A random-sampling high dimensional model representation neural network for building potential energy surfaces.

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SchNet: A continuous-filter convolutional neural network for modeling quantum interactions (2017, methodology): “Previous work has used neural networks and Gaussian processes applied to hand-crafted features to ﬁt potential energy surfaces [11–16].”
Integrating Physics-Based Modeling with Machine Learning: A Survey (2020, unknown): “In chemical physics, Manzhos et al. [169] first showed that NNs could be used to represent component functions within high dimensional model representations (HDMR) of energy potentials.”