Local motif-guided discovery of hydrogen-stabilizing environments in refractory high-entropy alloys
Computational Materials Science, cilt.275, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 275
- Basım Tarihi: 2026
- Doi Numarası: 10.1016/j.commatsci.2026.115094
- Dergi Adı: Computational Materials Science
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Applied Science & Technology Source, Chimica, Compendex, INSPEC, Academic Search Ultimate (EBSCO), Engineering Source (EBSCO)
- Anahtar Kelimeler: Density-functional tight-binding, High-entropy alloy, High-throughput screening, Hydrogen solution energy, Hydrogen storage, Local chemical motif
- Ankara Üniversitesi Adresli: Evet
Özet
Hydrogen storage in high-entropy alloys is governed by local environments that nominal composition alone cannot capture. Here, we develop a local motif-guided high-throughput screening framework for hydrogen-stabilizing environments in refractory high-entropy alloys. Four BCC-like Ti–V–Nb–Zr-based families were constructed to probe Cr-, Hf-, Mo-, and Al-substituted chemical spaces. From 500 chemically disordered 16-atom configurations, 420 DFTB-converged structures were retained, enabling 16,800 single-H insertion calculations. The fixed-host insertion-energy landscapes show strong family dependence: Ti–V–Nb–Zr–Cr and Ti–V–Nb–Zr–Al exhibit larger fractions of low-energy sites and more pronounced negative-energy tails than the Hf- and Mo-containing families. Nearest-neighbor motif analysis identifies Ti–V–Zr-rich environments as transferable favorable-site backbones and Cr-rich motifs as deep-stabilizing environments. In the Al-substituted family, favorable H incorporation arises mainly through redistribution of Ti–V–Nb–Zr motifs. DFT/PBE validation and machine learning support the predictive value of local chemical and geometric descriptors.