AI 中文总结
研究从电子光谱识别石墨烯多纳米气泡构型的难题，利用物理约束机器学习框架，依据态密度光谱解码，通过神经分解模型和优化基系数来确定构型，能准确重建且抗干扰，为表征相关纳米结构提供可解释途径。
AI 中文摘要
从电子光谱中识别多个石墨烯纳米气泡具有挑战性，因为它们的应变诱导特征会重叠。我们开发了一个物理约束的机器学习框架，可从态密度（DOS）光谱中解码纳米气泡构型。对于空间分离的纳米气泡，先前的全量子输运计算表明，多气泡DOS在数值上等同于组成单气泡光谱的归一化总和。我们将这种经过验证的加性关系编码在一个紧凑的神经分解模型中。对于每个目标光谱，独立优化基系数，所得权重直接识别组成几何形状。该方法能准确重建复杂度不断增加的构型，对重复成分、不完整基字典和模拟测量噪声具有鲁棒性。该框架为表征应变工程石墨烯纳米结构提供了一条可解释的途径，并且可能扩展到具有加性光谱响应的其他量子材料。
英文摘要
Identifying multiple graphene nanobubbles from electronic spectra is challenging because their strain-induced features overlap. We develop a physics-constrained machine-learning framework that decodes nanobubble configurations from density-of-states (DOS) spectra. For spatially separated nanobubbles, previous full quantum-transport calculations established that the multi-bubble DOS is numerically equivalent to the normalized sum of the constituent single-bubble spectra. We encode this validated additive relation in a compact neural decomposition model. For each target spectrum, the basis coefficients are optimized independently, and the resulting weights directly identify the constituent geometries. The method accurately reconstructs configurations of increasing complexity and remains robust to repeated constituents, incomplete basis dictionaries, and simulated measurement noise. The framework provides an interpretable route for characterizing strain-engineered graphene nanostructures and may extend to other quantum materials with additive spectral responses.
Comments16 pages (single-column), 4 figures
