Fine-tuning CHGNet on OMDB data and performing site substitution on QMOF prototypes yields novel highly magnetic MOFs.
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4 Pith papers cite this work. Polarity classification is still indexing.
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Conditional generative models double the rate of stable novel MAX phase structures by steering generation with MXene derivative counts and A-site binding energy surrogates, yielding five DFT-stable candidates out of ten tested.
ASRNNs recover Hamiltonian dynamics and symbolic equations from trajectories with only two irregularly spaced noisy points by preserving symplectic structure without derivative estimation.
Element-Weighted KANs achieve state-of-the-art accuracy on formation energy, band gap, and work function while revealing periodic-table-aligned chemical trends through their learnable activation functions.
citing papers explorer
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Generation of magnetic metal-organic frameworks
Fine-tuning CHGNet on OMDB data and performing site substitution on QMOF prototypes yields novel highly magnetic MOFs.
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Conditional Generative Models Enable Targeted Exploration of MAX Phase Design Space
Conditional generative models double the rate of stable novel MAX phase structures by steering generation with MXene derivative counts and A-site binding energy surrogates, yielding five DFT-stable candidates out of ten tested.
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Machine Learning Hamiltonian Dynamical Systems with Sparse and Noisy Data
ASRNNs recover Hamiltonian dynamics and symbolic equations from trajectories with only two irregularly spaced noisy points by preserving symplectic structure without derivative estimation.
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Interpretation of Crystal Energy Landscapes with Kolmogorov-Arnold Networks
Element-Weighted KANs achieve state-of-the-art accuracy on formation energy, band gap, and work function while revealing periodic-table-aligned chemical trends through their learnable activation functions.