ref [21] · 2604.24816 · notice #3318 · dispute
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, Ying, P., Xu, K., Liang, T., Wang, Y., Zeng, Z., Wu, X., Zhou, W., Xiong, S., Chen, S. and Fan, Z. 2024. Molecular dynamics simulations of heat transport using machine-learned potentials: A mini-review and tutorial on GPUMD with neuroevolution potentials. Journal of Applied Physics. 135, 16 (Apr. 2024), 161101. https://doi.org/10.1063/5.0200833. [21] Ying, P., Qian, C., Zhao, R., Wang, Y., Xu, K., Ding, F., Chen, S. and Fan, Z. 2025. Advances in modeling complex materials: The rise of neuroevolution potentials. Chemical Physics Reviews. 6, 1 (Mar. 2025), 011310. https://doi.org/10.1063/5.0259061. [22] Schaul, T., Glasmachers, T. and Schmidhuber, J. 2011. High dimensions and heavy tails for natural evolution strategies.
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Ying, P., Xu, K., Liang, T., Wang, Y., Zeng, Z., Wu, X., Zhou, W., Xiong, S., Chen, S. and Fan, Z. 2024. Molecular dynamics simulations of heat transport using machine-learned potentials: A mini-review and tutorial on GPUMD with neuroevolution potentials. Journal of Applied Physics. 135, 16 (Apr. 2024), 161101. https://doi.org/10.1063/5.0200833. [21] Ying, P., Qian, C., Zhao, R., Wang, Y., Xu, K., Ding, F., Chen, S. and Fan, Z. 2025. Advances in modeling complex materials: The rise of neuroevolution potentials. Chemical Physics Reviews. 6, 1 (Mar. 2025), 011310. https://doi.org/10.1063/5.0259061. [22] Schaul, T., Glasmachers, T. and Schmidhuber, J. 2011. High dimensions and heavy tails for natural evolution strategies