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: 1923929
• 负责人: Yunzhi Wang
• 金额: $ 33.9万
• 依托单位: Ohio State University
• 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=1923929&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.

该奖项支持理论和计算研究，以调查新理论和计算机模拟所支持的新材料设计概念。该方法将集中在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）。晶体可以改变其结构，以响应所施加的场，如温度，压力或应力，电场或磁场。晶体具有这样的属性，即对晶体进行的操作集合使晶体看起来相同。例如，一个立方晶体绕着特定轴旋转90度，原子会旋转到先前被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产生多个等效的结构状态。这些状态通过多个等效的前向和后向相变路径相互连接。这些路径可以用图形表示，形成一个称为相变图（PTG）的网络。晶体如何穿过PTG决定了结构相变的所有“活”特征，这些特征支撑了铁弹智能材料的实际重要特性。PTG分析为设计更智能的微观结构提供了新的机会，这些微观结构的尺度大于原子尺度，并且能够在适度放大下看到。微观结构与材料的性能，特别是机械性能有关。PI旨在开发微结构设计，从而获得具有前所未有性能的材料。本研究计划将利用PTG的“基因网络”设计演算法，“培育”新的内部微结构，以改善铁弹智能材料的功能与性能。这项研究的结果可能有利于汽车，航空航天，微机电系统和生物医学植入物中的许多先进技术应用。PTG分析，就像热力学中的相图一样，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。智能材料的直观性质及其酷的应用将有助于鼓励初中和高中学生进入科学和工程学科。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。 该奖项支持理论和计算研究，以研究新理论和计算机模拟支持的新材料设计概念。基于无扩散相变的智能材料的性质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个相变路径（PTP）的沿着破缺，而且还取决于它们的相变图（PTG）的拓扑结构和对称性。后者告诉我们父阶段和产品阶段的多个结构状态是如何相互连接的，以及系统在多个转换周期中可以访问哪些结构状态。PI将探索合金设计理念，并将通过结合使用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。将处理的具体科学问题包括：（a）量化相互连接的路径和过渡的自由能障碍，包括可能改变PTG拓扑结构和改变结构转换的基本特征，从而改变智能材料的功能和性能的非PTPs;（B）寻求以下问题的答案：PTG上的有偏随机游走对微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将分散在不同空间位置的应变状态“重置”回其原始状态并恢复原始微观结构？ (c)利用适当的浓度调制来调节马氏体相变，并使线性超弹性材料具有大的弹性应变极限，消失的滞后，和超低的伪弹性模量;（d）通过预测这些相变的激活应变体积和洗牌的优势来表征这些相变的温度和速率依赖性。该项目的成功有望改变铁弹性材料的设计。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。

项目成果

Heterostructures Enhance Simultaneously Strength and Ductility of a Commercial Titanium Alloy

• DOI: 10.1016/j.actamat.2023.119182
• 发表时间: 2023-07
• 期刊: Acta Materialia
• 影响因子: 9.4
• 作者: Di Wu;Mengyuan Hao;Tianlong Zhang;Zhen Wang;Jiang Wang;Guanghui Rao;Li-gang Zhang;Chaoyi Ding;Kechao Zhou;Li-bin Liu;Dong Wang;Yunzhi Wang

Unusual precipitation induced by solute segregation in coherent twin boundary in titanium alloys

• DOI: 10.1016/j.actamat.2022.118466
• 发表时间: 2022-10
• 期刊: Acta Materialia
• 影响因子: 9.4
• 作者: Chaoqiang Liu;Xingye Hu;Lin Qi;Houwen Chen;Zhiqiao Li;Xiaoyong Zhang;Hongge Yan;Kechao Zhou;M. Song;Yunzhi Wang;J. Nie

Composition-dependent shuffle-shear coupling and shuffle-regulated strain glass transition in compositionally modulated Ti-Nb alloys

成分调制的 Ti-Nb 合金中成分相关的洗牌剪切耦合和洗牌调节的应变玻璃化转变

• DOI: 10.1016/j.actamat.2023.118697
• 发表时间: 2023
• 期刊: Acta Materialia
• 影响因子: 9.4
• 作者: Yunting Su;Chuanxin Liang;Xun Sun;Hualei Zhang;Qianglong Liang;Yufeng Zheng;Yulin Hao;Rui Yang;Dong Wang;Dipankar Banerjee;Yunzhi Wang
• 通讯作者: Yunzhi Wang

Dependency of grain boundary dislocation network configuration on generalized stacking fault energy surface in FCC metals

• DOI: 10.1016/j.commatsci.2022.112003
• 发表时间: 2023-02
• 期刊: Computational Materials Science
• 影响因子: 3.3
• 作者: Yongxiang Li;Di Qiu;Yunzhi Wang

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