JARVIS-DFT

The 'JARVIS-DFT' dataset is a benchmark that contains computed properties of crystalline materials, used to evaluate the performance of machine learning models in predicting crystal properties.

Papers using JARVIS-DFT (5)

DenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and molecules2025 · 38 citesBeyond Structure: Invariant Crystal Property Prediction with Pseudo-Particle Ray Diffraction2025Accelerating Material Property Prediction using Generically Complete Isometry Invariants2024 · 9 citesCrystalformer: Infinitely Connected Attention for Periodic Structure Encoding2024 · 5 citesMaterial Property Prediction using Graphs based on Generically Complete Isometry Invariants2022 · 1 cites