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Differentiable EPR distance distribution modeling in JAX

Project description

📻 diff-epr: Differentiable EPR/DEER Simulation in JAX

Tests License: MIT JAX

diff-epr provides differentiable kernels for simulating EPR distance distributions (DEER/PELDOR) from structural ensembles and spin-label rotamers.


🎯 Features

  • Spin-Label Modeling: Differentiable distance calculations between paramagnetic centers.
  • Orientation Selection: Support for the Polyhach 5-angle formula (Polyhach et al., 2007) to model relative domain orientations.
  • Time-Domain Simulation: Simulate DEER modulation traces $V(t)$ with parameterizable background decay and modulation depth.
  • Rotamer Library Integration: Support for weighted rotamer averages in distance distribution calculations.
  • Hardware Acceleration: GPU-optimized distance kernels via JAX.

🏗️ Technical Architecture

  • Backend: JAX (XLA-compiled).
  • Physics: Dipolar coupling frequency ($\omega$) based kernels.
  • Performance: $O(N)$ scaling for distance distribution integration.

🚀 Roadmap

  • Core DEER trace simulation kernels.
  • Background decay and modulation depth parameters.
  • Integration with MMM (Multi-Scale Modeling of Macromolecules) rotamer libraries.
  • Full orientation-dependence support.

🚀 Installation

pip install diff-epr

🧪 Scientific Validation

  • Dipolar Frequency Parity: Coupling frequencies verified against the Pake pattern $1/r^3$ dependence.
  • Time-Domain Accuracy: DEER traces validated for parity against standard simulation tools (e.g., DeerAnalysis/MMM).
  • Auto-Diff Gradients: Differentiable distance-to-signal kernels verified with JAX grad.

🔗 Related Projects

diff-epr is part of the differentiable biophysics ecosystem:


📖 Citation

@software{diff_epr,
  author  = {Elkins, George},
  title   = {diff-epr: Differentiable EPR/DEER simulation in JAX},
  year    = {2026},
  url     = {https://github.com/elkins/diff-epr},
  version = {0.1.0}
}

⚖️ License

MIT

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