jax-md/jax-md

Health v2 · maintenance only

Adoption not part of the score

1458 stars · 248 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

JAX MD is a Python library for molecular dynamics simulations built on JAX, making them hardware accelerated on CPU, GPU, and TPU and end-to-end differentiable. It is a functional, data-driven research library for computational condensed matter physics and materials simulation.

Use cases

• simulate molecular dynamics of materials on GPU or TPU
• run differentiable physics simulations end-to-end in Python
• compute forces and energies with automatic differentiation
• study how small-scale interactions produce large-scale material behavior
• prototype molecular simulation code without writing specialized CUDA
• train machine learning models coupled to physics simulations

When to choose

• you need differentiable molecular dynamics integrated with JAX/ML workflows
• you want hardware-accelerated simulations without maintaining separate CPU/GPU code
• you are doing research in computational physics or materials science in Python

When to avoid

• you need a mature, feature-complete production MD package like LAMMPS or HOOMD Blue
• you require API stability - the project is a research effort with possible breaking changes
• you work outside the JAX/Python ecosystem

Facets

library · maturity active

simulation machine-learning gpu-computing math simulation chemistry machine-learning python cross-platform molecular-dynamics jax differentiable-programming computational-physics materials-science physics gpu

Member repositories

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markdown · JSON · MCP: product_card(name="jax-md/jax-md")