Work

An Interpretable Boosting-Based Predictive Model for Transformation Temperatures of Shape Memory Alloys

Year: 2022

Type: article

Source: Computational Materials Science

DOI: 10.1016/j.commatsci.2023.112225

Authors: Sina Hossein Zadeh, Amir Behbahanian, John Broucek, Mingzhou Fan, Guillermo Vazquez, Mohammad Noroozi, William Trehern, Xiaoning Qian, Ibrahim Karaman, Raymundo Arroyave

Institutions: Texas A&M University, University of South Florida

Language: English

Topic: Shape Memory Alloy Transformations

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: bronze

Funders: National Science Foundation, Qatar National Research Fund

Awards: DMREF: AI-Guided Accelerated Discovery of Multi-Principal Element Multi-Functional Alloys, Probing Microstructure-Martensitic Transformation Couplings in Metamagnetic Shape Memory Alloys, NRT-DESE: Data-Enabled Discovery and Design of Energy Materials, G7643256126, G858662124

OpenAlex ID: W4367171967

FWCI: 1.196

Cites: 84

Cited by: 21

Related to: 10