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.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Explicit supersaturation conditioning in CRNN surrogates for Allen-Cahn crystal growth yields higher-fidelity predictions than implicit conditioning from mini-sequences, with better scalability.
citing papers explorer
-
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.
-
Neural surrogates for crystal growth dynamics with variable supersaturation: explicit vs. implicit conditioning
Explicit supersaturation conditioning in CRNN surrogates for Allen-Cahn crystal growth yields higher-fidelity predictions than implicit conditioning from mini-sequences, with better scalability.