发表机构
The University of Chicago; Argonne National Laboratory（芝加哥大学; 阿贡国家实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过多任务图神经网络，利用核心空穴产生与俄歇衰变间的物理联系，共同预测俄歇电子与X射线光电子能谱，验证了多任务学习在光谱预测中的潜力。
AI 中文摘要
俄歇电子能谱长期以来一直作为X射线光电子能谱的第二种模态，用于分辨具有重叠核心电子结合能的化学状态。然而，分析俄歇谱面临其复杂性及模拟计算成本高昂的挑战。在此，我们证明核心空穴的产生与其相应的俄歇-迈特纳衰变之间的物理联系（即任务间关系）能够通过训练一个多任务图神经网络实现归纳知识迁移，该网络从共同的图嵌入中预测这两个可观测量。两个任务的损失通过不确定性加权过程以学习到的权重进行组合。总体而言，单任务和多任务模型在大多数情况下都能以良好的精度预测计算和实验的俄歇线形。两种任务模式之间的性能相似，其中单任务模型通常对谱中更精细的峰结构具有更好的预测。目前的结果表明，多任务训练是未来开发通用X射线光谱模型的一个有前景的途径，这些模型通过学习到的表示将分子结构映射到多种技术。
英文摘要
Auger-electron spectroscopy has long accompanied x-ray photoelectron spectroscopy as a second modality to resolve chemical states with overlapping core-electron binding energies. However, analyzing the Auger spectrum is challenged by its complexity and the computational expense of its simulation. Here we demonstrate that the physical connection, and thus inter-task relationship, between the generation of a core-hole and its corresponding Auger-Meitner decay enables inductive knowledge transfer through the training of a multi-task graph neural network to predict both observables from a common graph embedding. Both task losses are combined with learned weights via the uncertainty weighting procedure. Overall, the single-task and multi-task models predict calculated and experimental Auger lineshapes with good accuracy in most cases. The performance between the two task regimes is similar, with the single-task models generally having better predictions of the finer peak structures in the spectrum. The present results demonstrate that multi-task training is a promising avenue for future developments of universal x-ray spectroscopy models with learned representations that map the molecular structure to a multitude of techniques.
