sciai-lab/structures25-nuclear-gradients

Machine-learned orbital-free density functional theory

Predict olfactory descriptors from SMILES strings. A GINE-based graph neural network trained on the Leffingwell PMP dataset, reproducing the Principal Odor Map (POM) approach.

In pursuit of the universal functional for density functional theory (DFT), the OneDFT team from Microsoft Research AI for Science has developed the Skala-1.1 exchange-correlation functional, as introduced in Accurate and scalable exchange-correlation with deep learning, Luise et al. 2025.

In pursuit of the universal functional for density functional theory (DFT), the OneDFT team from Microsoft Research AI for Science has developed the Skala-1.0 exchange-correlation functional, as introduced in Accurate and scalable exchange-correlation with deep learning (arXiv v5), Luise et al.

Trained checkpoints for the ICLR 2026 paper A Function-Centric Graph Neural Network Approach For Predicting Electron Densities.

State-of-the-Art Geometric GNN for Predicting Molecular Properties