Get Started
Home
Topics
Search
Library
Research questionUnder what genericity, overlap, and connectivity conditions can sparse cutoff-based multi-layer message passing universally approximate interatomic potential energy surfaces?Practical graph neural network potentials often use per-layer cutoffs smaller than the physical interaction range, relying on multiple message-passing layers to propagate information. The key difficulty is determining when these sparse local interactions capture the full neighborhood information needed for universal approximation.
AI
Machine Learning
Research Paper
Technology
Latest papersRecent research connected to this question, newest first.Why Multi-Layer Message Passing Works: Completeness Theory for Graph Neural Network Interatomic PotentialsThe evidence concerns invariant hypergraph neural networks with 3-body message passing on sparse cutoff-based graphs. The result applies to generic configurations satisfying specified overlap and connectivity conditions, with universal-approximation consequences stated for DPA3 and CHGNet.research paper · Sep 2, 2026
Related questions
How can machine-learning interatomic potentials support Hessian-dependent applications using only energy and force data?How can graph neural networks capture long-range interactions without oversmoothing or oversquashing on diverse large graphs?How can physics-informed neural networks handle stiff, multiscale ion-electronic transport while supporting inverse estimation?How can drug–target interaction models preserve weak binding-relevant biochemical patterns across molecular scales?