GMSAC：改良型遺伝的アルゴリズムと埋め込み原子法による固体および合金のグローバル最小構造探索のための新しいソフトウェア

The global-minimum (GM) structure of alloys and compounds determines their physical, chemical, and mechanical properties, making its accurate identification critical for materials design and engineering. However, the exponential growth of possible atomic configurations with system size and the complexity of interatomic interactions pose major challenges to traditional structure-search methods. This work develops a new GMSAC package to address these challenges by integrating an improved genetic algorithm (GA) with the embedded atom method (EAM) for the structural prediction of alloys. GMSAC optimizes the GA workflow with symmetry-aware duplicate identification, adaptive operator probabilities and maximizing the fitness of population. The EAM potential is employed to rapidly calculate interatomic energies, balancing computational efficiency and prediction accuracy. Validation tests on eight binary alloy bulks (Al-Ag, Al-Cu, Au-Cu, Au-Pd, Pd-Al, Pd-Cu, Pt-Cu and Pt-Pd) demonstrate the good performance of GMSAC, which successfully maps convex hulls, identifies the stable structures per system, and locates GM structures with the lowest formation energies (e.g., Al₂₀Cu₁₂ for Al-Cu with -0.108 eV/atom). This work provides a new tool to accelerate the discovery of high-performance alloys and compounds.