REANN: A PyTorch-based end-to-end multi-functional deep neural network package for molecular, reactive, and periodic systems.

@article{Zhang2021REANNAP,
  title={REANN: A PyTorch-based end-to-end multi-functional deep neural network package for molecular, reactive, and periodic systems.},
  author={Yaolong Zhang and Jun Xia and Bin Jiang},
  journal={The Journal of chemical physics},
  year={2021},
  volume={156 11},
  pages={
          114801
        }
}
In this work, we present a general purpose deep neural network package for representing energies, forces, dipole moments, and polarizabilities of atomistic systems. This so-called recursively embedded atom neural network model takes advantages of both the physically inspired atomic descriptor based neural networks and the message-passing based neural networks. Implemented in the PyTorch framework, the training process is parallelized on both the central processing unit and the graphics… 
View on PubMed
arxiv.org
4 Citations

4 Citations

GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations.

It is shown that the NEP approach not only achieves above-average accuracy but also is far more computationally efficient than state-of-the-art MLPs, and that the gpumd package is a promising tool for solving challenging problems requiring highly accurate, large-scale atomistic simulations.

Atomistic Insights into the Oxidation of Flat and Stepped Platinum Surfaces Using Large-Scale Machine Learning Potential-Based Grand-Canonical Monte Carlo

Atomistic neural network representations for chemical dynamics simulations of molecular, condensed phase, and interfacial systems: Efficiency, representability, and generalization

Vibrational Relaxation of Highly Vibrationally Excited Molecules Scattered from Au(111): Role of the Dissociation Barrier

References

SHOWING 1-2 OF 2 REFERENCES

Advances in Neural Information Processing Systems 32 (NeurIPS 2019), edited by H

  • Wallach et al. (Curran Associates Inc.,
  • 2019

Kondor, in Advances in Neural Information Processing Systems 32, edited by H

  • Wallach et al. (Curran Associates, Inc.,
  • 2019