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.
ASJC Scopus subject areas
- Computer Science Applications
- Physical and Theoretical Chemistry