Collaborative Research: Traversals in Transformation Strain Space and Microstructure Design for High Performance Ferroelastic Materials

合作研究：高性能铁弹性材料的变换应变空间遍历和微观结构设计

• 批准号: 1923976
• 负责人: Ju Li
• 金额: $ 30.3万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1923976&HistoricalAwards=false
• 关键词: Collaborative; Research; Traversals; Transformation; Strain

NONTECHNICAL SUMMARYThis award supports theoretical and computational research to investigate new materials design concepts enabled by a new theory and computer simulations. The approach will be focused on an important class of smart materials – ferroelastic smart materials including superelastic metals and shape memory alloys (SMAs). Crystals can change their structures in response to an applied field, such as temperature, pressure or stress, electric or magnetic fields. Crystals have the property that sets of operations on a crystal leave the crystal looking the same. For example, 90-degree rotations around specific axes of a cubic crystal rotate atoms into the same positions previously occupied by atoms; the crystal is thus the same under such symmetry operations. Because of this crystal symmetry, changes associated with structural phase transformations can lead to the generation of multiple equivalent structural states. These states are interconnected by multiple equivalent forward and backward phase transformation pathways. These pathways can be represented pictorially as a graph that forms a web dubbed phase transformation graphs (PTGs). How a crystal traverses a PTG dictates all the “live” characteristics of structural phase transformations that underpin the practically important properties of a ferroelastic smart material. PTG analysis offers new opportunities to engineer smarter microstructures, the structure of crystals on scales larger than the atomic scale and able to be seen under modest magnification. Microstructures are connected to properties, particularly mechanical properties of materials. The PIs aim to develop microstructure designs that lead to materials with unprecedented properties. This research project will utilize the PTG "gene networks" in the design algorithms to "breed" new internal microstructures for improving functionality and performance of ferroelastic smart materials. The outcome of this research could benefit numerous advanced technological applications in automotive, aerospace, micro-electromechanical systems, and biomedical implants. The PTG analysis, just like phase diagrams in thermodynamics, is a fundamental tool in smart materials design and it can enrich undergraduate and graduate curricula in materials science and engineering. The intuitive nature of smart materials and their cool applications will help to encourage middle- and high-school students to enter science and engineering disciplines. The new alloy design strategies, PTG analysis and computer simulation techniques will be broadly disseminated at conferences, online tutorials, and in academic journals. TECHNICAL SUMMARYThis award supports theoretical and computational research to investigate new materials design concepts enabled by a new theory and computer simulations. It has yet to be recognized that the properties and performances of smart materials based on diffusionless transformations are dictated not only by the symmetry of the individual crystal structures involved and symmetry-breaking along a single phase transformation pathway (PTP), but also by the topology and symmetry of their phase transformation graphs (PTGs). The latter tells us how the multiple structural states of the parent and product phases are interconnected and what structural states could be visited by the system during multiple transformation cycles. The PIs will explore alloy design ideas and will address scientific issues by using a combination of PTG analysis, ab initio calculations, kinetic Monte Carlo, and phase field simulations. Specific scientific issues that will be addressed include: (a) Quantifying the connected pathways and free-energy barriers of transitions, including the symmetry-dictated non-PTPs that could alter the topology of PTGs and change the fundamental characteristics of the structural transformations and hence the functionality and performance of the smart materials; (b) Seeking answers for the following questions: What is the consequence of a biased random walk on PTG for microstructural evolution and functional fatigue? After dispersal on PTG, is there an effective way to “reset” the dispersed strain states at various spatial locations back to their original state and recover the original microstructure? (c) Making use of proper concentration modulations to regulate martensitic transformations and make linear super-elastic materials with large elastic strain limit, vanishing hysteresis, and ultralow pseudo-elastic modulus; (d) Characterizing the temperature- and rate-dependences of these transformations by predicting their activation strain-volume and pre-dominance of shuffling. Success of the project holds promise to transform ferroelastic materials design.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.

