Computational Design of Thermally Stable Nanoprecipitates in Al-Zn-Mg Alloys: Insights From High-Throughput DFT and Machine Learning.

Saved in:
Bibliographic Details
Title: Computational Design of Thermally Stable Nanoprecipitates in Al-Zn-Mg Alloys: Insights From High-Throughput DFT and Machine Learning.
Authors: Chiu YN; Department of Materials Science and Engineering, National Cheng Kung University, Tainan, Taiwan., Yen SY; Department of Materials Science and Engineering, National Cheng Kung University, Tainan, Taiwan., Lin WT; Department of Materials Science and Engineering, National Cheng Kung University, Tainan, Taiwan., Yu CY; China Steel Corporation (CSC), Aluminum Prod R&D Sect, New Mat R&D Dept, Kaohsiung, Taiwan., Lin SK; Department of Materials Science and Engineering, National Cheng Kung University, Tainan, Taiwan.; Hierarchical Green-Energy Materials (Hi-GEM) Research Center, National Cheng Kung University, Tainan, Taiwan.; Program on Smart and Sustainable Manufacturing, Academy of Innovative Semiconductor and Sustainable Manufacturing, National Cheng Kung University, Tainan, Taiwan.; Core Facility Center, National Cheng Kung University, Tainan, Taiwan.
Source: Small (Weinheim an der Bergstrasse, Germany) [Small] 2026 Mar; Vol. 22 (17), pp. e11883. Date of Electronic Publication: 2026 Feb 22.
Publication Type: Journal Article
Journal Info: Publisher: Wiley-VCH Country of Publication: Germany NLM ID: 101235338 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1613-6829 (Electronic) Linking ISSN: 16136810 NLM ISO Abbreviation: Small Subsets: MEDLINE; PubMed not MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1613-6829
DOI:10.1002/smll.202511883