Chen Po-Yen, Shibata Kiyou, Hagita Katsumi, Miyata Tomohiro, Mizoguchi Teruyasu
Department of Materials Engineering, the University of Tokyo, Tokyo, Japan.
Department of Materials Engineering, the University of Tokyo, Tokyo, Japan; Institute of Industrial Science, the University of Tokyo, Tokyo, Japan.
Micron. 2024 Dec;187:103723. doi: 10.1016/j.micron.2024.103723. Epub 2024 Sep 19.
ELNES/XANES spectra can be observed using TEM or synchrotron radiation and can elucidate the unoccupied state electronic structures of an excited states. The computation of their features is usually demanding substantial computational resources due to the requisite structure optimization and electronic structure calculations. Herein, we leverage a machine learning technique alongside an atomic-coordinate-independent descriptor, SMILES, to yield the ELNES/XANES spectra, directly, with heightened precision. Moreover, our approach extends to obtain ground state electronic structure, namely PDOS at both occupied and unoccupied ground states, underscoring its viability for a ground-state spectroscopy. Our study revealed that incorporation of long-SMILES molecules into the training dataset enhances prediction accuracy for such molecular structures. This study's direct derivation of spectroscopy from SMILES strings holds promise for expediting spectroscopic inquiries.
电子能量损失近边结构/ X射线吸收近边结构（ELNES/XANES）光谱可以使用透射电子显微镜（TEM）或同步辐射来观测，并且能够阐明激发态的未占据态电子结构。由于需要进行结构优化和电子结构计算，计算它们的特征通常需要大量的计算资源。在此，我们利用机器学习技术以及与原子坐标无关的描述符SMILES，直接以更高的精度生成ELNES/XANES光谱。此外，我们的方法还可以扩展以获得基态电子结构，即在占据和未占据基态下的态密度（PDOS），这突出了其在基态光谱学中的可行性。我们的研究表明，将长SMILES分子纳入训练数据集可提高对此类分子结构的预测准确性。这项研究从SMILES字符串直接推导光谱学，有望加快光谱学研究。
