Eronen E A, Vladyka A, Sahle Ch J, Niskanen J
Department of Physics and Astronomy, University of Turku, FI-20014 Turun yliopisto, Finland.
ESRF, The European Synchrotron, 71 Avenue des Martyrs, CS40220, 38043 Grenoble Cedex 9, France.
Phys Chem Chem Phys. 2024 Aug 28;26(34):22752-22761. doi: 10.1039/d4cp02454k.
Machine learning can reveal new insights into X-ray spectroscopy of liquids when the local atomistic environment is presented to the model in a suitable way. Many unique structural descriptor families have been developed for this purpose. We benchmark the performance of six different descriptor families using a computational data set of 24 200 sulfur Kβ X-ray emission spectra of aqueous sulfuric acid simulated at six different concentrations. We train a feed-forward neural network to predict the spectra from the corresponding descriptor vectors and find that the local many-body tensor representation, smooth overlap of atomic positions and atom-centered symmetry functions excel in this comparison. We found a similar hierarchy when applying the emulator-based component analysis to identify and separate the spectrally relevant structural characteristics from the irrelevant ones. In this case, the spectra were dominantly dependent on the concentration of the system, whereas adding the second most significant degree of freedom in the decomposition allowed for distinction of the protonation state of the acid molecule.
当以合适的方式将局部原子环境呈现给模型时，机器学习能够揭示液体X射线光谱学的新见解。为此已经开发了许多独特的结构描述符家族。我们使用在六种不同浓度下模拟的24200个硫酸水溶液的硫Kβ X射线发射光谱的计算数据集，对六个不同描述符家族的性能进行了基准测试。我们训练了一个前馈神经网络，从相应的描述符向量预测光谱，发现在这种比较中，局部多体张量表示、原子位置平滑重叠和以原子为中心的对称函数表现出色。当应用基于模拟器的成分分析来识别和分离光谱相关的结构特征与不相关的特征时，我们发现了类似的层次结构。在这种情况下，光谱主要取决于系统的浓度，而在分解中加入第二显著的自由度则可以区分酸分子的质子化状态。
