Why Multi-Layer Message Passing Works: Completeness Theory for Graph Neural Network Interatomic Potentials
cs.LG, math-ph, math.MP, physics.chem-ph, physics.comp-ph
Submitted: 2026-09-01
Updated: 2026-09-02
Terminology
Sources
- A foundation model for atomistic materials chemistry
- A Galois theorem for machine learning: Functions on symmetric matrices and point clouds via lightweight invariant features
- Foundation Models for Atomistic Simulation of Chemistry and Materials
- On the Completeness of Invariant Geometric Deep Learning Models
- On the Expressive Power of Sparse Geometric MPNNs
- Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds
- A Graph Neural Network for the Era of Large Atomistic Models
Related papers
- Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
- AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
- Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
- Convergence issues in Relational Concept Analysis based on AOC-posets
- Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
- Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks