Quantitative Prediction of Vertical Ionization Potentials from DFT via a Graph-Network-Based Delta Machine Learning Model Incorporating Electronic Descriptors
Quantitative Prediction of Vertical Ionization Potentials from DFT via a Graph-Network-Based Delta Machine Learning Model Incorporating Electronic Descriptors
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通过基于图网络并结合电子描述符的 Delta 机器学习模型从 DFT 定量预测垂直电离势
DOI：
10.1021/acs.jpca.2c08821
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Raghavachari, Krishnan
中科院分区：
文献类型：
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作者：
Maier, Sarah;Collins, Eric M.;Raghavachari, Krishnan
虽然像CCSD（T）这样精确的波函数理论能够模拟分子化学过程，但相关的陡峭计算缩放使它们难以处理大型系统或广泛的数据库。相比之下，密度泛函理论（DFT）在计算上更加可行，但往往无法定量描述化学过程中的电子变化。在本文中，我们报告了一种有效的增量机器学习（ΔML）模型，该模型建立在基于连接性的层次结构（CBH）方案上-一种基于系统分子碎片化协议的纠错方法-并通过校正DFT中的缺陷来实现垂直电离势的耦合簇精度。本研究整合了分子片段化、系统误差消除和机器学习的概念。首先，我们表明，通过使用电子布居差异图，分子内的电离位点可以很容易地确定，CBH电离过程的校正方案可以自动化。作为我们工作的一个中心特征，我们采用了基于图的QM/ML模型，该模型将描述CBH碎片的原子中心特征嵌入到计算图中，以进一步提高垂直电离势预测的准确性。此外，我们表明，从DFT的电子描述符，即电子布居差异功能的结合，提高模型性能远远超出化学精度（1千卡/摩尔），以接近基准精度。虽然原始DFT结果强烈依赖于所使用的底层泛函，但对于我们的最佳模型，性能是鲁棒的，并且对泛函的依赖性要小得多。
While accurate wave function theories like CCSD(T) are capable of modeling molecular chemical processes, the associated steep computational scaling renders them intractable for treating large systems or extensive databases. In contrast, density functional theory (DFT) is much more computationally feasible yet often fails to quantitatively describe electronic changes in chemical processes. Herein, we report an efficient delta machine learning (ΔML) model that builds on the Connectivity-Based Hierarchy (CBH) scheme─an error correction approach based on systematic molecular fragmentation protocols─and achieves coupled cluster accuracy on vertical ionization potentials by correcting for deficiencies in DFT. The present study integrates concepts from molecular fragmentation, systematic error cancellation, and machine learning. First, we show that by using an electron population difference map, ionization sites within a molecule may be readily identified, and CBH correction schemes for ionization processes may be automated. As a central feature of our work, we employ a graph-based QM/ML model, which embeds atom-centered features describing CBH fragments into a computational graph to further increase accuracy for the prediction of vertical ionization potentials. In addition, we show that the incorporation of electronic descriptors from DFT, namely electron population difference features, improves model performance well beyond chemical accuracy (1 kcal/mol) to approach benchmark accuracy. While the raw DFT results are strongly dependent on the underlying functional used, for our best models, the performance is robust and much less dependent on the functional.