Atomic Charges via Gradient Boosting: Development and Application for Solvation Energies in Organic Solvents

ABSTRACT

A gradient‐boosting based atomic‐charge scheme,
BoostCha,
is introduced. The
BoostCha
model operates in three steps: it first predicts pseudo‐charges for individual atoms based on their local environments, represented by three‐dimensional descriptors of Kocer–Mason–Erturk type, then refines these values using global molecular information, and finally restores the charge conservation. The
BoostCha
charges are employed as input features in two independent machine‐learning models for predicting solvation free energies in organic solvents:
ESE‐Boost
, a gradient‐boosting model, and
ESE‐ANN
, a dense artificial neural network. Both approaches yield strong predictive performance, with average root‐mean‐square errors of 0.49 and 0.52 kcal/mol, respectively. The methods demonstrate consistent performance across diverse solvent classes and are particularly accurate for alkanes, alcohols, ethers, esters, ketones, and aromatic and haloaromatic solvents.