Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting

@article{Thaler2021LearningNN,
  title={Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting},
  author={Stephan Thaler and Julija Zavadlav},
  journal={Nature Communications},
  year={2021},
  volume={12}
}
In molecular dynamics (MD), neural network (NN) potentials trained bottom-up on quantum mechanical data have seen tremendous success recently. Top-down approaches that learn NN potentials directly from experimental data have received less attention, typically facing numerical and computational challenges when backpropagating through MD simulations. We present the Differentiable Trajectory Reweighting (DiffTRe) method, which bypasses differentiation through the MD simulation for time-independent… 

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