Machine Learning for Ionization Potentials and Photoionization Cross Sections of Volatile Organic Compounds
Machine Learning for Ionization Potentials and Photoionization Cross Sections of Volatile Organic Compounds
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DOI：
10.1021/acsearthspacechem.3c00009
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发表时间：
2023-04
影响因子：
3.4
通讯作者：
Matthew P. Stewart;S. Martin
中科院分区：
文献类型：
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作者：
Matthew P. Stewart;S. Martin
分子电离电位（IP）和光电离截面（σ）会影响光电离探测器（pid）和其他气体传感器的灵敏度。本研究采用了几种机器学习（ML）方法，对1251种气态有机物质数据集在10.6 eV （117 nm）下的IP和σ值进行了预测。在各种方法中，对物种电子结构处理的明确性逐渐增加。该研究将IP和σ值的ML预测结果与量子化学计算结果进行了比较。当根据测量结果进行评估时，ML预测的性能与量子计算的性能相当。与测量值相比，预训练进一步降低了平均绝对误差（ε）。基于图形的细心指纹模型的准确率最高，其εIP = 0.23±0.01 eV, εσ = 2.8±0.2 Mb。IP的ML预测与测量的IP （R2 = 0.88）和在M06-2X/aug-cc-pVTZ水平上计算的IP （R2 = 0.82）都有很好的相关性。σ的ML预测值与计算截面的相关性相当好（R2 = 0.66）。所开发的IP和σ值的ML方法，代表了与工业应用和大气化学相关的一组可推广的挥发性有机化合物（VOCs）的性质，可用于定量描述使用电离辐射作为传感机制一部分的化学传感器（如光电离探测器）的物种依赖灵敏度。
Molecular ionization potentials (IP) and photoionization cross sections (σ) can affect the sensitivity of photoionization detectors (PIDs) and other sensors for gaseous species. This study employs several methods of machine learning (ML) to predict IP and σ values at 10.6 eV (117 nm) for a dataset of 1251 gaseous organic species. The explicitness of the treatment of the species electronic structure progressively increases among the methods. The study compares the ML predictions of the IP and σ values to those obtained by quantum chemical calculations. The ML predictions are comparable in performance to those of the quantum calculations when evaluated against measurements. Pretraining further reduces the mean absolute errors (ε) compared to the measurements. The graph-based attentive fingerprint model was most accurate, for which εIP = 0.23 ± 0.01 eV and εσ = 2.8 ± 0.2 Mb compared to measurements and computed cross sections, respectively. The ML predictions for IP correlate well with both the measured IPs (R2 = 0.88) and with IPs computed at the level of M06-2X/aug-cc-pVTZ (R2 = 0.82). The ML predictions for σ correlated reasonably well with computed cross sections (R2 = 0.66). The developed ML methods for IP and σ values, representing the properties of a generalizable set of volatile organic compounds (VOCs) relevant to industrial applications and atmospheric chemistry, can be used to quantitatively describe the species-dependent sensitivity of chemical sensors that use ionizing radiation as part of the sensing mechanism, such as photoionization detectors.