传统的大学环境之外，和（iv）创建一个暑期研究奖学金计划，为科学，工程和数学专业的有才华的本科生提供进行计算材料研究的机会。技术总结这个职业生涯奖支持理论和计算研究，以促进原子水平上的理解固-固相变。固-固相变是一种普遍存在的现象，在物理学、化学、生物学、材料科学和工程学的各种技术中发挥着关键作用。尽管已经研究了超过世纪，相变动力学的基本理解仍然主要是定性或现象学的，这种过渡过程的原子机制和控制动力学的设计规则仍然是至关重要的缺失。该项目将使用现代第一性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，推进对固-固相变动力学的原子级理解。具体目标是：（i）确定控制第一原理多态转换动力学障碍的物理原理和结构基序，以及（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转换障碍。这项研究将对一组选定的众所周知的相变材料进行，这些材料在能源和电子应用方面具有重要的技术意义。这项研究将加速设计和发现新的功能相变材料，动力学是必不可少的。教育和推广活动被整合在这个项目中，旨在激励和发展一个多元化的，具有全球竞争力的下一代STEM劳动力在计算材料科学，这将有利于缅因州以及国家。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“动力学驱动相变材料设计”模块，（ii）为缅因州大学科学和工程系的高年级和研究生开发“计算材料物理和建模”高级课程，㈢扩大缅因州大学和橡树岭国家实验室之间的伙伴关系，使学生有机会利用国家实验室的设施和计算资源，将他们的经验扩展到传统的大学环境之外，及（iv）设立暑期研究奖学金计划，为主修科学、工程及数学的有才华本科生提供进行计算材料研究的机会。该计划由材料研究部透过凝聚态物质及材料理论计划共同资助，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.