Partial Charges Optimized by Genetic Algorithms for Deep Eutectic Solvent Simulations

Deep eutectic solvents (DESs) are
a class of solvents often composed
of ammonium-based chloride salts and a neutral hydrogen bond donor
(HBD) at specific ratios. These cost-effective and environmentally
friendly solvents have seen significant growth in multiple fields,
including organic synthesis, and in materials and extractions because
of their desirable properties. In the present work, a new software
called genetic algorithm machine learning (GAML) was developed that
utilizes a genetic algorithm (GA) approach to facilitate the development
of optimized potentials for liquid simulation (OPLS)-based force field
(FF) parameters for eight unique DESs based on three ammonium-based
salts and five HBDs at multiple salt:HBD ratios. As an initial test
of GAML, partial charges were created for 86 conventional solvents
based on neutral organic molecules that yielded excellent overall
mean absolute deviations (MADs) of 0.021 g/cm3, 0.63 kcal/mol,
and 0.20 kcal/mol compared to experimental densities, heats of vaporization
(ΔHvap), and free energies of hydration
(ΔGhyd), respectively. FFs for DESs
constructed from ethylammonium, N,N-diethylethanolammonium,
and N-ethyl-N,N-dimethylethanolammonium chloride salts were then parameterized using
GAML with exceptional agreement achieved at multiple temperatures
for experimental densities, surface tensions, and viscosities with
MADs of 0.024 g/cm3, 4.2 mN/m, and 5.3 cP, respectively.