Efficiently Trained Deep Learning Potential for Graphane
Efficiently Trained Deep Learning Potential for Graphane
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DOI：
10.1021/acs.jpcc.1c01411
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发表时间：
2021-07-01
影响因子：
3.7
通讯作者：
Johnson, J. Karl
中科院分区：
文献类型：
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作者：
Achar, Siddarth K.;Zhang, Linfeng;Johnson, J. Karl
我们使用1000K下0.5ps密度泛函理论(DFT)分子动力学模拟的1000个快照组成的非常小的训练集，为石墨烯开发了准确而高效的深度学习潜力(DP)。我们评估了DP对未包括在训练集中的体系大小、温度和晶格应变进行外推的能力。DP的表现出人意料地好，与DFT数据相比，在声子态密度、热力学性质、速度自相关函数和直至屈服点的应力-应变曲线方面，DP的表现优于经验多体势能。这表明我们的DP可以可靠地外推到训练数据的限制之外。我们计算了石墨烷的热涨落作为体系大小的函数。我们发现，与石墨烯相比，石墨烯具有更大的热涨落，但具有大致相同的面外刚性。
We have developed an accurate and efficient deep-learning potential (DP) for graphane, which is a fully hydrogenated version of graphene, using a very small training set consisting of 1000 snapshots from a 0.5 ps density functional theory (DFT) molecular dynamics simulation at 1000 K. We have assessed the ability of the DP to extrapolate to system sizes, temperatures, and lattice strains not included in the training set. The DP performs surprisingly well, outperforming an empirical many-body potential when compared with DFT data for the phonon density of states, thermodynamic properties, velocity autocorrelation function, and stress-strain curve up to the yield point. This indicates that our DP can reliably extrapolate beyond the limit of the training data. We have computed the thermal fluctuations as a function of system size for graphane. We found that graphane has larger thermal fluctuations compared with graphene, but having about the same out-of-plane stiffness.