Linfeng Zhang, De-Ye Lin, Han Wang, Roberto Car, Weinan E., Phys. Rev. Mat. 3, 023804 (2019)
URL: https://doi.org/10.1103/PhysRevMaterials.3.023804
Linfeng Zhang, De-Ye Lin, Han Wang, Roberto Car, Weinan E., Phys. Rev. Mat. 3, 023804 (2019)
Abstract An active learning procedure called deep potential generator (DP-GEN) is proposed for the construction of accurate and transferable machine learning-based models of the potential energy surface (PES) for the molecular modeling of materials. This procedure consists of three main components: exploration, generation of accurate reference data, and training. Application to the sample systems of Al, Mg, and Al-Mg alloys demonstrates that DP-GEN can produce uniformly accurate PES models with a minimal number of reference data.
URL: https://doi.org/10.1103/PhysRevMaterials.3.023804
Zhutian Ding and Annabella Selloni, J. Phys. Chem. C 2020, 124, 49, 26741–26747 (2020) URL: https://pubs.acs.org/doi/abs/10.1021/acs.jpcc.0c07581
D Lu, H Wang, M Chen, J Liu, L Lin, R Car, W. E, W Jia, L Zhang Comp. Phys. Comm., vol […]
Yixiao Chen, Linfeng Zhang, Han Wang, and Weinan E, J. Phys. Chem. A 2020, 124, 35, 7155–7165 (2020) URL: https://doi.org/10.1021/acs.jpca.0c03886
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