AI-driven discovery of high-performance LiMHx (M = Sc, Ti; x = 3, 4, 5) hydrides: A first-principles investigation structural, mechanical, electronic, thermophysical, optical and hydrogen storage properties
     
Yazarlar (3)
Çağatay Yamçıçıer
Osmaniye Korkut Ata University, Türkiye
Sümeyra Yamçıçıer
Osmaniye Korkut Ata University, Türkiye
Prof. Dr. Cihan KÜRKÇÜ Kırşehir Ahi Evran Üniversitesi, Türkiye
Makale Türü Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
Dergi Adı International Journal of Hydrogen Energy (Q1)
Dergi ISSN 0360-3199 Wos Dergi Scopus Dergi
Dergi Tarandığı Indeksler SCI-Expanded
Makale Dili İngilizce Basım Tarihi 11-2025
Cilt / Sayı / Sayfa 187 / 1 / 152097–0 DOI 10.1016/j.ijhydene.2025.152097
Makale Linki https://doi.org/10.1016/j.ijhydene.2025.152097
Özet
The pursuit of sustainable energy solutions critically depends on the development of efficient and safe hydrogen storage technologies to enable the widespread adoption of hydrogen as a clean energy carrier. While complex metal hydrides show immense promise for high-density hydrogen storage, the discovery and optimization of novel materials remain a significant challenge. This study presents the first systematic computational investigation of a novel class of hydrogen storage materials: LiMH x (M= Sc, Ti; x= 3, 4, 5) hydrides. Crucially, these materials were generated by an innovative AI-based autoregressive large language modeling (LLM) tool, representing a cutting-edge approach to materials discovery. Employing Density Functional Theory (DFT) within the CASTEP software package, we comprehensively characterize their fundamental properties. These compounds demonstrate notable thermodynamic …
Anahtar Kelimeler
AI-driven materials discovery | Complex metal hydrides | Density functional theory | Hydrogen storage | Lithium scandium hydrides | Lithium titanium hydrides