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• W3043300003 abstract "Machine learning techniques allow a direct mapping of atomic positions and nuclear charges to the potential energy surface with almost ab-initio accuracy and the computational efficiency of empirical potentials. In this work we propose a machine learning method for constructing high-dimensional potential energy surfaces based on feed-forward neural networks. As input to the neural network we propose an extendable invariant local molecular descriptor constructed from geometric moments. Their formulation via pairwise distance vectors and tensor contractions allows a very efficient implementation on graphical processing units (GPUs). The atomic species is encoded in the molecular descriptor, which allows the restriction to one neural network for the training of all atomic species in the data set. We demonstrate that the accuracy of the developed approach in representing both chemical and configurational spaces is comparable to the one of several established machine learning models. Due to its high accuracy and efficiency, the proposed machine-learned potentials can be used for any further tasks, for example the optimization of molecular geometries, the calculation of rate constants or molecular dynamics." @default.
• W3043300003 created "2020-07-23" @default.
• W3043300003 creator A5047160329 @default.
• W3043300003 creator A5056979833 @default.
• W3043300003 date "2020-07-16" @default.
• W3043300003 modified "2023-10-17" @default.
• W3043300003 title "Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentials" @default.
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• W3043300003 doi "https://doi.org/10.1021/acs.jctc.0c00347" @default.
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• W3043300003 hasPublicationYear "2020" @default.
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