Collaborative Research: Design of Low-Hysteresis High-Susceptibility Materials by Nanodomain Engineering

该奖项支持理论和计算研究，旨在为形状记忆合金、铁电和铁磁材料开发新的设计概念和原则，以实现各种应用的改进功能。在这些材料中，结构域可以通过施加外场（如应力、电场或磁场）从一个切换到另一个，从而使传感和驱动同时实现。这些智能材料在许多领域都有重要的应用，包括医疗设备、卫星、机器人、导航系统、数据存储和检索、机电和电光系统等。然而，在这些材料中形成的典型畴结构太大，导致性能不适合应用。另一个常见的问题是导致过早失效的功能性疲劳。pi将使用先进的计算和理论方法来研究连接晶体结构，缺陷，域结构和功能特性的新设计概念和原理。这些设计理念和原则旨在指导实验工作，加速发现具有最佳性能的新型智能和结构材料。这与材料基因组计划是一致的。该项目将直接培养研究生，为综合计算材料科学与工程的成功做出贡献。此外，对参与材料开发的研究人员的培训将使他们能够迅速接受新的设计概念和方法，从而提高材料技术人员的效率。该项目的教育推广旨在对鼓励代表性不足群体的高中生进入科学和工程学科产生重大影响。该奖项支持理论和计算研究，重点是基于铁的功能材料，包括形状记忆合金，铁电和铁磁材料。该项目的主要目标是通过设计(a)转化途径网络和(b)结构和化学异质性，加速发现具有强抗疲劳性的新型低滞后高磁化率铁基功能材料。前者探索了通过识别具有孤立圆形转变途径的体系来实现高磁化率的方法，而后者探索了如何通过抑制自催化和调节畴生长和粗化的空间范围，将传统的微米尺寸、远程有序、自适应的应变、极化和磁化畴转变为纳米畴。本文将基于群论、相变晶体学和图方法建立一个严谨的理论框架来分析相变路径网络（TPNs）。通过研究TPN图的对称性和拓扑结构，介绍了一种新的结构相变分类。pi旨在区分三种不同的TPN类型：一种可能提供高易感性，一种是可逆的并表现出形状记忆效应，以及一种可能通过转换导致功能疲劳而产生位错。PI将对特定系统进行系统的第一性原理和原子计算，以协助构建和分类TPN图，量化能量景观，并研究各种晶体缺陷的影响。最后，将进行相场模拟，以检查可能的连续相分离和其他机制，以在母相中产生纳米级结构和化学不均匀性，并研究它们对随后的铁相转变和铁纳米畴形成的影响。这些铁纳米畴对温度和外部场的响应将被记录下来。该材料具有与微畴材料截然不同的性能，特别是超低模量准线性伪弹性、低迟滞、高磁化率（如巨压电性、巨磁致伸缩和巨非迟滞应变响应）和强抗疲劳性能。该项目的教育推广旨在对鼓励代表性不足群体的高中生进入科学和工程学科产生重大影响。

NONTECHICAL SUMMARYThis award supports theoretical and computational research aimed to develop new design concepts and principles for shape memory alloys, and ferroelectric and ferromagnetic materials to achieve improved functionality for various applications. In these materials structural domains can switch from one to another by the application of an external field, such as stress, electric or magnetic fields, allowing sensing and actuation to be realized simultaneously. These smart materials have found critical applications in many fields, including medical devices, satellites, robots, navigation systems, data storage and retrieving, electromechanical and electro-optic systems, to name a few. However, typical domain structures formed in these materials are too large leading to properties that are not optimal for applications. Another common problem is that functional fatigue that leads to premature failure. The PIs will use advanced computational and theoretical methods to investigate new design concepts and principles that connect crystal structure, defects, domain structure and functional properties. These design concepts and principles are aimed to guide experimental efforts and accelerate the discovery of new smart as well as structural materials with optimal properties. This is in alignment with the Materials Genome Initiative. This project will directly prepare graduate students to immediately contribute to the success of integrated computational materials science and engineering. Additionally, the training of researchers involved in materials development will afford a rapid uptake of new design concepts and methodology, resulting in increased effectiveness of materials technologists. The educational outreach of the project is designed to have a significant influence on encouraging high school students who are members of underrepresented groups to enter science and engineering disciplines.TECHNICAL SUMMARYThis award supports theoretical and computational research that focuses on ferroic-based functional materials including shape memory alloys, and ferroelectric and ferromagnetic materials. The main objective of this project is to accelerate the discovery of novel low-hysteresis high-susceptibility ferroic-based functional materials with strong fatigue resistance via the design of (a) transformation pathway networks, and (b) structural and chemical heterogeneities. The former explores the means to achieve high susceptibility by identifying systems with isolated circular transformation pathways, while the latter explores how to transform conventional micron-sized, long-range ordered, self-accommodating strain, polarization and magnetization domains into nanodomains by suppressing autocatalysis and regulating the spatial extent of domain growth and coarsening during ferroic phase transitions. A rigorous theoretical framework will be developed based on group theory, phase transformation crystallography and graph methods to analyze transformation pathway networks (TPNs). Through investigating the symmetry and topology of TPN graphs, a new classification of structural phase transformations will be introduced. The PIs aim to distinguish three distinct TPN types: ones that could provide high susceptibility, ones that are reversible and exhibit shape memory effect, and ones that could generate dislocations through transformations causing functional fatigue. The PI will perform systematic first principles and atomistic calculations for specific systems to assist in constructing and classifying TPN graphs, to quantify the energy landscapes, and to investigate the effects of various crystalline defects. Finally phase field simulations will be carried out to examine possible continuous phase separations and other mechanisms to generate nanoscale structural and chemical non-uniformities in the parent phase and to study their effects on subsequent ferroic phase transitions and ferroic nanodomain formation. The responses of these ferroic nanodomains to temperature and external fields will be documented. Drastically different properties from those of their microdomain counterparts are expected, in particular ultra-low-modulus quasi-linear pseudoelasticity, low hysteresis, high susceptibility such as giant piezoelectricity, giant magnetostriction and giant non-hysteretic strain response, and strong fatigue resistance. The educational outreach of the project is designed to have a significant influence on encouraging high school students who are members of underrepresented groups to enter science and engineering disciplines.