nequip-training

NequIP Training

Description

This skill covers training, validating, and deploying NequIP-style E(3)-equivariant neural network interatomic potentials from DFT reference data. Invoke it when building a system-specific equivariant potential, comparing NequIP against MACE, DeePMD, CHGNet, M3GNet, or universal potentials, diagnosing unstable MD despite low validation errors, or deciding whether to fine-tune an existing model versus training from scratch.

Domain Context

NequIP is a local, E(3)-equivariant graph neural network potential. It represents atoms as nodes, neighbor pairs within a finite cutoff as edges, and internal features as irreducible representations (irreps) of the rotation group. The total energy is invariant to translation, rotation, and atom indexing; forces transform equivariantly because they are gradients of the energy with respect to positions.

The main physical approximation is locality: each atomic energy contribution depends on the environment within a cutoff radius and a finite number of message-passing layers. This is appropriate for many condensed-phase, molecular, and materials systems when the DFT training set covers the relevant local environments. It is incomplete for long-range electrostatics, charged defects, polar surfaces, dispersion-dominated systems, and electronic phenomena that are not encoded in the structure or training labels unless the workflow includes special treatment. [EXPERT REVIEW NEEDED]

Equivariance gives NequIP strong data efficiency because the model does not need to learn rotational symmetry from augmented data. This is especially useful for small to medium system-specific datasets with accurate forces. Data efficiency does not remove the need for broad configuration coverage: a model trained only on near-equilibrium structures can still fail catastrophically in MD, surfaces, defects, high-temperature events, or compression/expansion regimes.

NequIP is usually trained on DFT energies and forces, and sometimes stress/virial information where the software version and deployment path support it. Stress support, LAMMPS deployment, Allegro integration, and configuration keys are version-dependent. Mark every production workflow with the exact NequIP, e3nn, PyTorch, ASE, CUDA, and deployment plugin versions. [EXPERT REVIEW NEEDED]