Accession Number:

AD1096609

Title:

High-Throughput Experimentally and Computationally Guided Discovery of Next Generation High-Temperature Shape Memory Alloys

Descriptive Note:

Technical Report,01 May 2016,30 Apr 2019

Corporate Author:

Harvard University Cambridge United States

Personal Author(s):

• Vlassak,Joost J
• Arroyave,Raymundo

Report Date:

2019-07-31

Pagination or Media Count:

29.0

Abstract:

We have developed a framework for the discovery of novel high-temperature shape memory alloys by combining high-throughput experimental techniques, high-throughput computational methods, and statistical analysismachine learning techniques. We have fabricated a novel resistance sensor array for the combinatorial screening of shape memory alloys, built a binary alloy database, and developed an efficient experiment design technique. An in-depth experimental-computational study on a broad range of Cu-Zr-X shape memory alloys shows that DFT simulations are a useful tool to guide the experimental development of shape memory alloys provided relevant energy terms are taken into account.

Descriptors:

• high temperature
• SHAPE MEMORY ALLOYS
• statistical analysis
• machine learning
• databases
• density functional theory
• calorimetry
• first principles calculations
• phase transformations
• temperature
• microstructure
• Martensite
• austenite
• Binary Alloys
• experimental design

Subject Categories:

• Metallurgy and Metallography
• Physical Chemistry

Distribution Statement:

APPROVED FOR PUBLIC RELEASE