Official Resources
- Homepage: https://github.com/jax-md/jax-md
- Documentation: https://jax-md.readthedocs.io/
- Source Repository: https://github.com/jax-md/jax-md
- License: Apache-2.0
Overview
JAX-MD is a framework for differentiable physics simulations with a focus on molecular dynamics. Built on JAX, it enables end-to-end differentiable simulations, automatic differentiation through dynamics, and seamless integration with machine learning.
Scientific domain: Differentiable MD, machine learning, GPU-accelerated simulations
Target user community: Researchers combining MD with machine learning
Theoretical Methods
- Differentiable molecular dynamics
- Automatic differentiation
- Neural network potentials
- End-to-end learning
- Various integrators (NVE, NVT, NPT)
Capabilities (CRITICAL)
- End-to-end differentiable MD
- JAX automatic differentiation
- GPU/TPU acceleration
- Neural network integration
- Multiple thermostats/barostats
- Custom potentials
Key Strengths
Differentiability:
- Full autodiff support
- Gradient through dynamics
- End-to-end training
- Loss function flexibility
JAX Backend:
- GPU/TPU acceleration
- JIT compilation
- Vectorization (vmap)
- Functional programming
Inputs & Outputs
-
Input formats:
- JAX arrays
- Python configuration
-
Output data types:
- Trajectories
- Gradients
- Observables
Interfaces & Ecosystem
- JAX: Backend
- Flax/Haiku: Neural networks
- Optax: Optimization
Advanced Features
- Autodiff: Through entire simulation
- Neural potentials: Easy integration
- Custom forces: Differentiable
- Thermostats: Nose-Hoover, Langevin
- Neighbor lists: Efficient
Performance Characteristics
- Excellent GPU performance
- JIT compilation
- Vectorization support
- Good scaling
Computational Cost
- GPU provides major speedup
- Autodiff adds overhead
- JIT compilation helps
- Overall: Efficient on GPU
Best Practices
- Use JIT compilation
- Leverage vmap for batching
- Validate gradients
- Use appropriate precision
Limitations & Known Constraints
- JAX ecosystem required
- Different programming paradigm
- Less traditional MD features
- Active development
Application Areas
- ML potential development
- Inverse design
- Parameter optimization
- Method development
- Differentiable physics
Comparison with Other Codes
- vs TorchMD: JAX-MD uses JAX, TorchMD uses PyTorch
- vs OpenMM: JAX-MD fully differentiable, OpenMM traditional MD
- vs LAMMPS: JAX-MD for ML research, LAMMPS for production
- Unique strength: Full autodiff through dynamics, JAX ecosystem (vmap, pmap, JIT), GPU/TPU
Community and Support
- Google Research development
- GitHub issues
- Growing community
- JAX ecosystem
Verification & Sources
Primary sources:
- GitHub: https://github.com/jax-md/jax-md
- S. Schoenholz & E. Cubuk, NeurIPS 2020
Secondary sources:
- JAX-MD tutorials
- JAX ecosystem documentation
- Published ML physics applications
Confidence: VERIFIED
Verification status: ✅ VERIFIED
- Source code: OPEN (GitHub, Apache-2.0)
- Academic citations: >400
- Active development: Google Research
- Growing adoption in ML physics