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Accurate wavefunction methods like CCSD(T) can predict chemical and thermodynamic properties of small molecules with near-experimental precision. However, their steep computational cost limits their use for large systems or extensive datasets. In contrast, DFT is faster and more practical for large molecules but often fails to accurately capture electronic changes, especially upon geometry relaxation. To address this, we have developed a graph neural network (GNN)-based Delta-Machine Learning (ΔML) model using Connectivity-Based Hierarchy (CBH-2) molecular fragments as descriptors to predict adiabatic ionization potentials (IPs). Our approach shows systematic error cancellation, guided by electron population difference maps to identify ionization sites within the molecule to construct CBH-2 fragments. We have considered both structural and electronic features to improve our ΔML model. We have observed that upon incorporation of electronic features, the graph-based ΔML model achieved a coupled cluster level of accuracy with an MAE of 0.02 eV for IP prediction. Furthermore, we compared adiabatic and vertical IP predictions across different methods. While raw DFT errors are strongly dependent on the functional used, our ΔML model is robust and much less functional-sensitive.

Combining molecular fragmentation and machine learning for accurate prediction of adiabatic ionization potentials