Phase diagram of Mn8Al8C; eabovehull: 0.363531 eV/atom; predicted_stable: False
Cell + Ionic relaxation with Orb v3; 0.03 eV/Å threshold; final energy = -110.4407 eV; energy change = -38.0076 eV; symmetry: P4/m → P1
Crystal structure generated by GEPA optimization (iteration 2)
Phase diagram of Mn8Al8C; eabovehull: 0.213000 eV/atom; predicted_stable: False
Cell + Ionic relaxation with Orb v3; 0.03 eV/Å threshold; final energy = -112.9999 eV; energy change = -127.2521 eV; symmetry: P4/mmm → Cm
Crystal structure generated by GEPA optimization (iteration 1)
Phase diagram of Fe4CoB2P; eabovehull: 0.282950 eV/atom; predicted_stable: False
Cell + Ionic relaxation with Orb v3; 0.03 eV/Å threshold; final energy = -243.0077 eV; energy change = -47.8533 eV; symmetry: I4/mcm → P1
Crystal structure generated by GEPA optimization (iteration 1)
Phase diagram of Mn8Al8C; eabovehull: 0.267273 eV/atom; predicted_stable: False
Cell + Ionic relaxation with Orb v3; 0.03 eV/Å threshold; final energy = -112.0774 eV; energy change = -119.1224 eV; symmetry: P4/mmm → P1
Crystal structure generated by GEPA optimization (iteration 1)
Cell + Ionic relaxation with Orb v3; 0.03 eV/Å threshold; final energy = -25.0288 eV; energy change = 0.0000 eV; symmetry: P4/mmm → P4/mmm
FeNiB (auto-selected space group: P3m1 #156)
Phonon band structure (supercell [2, 2, 2], Δ=0.01 Å); imaginary modes detected; min freq = -4.03 THz
Phase diagram of FeCoNiPt; eabovehull: 0.725386 eV/atom; predicted_stable: False
FeCoNiPt (auto-selected SG: P422 #89, calculated SG: P4/mmm #123, optimized: 9 steps, cell relaxed (isotropic))
Discovering new materials can have significant scientific and technological implications but remains a challenging problem today due to the enormity of the chemical space. Recent advances in machine learning have enabled data-driven methods to rapidly screen or generate promising materials, but these methods still depend heavily on very large quantities of training data and often lack the flexibility and chemical understanding often desired in materials discovery. This paper introduces LLMatDesign, a novel language-based framework for interpretable materials design powered by large language models (LLMs).
Phase diagram of Al3FeSi; eabovehull: 1.279308 eV/atom; predicted_stable: False
Phase diagram of Al3FeSi; eabovehull: 1.279327 eV/atom; predicted_stable: False