MODEL LEARNING PATH

NequIP

NequIP builds an invariant atomic energy from equivariant geometric features. Summing atomic contributions gives system energy, and differentiating that energy yields conservative forces.

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Made E(3)-equivariant tensor message passing a compelling approach to data-efficient, conservative learned interatomic potentials, with subsequent architectures and simulation systems building on the approach.

Chapters

  1. Describe atoms and neighbors

    Species, positions and cell information determine neighbor geometry and initial scalar features.

    Construct neighbor geometry with species and periodic-cell information.

  2. Learn equivariant interactions

    Radial features and spherical harmonics drive tensor-product messages and symmetry-compatible gated updates.

    Distinguish radial scalar features from equivariant angular messages.

  3. Read a system energy

    Invariant scalar channels yield atom energies, which sum to the system’s total energy.

    Sum invariant atomic energies into a system energy.

  4. Differentiate for forces

    Coordinate derivatives of the same total energy give the force prediction.

    Obtain forces as the negative coordinate derivative of energy.

All exercises in this guide