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- W4385261570 abstract "In this work, we present a general machine learning approach for full-dimensional potential energy surfaces for tetra-atomic systems. Our method employs an active learning scheme trained on {it ab initio} points, which size grows based on the accuracy required. The training points are selected based on molecular dynamics simulations, choosing the most suitable configurations for different collision energy and mapping the most relevant part of the potential energy landscape of the system. The present approach does not require long-range information and is entirely general. As an example, we provide the full-dimensional AlF-AlF potential energy surface, requiring $lesssim 0.1%$ of the configurations to be calculated {it ab initio}. Furthermore, we analyze the general properties of the AlF-AlF system, finding key difference with other reported results on CaF or bi-alkali dimers." @default.
- W4385261570 created "2023-07-26" @default.
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- W4385261570 date "2023-07-23" @default.
- W4385261570 modified "2023-10-14" @default.
- W4385261570 title "Molecular dynamics-driven global tetra-atomic potential energy surfaces: Application to the AlF dimer" @default.
- W4385261570 doi "https://doi.org/10.1063/5.0169080" @default.
- W4385261570 hasPublicationYear "2023" @default.
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