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Neural networks (NNs) often assign high confidence to their predictions, even for points far out-of-distribution, making uncertainty quantification (UQ) a challenge.
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Unke, O.T., Meuwly, M.: PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges. Journal of Chemical Theory and Computation 15
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Mueller, T., Hernandez, A., Wang, C.: Machine learning for interatomic potential models. The Journal of Chemical Physics 152
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2021
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Jakse, N., Sandberg, J., Granz, L.F., Saliou, A., Jarry, P., Devijver, E., Voigtmann, T., Horbach, J., Meyer, A.: Machine learning interatomic potentials for aluminium: application to solidification phenomena. Journal of Physics: Condensed Matter (2022) https://doi.org/10.1088/1361-648x/ac9d7d
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Fu, X., Wu, Z., Wang, W., Xie, T., Keten, S., Gomez-Bombarelli, R., Jaakkola, T.: Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations (2022) https://doi.org/10.48550/arxiv.2210.07237
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2023
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Tan, A.R., Urata, S., Goldman, S., Gómez-Bombarelli, R.: Single-model uncertainty quantification in neural network potentials does not consistently outperform model ensembles. Materials Cloud Archive (2023) https://doi.org/10.24435/materialscloud:55-sd
2023
Closest in time.