Nguyen Tuan H, Le Khang M, Nguyen Lam H, Truong Thanh N
Faculty of Chemical Engineering, Ho Chi Minh City University of Technology, 268 Ly Thuong Kiet Street, District 10, Ho Chi Minh City 7000000, Vietnam.
Faculty of Chemistry, VNUHCM-University of Science, 227 Nguyen Van Cu Street, Ho Chi Minh City 700000, Vietnam.
ACS Omega. 2023 Oct 2;8(41):38441-38451. doi: 10.1021/acsomega.3c05212. eCollection 2023 Oct 17.
This study presents the development of machine-learning-based quantitative structure-property relationship (QSPR) models for predicting electron affinity, ionization potential, and band gap of fusenes from different chemical classes. Three variants of the atom-based Weisfeiler-Lehman (WL) graph kernel method and the machine learning model Gaussian process regressor (GPR) were used. The data pool comprises polycyclic aromatic hydrocarbons (PAHs), thienoacenes, cyano-substituted PAHs, and nitro-substituted PAHs computed with density functional theory (DFT) at the B3LYP-D3/6-31+G(d) level of theory. The results demonstrate that the GPR/WL kernel methods can accurately predict the electronic properties of PAHs and their derivatives with root-mean-square deviations of 0.15 eV. Additionally, we also demonstrate the effectiveness of the active learning protocol for the GPR/WL kernel methods pipeline, particularly for data sets with greater diversity. The interpretation of the model for contributions of individual atoms to the predicted electronic properties provides reasons for the success of our previous degree of π-orbital overlap model.
本研究提出了基于机器学习的定量结构-性质关系（QSPR）模型，用于预测不同化学类别的并苯的电子亲和能、电离势和带隙。使用了基于原子的魏斯费勒-莱曼（WL）图核方法的三种变体以及机器学习模型高斯过程回归器（GPR）。数据集包括在B3LYP-D3/6-31+G(d)理论水平下用密度泛函理论（DFT）计算的多环芳烃（PAHs）、噻吩并并苯、氰基取代的PAHs和硝基取代的PAHs。结果表明，GPR/WL核方法能够以0.15 eV的均方根偏差准确预测PAHs及其衍生物的电子性质。此外，我们还证明了主动学习协议对GPR/WL核方法管道的有效性，特别是对于具有更大多样性的数据集。对单个原子对预测电子性质贡献的模型解释为我们之前的π轨道重叠程度模型的成功提供了原因。
