Automatically growing global reactive neural network potential energy surfaces: A trajectory-free active learning strategy.

@article{Lin2020AutomaticallyGG,
  title={Automatically growing global reactive neural network potential energy surfaces: A trajectory-free active learning strategy.},
  author={Qidong Lin and Yaolong Zhang and Bin Zhao and Bin Jiang},
  journal={The Journal of chemical physics},
  year={2020},
  volume={152 15},
  pages={
          154104
        }
}
An efficient and trajectory-free active learning method is proposed to automatically sample data points for constructing globally accurate reactive potential energy surfaces (PESs) using neural networks (NNs). Although NNs do not provide the predictive variance as the Gaussian process regression does, we can alternatively minimize the negative of the squared difference surface (NSDS) given by two different NN models to actively locate the point where the PES is least confident. A batch of… 
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20 Citations

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