电子态密度的机器学习模型的自适应能量参考
Adaptive energy reference for machine-learning models of the electronic density of states
AI总结：
针对电子态密度机器学习模型中绝对能量参考不明确的问题，提出自适应优化每个结构能量参考的训练方案，持续提升不同材料和模型架构下的模型质量与可迁移性。
摘要：
电子态密度（DOS）提供了材料中电子能级分布的信息，可用于近似其光学和电子性质，从而指导计算材料设计。鉴于其有用性和相对简单性，它是最早被用作超越原子间势的机器学习方法目标的电子性质之一。一个微妙但重要的点，在凝聚态物理界已被充分认识，但在构建数据驱动模型时通常被忽视，即对于体相构型，单粒子能级的绝对能量参考是不明确的。只有能量差才有意义，由DOS导出的量通常与绝对对齐无关。我们引入了一种自适应方案，在训练过程中优化每个结构的能量参考，并表明与传统的能量参考选择相比，对于不同类别的材料和不同的模型架构，它持续提高了机器学习模型的质量。在实际层面上，我们将改进的性能归因于这种自对齐方案匹配DOS中最显著特征的能力。更广泛地说，我们相信这项工作强调了将物理目标本质的见解纳入机器学习模型架构定义和适当性能指标的重要性，这转化为更好的可迁移性和整体性能。
英文摘要：
The electronic density of states (DOS) provides information regarding the distribution of electronic energy levels in a material, and can be used to approximate its optical and electronic properties and therefore guide computational material design. Given its usefulness and relative simplicity, it has been one of the first electronic properties used as target for machine-learning approaches going beyond interatomic potentials. A subtle but important point, well-appreciated in the condensed matter community but usually overlooked in the construction of data-driven models, is that for bulk configurations the absolute energy reference of single-particle energy levels is ill-defined. Only energy differences matter, and quantities derived from the DOS are typically independent on the absolute alignment. We introduce an adaptive scheme that optimizes the energy reference of each structure as part of the training process, and show that it consistently improves the quality of ML models compared to traditional choices of energy reference, for different classes of materials and different model architectures. On a practical level, we trace the improved performance to the ability of this self-aligning scheme to match the most prominent features in the DOS. More broadly, we believe that this work highlights the importance of incorporating insights into the nature of the physical target into the definition of the architecture and of the appropriate figures of merit for machine-learning models, that translate in better transferability and overall performance.
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