Rational development of next-generation shape memory alloys

形状记忆合金（SMA）是一种在温度变化时会发生较大形状变化的材料。这种独特的性能使SMA成为制造各种应用中功能强大但重量轻的致动器的理想选择。虽然SMA已经在许多商业技术中被采用-从智能手机中的图像稳定器到微创血管内手术的工具-但市售SMA的相对较低的操作温度（通常在室温和100摄氏度之间）使其不适合许多应用。需要在高得多或低得多的温度下工作的新合金，以防止由于环境温度波动而引起的意外致动或允许在不利条件下工作。在这个项目中，将开发一个新的SMA的合理设计的框架。该框架将耦合高通量的计算和实验技术，使用一个物理模型，定量相关的材料特性的物理参数。该框架将用于筛选一系列合金，其目标是确定在操作温度和功能稳定性方面具有定制形状记忆性能的SMA的新家族。计算策略将从快速筛选表现出形状记忆合金相关特性的合金端点开始。然后将训练物理模型以快速内插跨越端点的区域中的复杂合金成分的热机械性能。在计算阶段确定感兴趣的材料系统，然后将使用最先进的实验技术进行研究。在这个项目中开发的方法和随后的基本理解的相变负责的形状记忆效应将提供一个途径，有针对性的设计新的SMA，可用于广泛的应用。该方法也很容易转移到设计和理解其他类别的活性材料，可用于传感器或致动器。在这个项目中开发的计算方法非常适合计算材料科学的研究生课程，并将被纳入计算材料设计的研究生课程。该项目还将为本科生和当地公立学校的高中生提供暑期实习计划，为学生提供材料，加工，建模和数据科学实验的学习环境。形状记忆合金在热弹性马氏体相变过程中会发生较大的可恢复形状变化。这些合金具有比任何其它固态致动器高一个数量级的致动能量密度，因此对于重量轻且坚固的致动系统是有意义的。镍钛诺是最常用的SMA，其驱动温度略高于环境温度。为了充分发挥SMA的潜力，需要能够在更高或更低温度下工作的新合金。在这个项目中，将开发一个新的SMA的合理设计的框架。该框架将耦合高通量的计算和实验技术，使用一个物理模型，定量相关的材料特性的结构参数。该框架将用于筛选一系列合金，目标是确定新的SMA家族，其在相变温度、滞后和稳定性方面具有定制的形状记忆性能。这项工作最初将集中在已知的立方二元和三元相与铁，铜，或镍作为主要成分，并将扩大必要的。计算筛选策略将从快速筛选表现出热弹性马氏体转变和稳定性的相关特性的合金端点开始。有前途的结构将进行更详细的研究，重点是其性能的温度依赖性。然后将训练物理模型以快速内插跨越端点的区域中的复杂合金成分的热机械性能。在计算阶段确定的感兴趣的材料系统将进行实验研究，结合组合纳米量热法和电阻率测量技术，使用沉积组合物扩散。在这个项目中开发的方法和随后的基本理解将提供一个途径，以有针对性地设计新的SMA，服务于广泛的应用。这个奖项反映了NSF的法定使命，并已被认为是值得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的支持。

Non-technical AbstractShape memory alloys (SMAs) are materials that undergo large shape changes when their temperatures are changed. This unique property makes SMAs ideal for the fabrication of powerful yet lightweight actuators for a variety of applications. While SMAs have already been adopted in a number of commercial technologies - from image stabilizers in smart phones to tools for minimally invasive endovascular surgery - the relatively low operating temperatures of commercially available SMAs (typically between room temperature and 100 degrees C) make them unsuitable for many applications. New alloys are needed that operate at either much higher or much lower temperatures to prevent inadvertent actuation by ambient temperature fluctuations or to allow operation under adverse conditions. In this project, a framework for the rational design of novel SMAs will be developed. The framework will couple high-throughput computational and experimental techniques using a physical model that quantitatively relates material properties to physical parameters. This framework will be used to screen a range of alloys with the goal of identifying new families of SMAs with tailored shape memory properties in terms of operating temperature and functional stability. The computational strategy will start with a rapid screening for alloy endpoints that exhibit relevant characteristics of shape memory alloys. Physical models will then be trained to rapidly interpolate the thermomechanical properties of complex alloy compositions in the region spanning the endpoints. Materials systems of interest identified in the computational phase will then be investigated using state-of-the art experimental techniques. The methodology developed in this project and the ensuing fundamental understanding of the phase transformation responsible for the shape memory effect will provide a pathway to the targeted design of novel SMAs that may be used in a broad range of applications. The methodology is also easily transferred to the design and understanding of other classes of active materials that may be used in sensors or actuators. The computational methods that will be developed in this project fit very well within a graduate curriculum for computational materials science and will be incorporated in a graduate course on computational materials design. This project will also provide the context for a summer internship program for undergraduate students and for high-school students from local public schools that will provide a learning environment for students to experiment with materials, processing, modeling, and data science. Technical AbstractShape memory alloys (SMAs) undergo large recoverable shape changes as a result of thermoelastic martensitic transformations. These alloys have actuation energy densities that are an order of magnitude higher than any other solid-state actuator and are therefore of interest for lightweight and robust actuation systems. Nitinol, the most commonly used SMA, has actuation temperatures slightly above ambient temperature. To fully realize the potential of SMAs, new alloys are needed that can operate at either much higher or lower temperatures. In this project, a framework for the rational design of novel SMAs will be developed. The framework will couple high-throughput computational and experimental techniques using a physical model that quantitatively relates material properties to structural parameters. This framework will be used to screen a range of alloys with a goal of identifying new families of SMAs with tailored shape memory properties in terms of transformation temperature, hysteresis and stability. The effort will initially focus on known cubic binary and ternary phases with Fe, Cu, or Ni as the main component, and will expand as necessary. The computational screening strategy will start with a rapid screening for alloy endpoints that exhibit relevant characteristics of thermoelastic martensitic transformations and stability. Promising structures will be investigated in more detail, focusing on the temperature dependence of their properties. Physical models will then be trained to rapidly interpolate thermomechanical properties of complex alloy compositions in the region spanning the endpoints. Materials systems of interest identified in the computational phase will be investigated experimentally using sputter-deposited composition spreads combined with combinatorial nanocalorimetry and resistivity measurement techniques. The methodology developed in this project and the ensuing fundamental understanding will provide a pathway to the targeted design of novel SMAs that serve a broad range of applications.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.