Δ-Machine Learning toward CCSD Accuracy for Homohalogenated Borane--Phosphine Adducts: Screening Low-Energy Structures from DFT and MP2 Libraries

Abstract

Accurate formation energies for weak boron–phosphorus Lewis adducts can be challenging because MP2 and density-functional treatments may mis-rank low-energy motifs on shallow potential-energy landscapes. Herein, we combine large-scale structure-library sampling with Δ-machine learning (Δ-ML) to approach coupled-cluster accuracy for the homohalogenated adducts F₃B-PF₃,Cl₃B-PCl₃, and Br₃B-PBr₃. DFT (B3LYP-D3) and MP2 libraries comprising several thousand geometries per system are generated and used to train CCSD-referenced Δ-ML models that predict E_CCSD from low-level inputs. The resulting models reproduce CCSD energies with low errors and enable efficient screening of the full libraries, after which a compact low-energy subset is refined with targeted CCSD(T) calculations. The CCSD(T) results show that MP2 substantially overbinds for the chlorinated and brominated adducts, while DFT/B3LYP-D3 can even mispredict the stability sign for Cl₃B-PCl₃. Distance-resolved scans and Morokuma-type EDA rationalize the distinct binding regimes across F/Cl/Br in terms of the balance between Pauli repulsion, polarization/exchange, and dispersion. The proposed workflow enables reliable coupled-cluster-level screening of weak donor–acceptor adducts at greatly reduced cost.