MSGID: 1:317/3 6274a4e5   
   PID: hpt/lnx 1.9.0-cur 2019-01-08   
   TID: hpt/lnx 1.9.0-cur 2019-01-08   
    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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