Constructing Interpretable Machine Learning Models for Predicting Reaction Equilibrium Constant of Carbonyl Sulfide with Organic Amines

The
sustainable utilization of natural gas is crucial for the low-carbon
transformation of the global energy mix. Carbonyl sulfide (COS), commonly
found in natural gas reservoirs, poses significant hazards and is
typically removed via amine-based absorption methods. However, the
development of efficient solvents remains challenging due to the unclear
reaction characteristics between organic amines and COS involved.
This study investigates the reaction equilibrium between organic amines
and COS, focusing on substituent effects on the two-step zwitterion
mechanism. Using density functional theory (DFT), we analyzed the
Gibbs free energy and equilibrium constants for linear aliphatic,
cyclic aliphatic, and aromatic amines. Key descriptors, such as the
average local ionization energy (ALIE) and Hirshfeld charge, are identified
to quantitatively correlate with reaction spontaneity. Cyclic aliphatic
amines exhibit the highest reactivity due to favorable electron distribution,
while aromatic amines show lower reactivity due to conjugation effects.
Furthermore, we developed an interpretable machine learning (ML) model
to predict the first-step equilibrium constant for 535 amines, achieving
high accuracy (R2 = 0.978, Q2 = 0.834). This work integrates first-principles calculations
with interpretable ML to guide the design of efficient COS capture
solvents, advancing sustainable natural gas purification.