CBH-BDC Enhanced Δ‑ML for Predicting the Accurate Standard Enthalpy of Formation

The
standard enthalpy of formation (ΔfH°) is a fundamental thermodynamic property that is
essential for understanding various physicochemical processes. Our
group recently developed the connectivity-based hierarchy with the
bond difference correction (CBH–BDC) method for calculating
the accurate ΔfH°. However,
it encounters challenges in high-accuracy electron energy calculations
and is restricted by BDC parameters that are limited to specific elements.
In this work, we introduce a CBH–BDC enhanced delta machine
learning (Δ-ML) approach that utilizes effective and interpretable
molecular descriptors derived from connection-based hierarchy fragments
and BDC, enabling the accurate prediction of ΔfH° while bypassing high-level quantum calculations.
The approach is validated using 464 species with experimental ΔfH° and applied to extrapolate ΔfH° from density functional theory (DFT)
accuracy to CCSD(T) accuracy for 120,416 stable organic molecules
in the QM9 database. It demonstrates significant improvements in accuracy,
enabling the construction of a high-quality ΔfH° database for chemical deep learning.