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arXiv 1508.04899cond-mat.mtrl-sci
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晶体硅和液态硅神经网络势中基的稀疏选择

Sparse selection of bases in neural-network potential for crystalline and liquid Si

Ryo Kobayashi, Tomoyuki Tamura, Ichiro Takeuchi, Shuji Ogata

摘要:

本文采用前向逐步回归技术,在保证势函数精度的前提下,减少了基元数目,建立了晶体硅和液态硅的神经网络原子间相互作用势函数。这种构造神经网络势的方法使我们能够用较少的重要基来构造精确的原子间势。体结晶性能的评估,和液态硅的动态性能之间的神经网络势和从头计算结果显示出良好的协议。

英文摘要:

The neural-network interatomic potential for crystalline and liquid Si has been developed using the forward stepwise regression technique to reduce the number of bases with keeping the accuracy of the potential. This approach of making the neural-network potential enables us to construct the accurate interatomic potentials with less and important bases selected systematically and less heuristically. The evaluation of bulk crystalline properties, and dynamic properties of liquid Si show good agreements between the neural-network potential and ab-initio results.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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