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 CrFe7; e_above_hull: 0.000000 eV/atom; predicted_stable: True

Phonon band structure (supercell [2, 2, 2], Δ=0.01 Å); no imaginary modes; min freq = -0.07 THz

Cell + Ionic relaxation with Orb v3; 0.03 eV/Å threshold; final energy = -137.4990 eV; energy change = -0.2603 eV; symmetry: R3m → Im-3m

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Phase diagram of Fe8Co4N; e_above_hull: 0.093154 eV/atom; predicted_stable: False

Phonon band structure (supercell [2, 2, 2], Δ=0.01 Å); no imaginary modes; min freq = -0.02 THz

Phonon band structure (supercell [2, 2, 2], Δ=0.01 Å); no imaginary modes; min freq = -0.01 THz

Phase diagram of Fe8Co4N; e_above_hull: 0.093253 eV/atom; predicted_stable: False

Phase diagram of Fe8Co4N; e_above_hull: 0.173013 eV/atom; predicted_stable: False

Phonon band structure (supercell [2, 2, 2], Δ=0.01 Å); imaginary modes detected; min freq = -0.64 THz

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Phonon band structure (supercell [2, 2, 2], Δ=0.01 Å); no imaginary modes; min freq = -0.06 THz

Phase diagram of Fe2Co2N; e_above_hull: 0.142871 eV/atom; predicted_stable: False

Phase diagram of Fe2Co2N; e_above_hull: 0.100092 eV/atom; predicted_stable: False

Phonon band structure (supercell [2, 2, 2], Δ=0.01 Å); no imaginary modes; min freq = -0.06 THz

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Phonon band structure (supercell [2, 2, 2], Δ=0.01 Å); no imaginary modes; min freq = -0.03 THz

Phase diagram of Fe7Co3N; e_above_hull: 0.091768 eV/atom; predicted_stable: False

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