非技术摘要该奖项支持理论和计算研究，以研究通过新理论和计算机模拟实现的新材料设计概念。该方法将重点关注一类重要的智能材料——铁弹性智能材料，包括超弹性金属和形状记忆合金（SMA）。晶体可以响应施加的场（例如温度、压力或应力、电场或磁场）而改变其结构。晶体具有使晶体看起来相同的特性。围绕立方晶体特定轴的 90 度旋转将原子旋转到先前原子占据的相同位置；因此在这种对称操作下晶体是相同的，由于这种晶体对称性，与结构相变相关的变化可以导致生成。这些状态通过多个等效的前向和后向相变路径相互连接，这些路径可以形象地表示为形成称为相变图（PTG）的网络的图，该图指示了晶体如何穿过PTG。结构相变的“实时”特征支撑着铁弹性智能材料的实际重要特性，为设计更智能的微观结构（比原子尺度更大并且能够在适度放大倍率下可见的晶体结构）提供了新的机会。微观结构与材料的性能有关，特别是材料的机械性能，该研究项目将在设计算法中利用 PTG“基因网络”来开发具有前所未有性能的材料。 “培育”新的内部微观结构，以改善铁弹性智能材料的功能和性能，这项研究的成果可能有益于汽车、航空航天、微机电系统和生物医学植入物中的众多先进技术应用，就像 PTG 分析中的相图一样。热力学是智能材料设计的基本工具，它可以丰富材料科学与工程的本科生和研究生课程，智能材料的直观性及其酷炫的应用将有助于鼓励初中和高中学生。新的合金设计策略、PTG 分析和计算机模拟技术将在会议、在线教程和学术期刊上广泛传播。技术摘要该奖项支持理论和计算研究，以研究新的材料设计概念。目前尚未认识到基于无扩散转变的智能材料的特性和性能不仅取决于所涉及的单个晶体结构的对称性和沿单相的对称性破缺。相变路径（PTP），还通过其相变图（PTG）的拓扑和对称性，后者告诉我们母相和产物相的多种结构状态如何相互关联，以及系统在过程中可以访问哪些结构状态。 PI 将探索合金设计思路，并通过结合使用 PTG 分析、从头计算、动力学蒙特卡罗和相场模拟来解决科学问题，其中包括：(a)量化转变的连接路径和自由能势垒，包括对称性决定的非 PTP，它们可以改变 PTG 的拓扑结构并改变结构转变的基本特征，从而改变智能材料的功能和性能 (b)寻求以下问题的答案：PTG上的偏向随机游走对微观结构演化和功能疲劳的影响是什么？在PTG上分散后，是否有一种有效的方法来“重置”各个空间位置的分散应变状态？回到原来的状态并恢复原来的微观结构？（c）利用适当的浓度调节来调节马氏体转变，制造具有大弹性应变极限、消失滞后和超低伪弹性模量的线性超弹性材料（d）；通过预测这些转变的激活应变体积和改组的主导地位来表征这些转变的温度和速率依赖性，该项目的成功有望实现铁弹性材料的转变。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。

项目成果

Sliding ferroelectricity in 2D van der Waals materials: Related physics and future opportunities

• DOI: 10.1073/pnas.2115703118
• 发表时间: 2021-12-14
• 期刊: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• 影响因子: 11.1
• 作者: Wu, Menghao;Li, Ju
• 通讯作者: Li, Ju

High accuracy neural network interatomic potential for NiTi shape memory alloy

• DOI: 10.1016/j.actamat.2022.118217
• 发表时间: 2022-07
• 期刊: Acta Materialia
• 影响因子: 9.4
• 作者: Hao Tang;Yin Zhang;Qingjie Li;Haowei Xu;Yuchi Wang;Yunzhi Wang;Ju Li

Phase transitions in 2D materials

• DOI: 10.1038/s41578-021-00304-0
• 发表时间: 2021-04-09
• 期刊: NATURE REVIEWS MATERIALS
• 影响因子: 83.5
• 作者: Li, Wenbin;Qian, Xiaofeng;Li, Ju
• 通讯作者: Li, Ju