New shape memory alloy discovered through artificial intelligence
framework
Nickel-titanium shape memory records highest efficiency

Date:
May 5, 2022
Source:
Texas A&M University
Summary:
Researchers used an Artificial Intelligence Materials Selection
framework (AIMS) to discover a new shape memory alloy. The shape
memory alloy showed the highest efficiency during operation achieved
thus far for nickel-titanium-based materials. In addition, their
data-driven framework offers proof of concept for future materials
development.



FULL STORY ========================================================================== Funded by the National Science Foundation's Designing Materials to Revolutionize Our Engineering Future (DMREF) Program, researchers
from the Department of Materials Science and Engineering at Texas A&M University used an Artificial Intelligence Materials Selection framework
(AIMS) to discover a new shape memory alloy. The shape memory alloy
showed the highest efficiency during operation achieved thus far for nickel-titanium-based materials. In addition, their data-driven framework offers proof of concept for future materials development.


========================================================================== Shape memory alloys are utilized in various fields where compact,
lightweight and solid-state actuations are needed, replacing hydraulic
or pneumatic actuators because they can deform when cold and then return
to their original shape when heated. This unique property is critical
for applications, such as airplane wings, jet engines and automotive components, that must withstand repeated, recoverable large-shape changes.

There have been many advancements in shape memory alloys since
their beginnings in the mid-1960s, but at a cost. Understanding and
discovering new shape memory alloys has required extensive research
through experimentation and ad-hoc trial and error. Despite many of which
have been documented to help further shape memory alloy applications,
new alloy discoveries have occurred in a decadal fashion. About every 10
years, a significant shape memory alloy composition or system has been discovered. Moreover, even with advances in shape memory alloys, they
are hindered by their low energy efficiency caused by incompatibilities
in their microstructure during the large shape change.

Further, they are notoriously difficult to design from scratch.

To address these shortcomings, Texas A&M researchers have combined
experimental data to create an AIMS computational framework capable of determining optimal materials compositions and processing these materials, which led to the discovery of a new shape memory alloy composition.

"When designing materials, sometimes you have multiple objectives or constraints that conflict, which is very difficult to work around,"
said Dr.

Ibrahim Karaman, Chevron Professor I and materials science and
engineering department head. "Using our machine-learning framework, we
can use experimental data to find hidden correlations between different materials' features to see if we can design new materials." The shape
memory alloy found during the study using AIMS was predicted and proven
to achieve the narrowest hysteresis ever recorded. In other words, the
material showed the lowest energy loss when converting thermal energy to mechanical work. The material showcased high efficiency when subject to
thermal cycling due to its extremely small transformation temperature
window. The material also exhibited excellent cyclic stability under
repeated actuation.

A nickel-titanium-copper composition is typical for shape memory alloys.

Nickel-titanium-copper alloys typically have titanium equal to 50% and
form a single-phase material. Using machine learning, the researchers
predicted a different composition with titanium equal to 47% and copper
equal to 21%. While this composition is in the two-phase region and forms particles, they help enhance the material's properties, explained William Trehern, doctoral student and graduate research assistant in the materials science and engineering department and the publication's first author.

In particular, this high-efficiency shape memory alloy lends itself to
thermal energy harvesting, which requires materials that can capture
waste energy produced by machines and put it to use, and thermal energy storage, which is used for cooling electronic devices.

More notably, the AIMS framework offers the opportunity to use
machine-learning techniques in materials science. The researchers see
potential to discover more shape memory alloy chemistries with desired characteristics for various other applications.

"It is a revelation to use machine learning to find connections that our
brain or known physical principles may not be able to explain," said
Karaman. "We can use data science and machine learning to accelerate
the rate of materials discovery. I also believe that we can potentially discover new physics or mechanisms behind materials behavior that we did
not know before if we pay attention to the connections machine learning
can find." Other contributors include Dr. Raymundo Arro'yave and
Dr. Kadri Can Atli, professors in the materials science and engineering department, and materials science and engineering undergraduate student
Risheil Ortiz-Ayala.

"While machine learning is now widely used in materials science, most approaches to date focus on predicting the properties of a material
without necessarily explaining how to process it to achieve target
properties," said Arro'yave. "Here, the framework looked not only at
the chemical composition of candidate materials, but also the processing necessary to attain the properties of interest."

========================================================================== Story Source: Materials provided by Texas_A&M_University. Original written
by Michelle Revels. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. W. Trehern, R. Ortiz-Ayala, K.C. Atli, R. Arroyave,
I. Karaman. Data-
driven shape memory alloy discovery using Artificial Intelligence
Materials Selection (AIMS) framework. Acta Materialia, 2022; 228:
117751 DOI: 10.1016/j.actamat.2022.117751 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/05/220505143808.htm

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