NEMORA: Neural Equivariant Multipole Operators for Long-Range Atomistic Learning
NEMORA introduces a neural network architecture to enhance classical long-range methods like FMM for large atomistic systems. It replaces fixed analytical components with learned, equivariant operators and charges that operate on an adaptive spatial hierarchy. This allows the model to capture complex tensorial interactions efficiently, achieving massive accuracy gains while maintaining linear time complexity.