Non-technical Abstract Shape 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.
Shape 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.
