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SevenNet

SevenNet provides graph neural network interatomic potentials, pretrained models, fine-tuning interfaces and ASE/LAMMPS integration.

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Overview

It combines learned atomistic interactions with simulation interfaces. Its parallel molecular dynamics workflow uses LAMMPS; it is a potential-model project rather than an autonomous agent.

Limitations

Follow the documented LAMMPS, CUDA and model dependency requirements. Check checkpoint coverage and terms separately from the code license.

Key Features

  • Pretrained potentials and fine-tuning
  • ASE and LAMMPS interfaces

Use Cases

  • Study learned potentials for materials
  • Evaluate molecular dynamics with a suitable checkpoint

How to Use

Read the installation and pretrained-model guides, choose a suitable checkpoint and start with an ASE example. Configure LAMMPS for the parallel workflow.

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Works with

Used in Recipes

Community

Fixed-Cell Crystal Relaxation with SevenNet and ASE

Attach an inspected SevenNet calculator to an ASE crystal, compute energy and forces, and perform a bounded fixed-cell relaxation with explicit convergence records.

SevenNet + ASE

Materials Research

Level: Intermediate Cost: Free Privacy: Local ~45 min

Python

View Setup →

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