段老师在 Comp Mater Sci 期刊发表论文

段老师参与完成的题目为“Development of a multi-element neural network modified lattice inversion potential and application to the Ta-He system”的论文被期刊Computational Materials Science接收。

本文介绍了一种新型的神经网络修改的晶格反演势（NN-LIP）模型，用于描述钽-氦（Ta-He）相互作用。该模型结合了元素特异的原子密度描述符和晶格反演交叉势，以实现对多元素系统的精确建模。研究使用了大量数据集，涵盖了钽的基本性质、氦空位、迁移特性等信息，用于训练Ta-He系统的NN-LIP模型。研究结果表明，NN-LIP在描述Ta-He系统的复杂相互作用方面具有较高的准确性和适用性，达到了DFT级别的精度。因此，该多元素NN-LIP模型为准确捕捉元素对总体势能面的贡献提供了有效的工具。

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该文章基本信息如下：

  • 标题：Development of a multi-element neural network modified lattice inversion potential and application to the Ta-He system

  • 作者：Feifeng Wu, Xianbao Duan*, Zhaojie Wang, Yanwei Wen, Rong Chen, Aimin Zhang, Bin Shan*

  • 摘要：Under extended radiation exposure and elevated temperatures, helium (He) accumulation can compromise the integrity of Tantalum (Ta), a material showing substantial promise for nuclear fusion reactor applications. An imperative step towards understanding and enhancing the performance of Ta-based materials lies in the development of an accurate potential for Ta-He interactions. In this study, we introduce a neural network modified lattice inversion potential (NN-LIP) specifically designed for nuanced, element-specific Ta-He interactions. The conventional atomic density descriptors have been augmented to encompass sub-atomic characteristics, ensuring an accurate representation of varied local chemical environments across the energy spectrum. The lattice inversion potential for individual atoms introduces an atomic cross-potential, bolstering the transferability and robustness of NN-LIP, thereby amplifying its extrapolation capacity. Empirical calculations centered on pivotal properties of the Ta-He system affirm the precision of the multi-element NN-LIP potential. Our comprehensive dataset, spanning 19162 samples, covers a gamut from Ta bulk properties to point defects, He vacancies, and migration traits, achieving a validation set accuracy of 5.14 meV/atom. Notably, the migration barrier prediction accuracy displayed marked improvement over prior studies. The formulated multi-element NN-LIP offers a detailed examination of Ta-He interactions and holds potential for modeling analogous metal-helium interactions in nuclear substrates.

  • 期刊：Computational Materials Science

  • 链接：https://www.sciencedirect.com/science/article/abs/pii/S0927025624001204

Computational Materials Science 影响因子在2022年影响因子为3.572，在中科院分区中属于工程技术大类3区期刊。