基于机器学习经验赝势的晶态与非晶态GeSe及GeSbTe化合物电子结构研究
Electronic structures of crystalline and amorphous GeSe and GeSbTe compounds using machine learning empirical pseudopotentials
AI总结：
针对传统经验赝势迁移性差的问题，提出机器学习经验赝势方法，在GeSe、GST相变材料上验证其可精准复现第一性原理电子结构结果，计算成本更低且对非晶相等多样原子环境仍保持精度。
摘要：
新开发的机器学习（ML）经验赝势（EP）方法借助机器学习技术克服了传统经验赝势方法迁移性差的缺陷，同时保留了其形式简洁、计算高效的优势。我们将该新方法应用于GeSe、Ge-Sb-Te（GST）化合物等二元与三元体系，这类材料是知名的非易失性相变存储及相关技术所用材料。我们利用多种GeSe与GST化合物的从头算电子能带训练集以及旋转协变描述符，生成了适用于Ge、Se、Sb、Te的可迁移经验赝势。我们验证表明，该新型机器学习模型可精准复现训练集外结构的能带与波函数，与第一性原理计算结果高度吻合。由于无需进行自洽迭代且平面波基组规模更小，该方法在实现上述精度的同时计算成本大幅降低。值得注意的是，即便面对非晶相、训练集中未明确包含的更大体系等多样的局域原子环境，该方法仍能保持精度。
英文摘要：
The newly developed machine learning (ML) empirical pseudopotential (EP) method overcomes the poor transferability of the traditional EP method with the help of ML techniques while preserving its formal simplicity and computational efficiency. We apply the new method to binary and ternary systems such as GeSe and Ge-Sb-Te (GST) compounds, well-known materials for non-volatile phase-change memory and related technologies. Using a training set of {\it ab initio} electronic energy bands and rotation-covariant descriptors for various GeSe and GST compounds, we generate transferable EPs for Ge, Se, Sb, and Te. We demonstrate that the new ML model accurately reproduces the energy bands and wavefunctions of structures outside the training set, closely matching first-principles calculations. This accuracy is achieved with significantly lower computational costs due to the elimination of self-consistency iterations and the reduced size of the plane-wave basis set. Notably, the method maintains accuracy even for diverse local atomic environments, such as amorphous phases or larger systems not explicitly included in the training set.
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