# AMARO: All Heavy-Atom Transferable Neural Network Potential
This repository hosts the trained checkpoint for **AMARO v1.0**.
All-atom molecular simulations provide detailed insight into macromolecular phenomena, but their computational cost limits the exploration of complex biological processes.
**AMARO**, **Advanced Machine-learning Atomic Representation Omni-force-field**, is a neural network potential that combines the O(3)-equivariant message-passing architecture **TensorNet** with a coarse-graining map that excludes hydrogen atoms.
AMARO demonstrates that coarse-grained neural network potentials can be trained without explicit prior-energy terms while retaining stable protein dynamics, scalability, and transferability.
## Representation
AMARO uses a **no-hydrogen, no-water mapping**. Each retained bead corresponds to one protein heavy atom.
For each retained heavy atom, the reference force is constructed as the sum of:
1. the force acting on the heavy atom; and
2. the fo…
