Zheng Chen, Chen Chi, Chen Yiming, Ong Shyue Ping
Materials Virtual Lab, Department of NanoEngineering, University of California San Diego, 9500 Gilman Drive, Mail Code 0448, La Jolla, CA 92093-0448, USA.
Patterns (N Y). 2020 Apr 21;1(2):100013. doi: 10.1016/j.patter.2020.100013. eCollection 2020 May 8.
Analyzing coordination environments using X-ray absorption spectroscopy has broad applications in solid-state physics and material chemistry. Here, we show that random forest models trained on 190,000 K-edge X-ray absorption near-edge structure (XANES) spectra can identify the main atomic coordination environment with a high accuracy of 85.4% and all associated coordination environments with a high Jaccard score of 81.8% for 33 cation elements in oxides, significantly outperforming other machine-learning models. In a departure from prior works, the coordination environment is described as a distribution over 25 distinct coordination motifs with coordination numbers ranging from 1 to 12. More importantly, we show that the random forest models can be used to predict coordination environments from experimental K-edge XANES with minimal loss in accuracy. A drop-variable feature importance analysis highlights the key roles that the pre-edge and main-peak regions play in coordination environment identification.
利用X射线吸收光谱分析配位环境在固态物理学和材料化学中有着广泛的应用。在此，我们表明，基于190,000个K边X射线吸收近边结构（XANES）光谱训练的随机森林模型，能够以85.4%的高精度识别主要原子配位环境，并以81.8%的高杰卡德分数识别氧化物中33种阳离子元素的所有相关配位环境，显著优于其他机器学习模型。与先前的研究不同，配位环境被描述为25种不同配位模式的分布，配位数范围为1至12。更重要的是，我们表明随机森林模型可用于从实验K边XANES预测配位环境，且准确性损失最小。降变量特征重要性分析突出了前边缘和主峰区域在配位环境识别中所起的关键作用。
