Implicit solvation methods for catalysis at electrified interfaces
Implicit solvation is an effective, highly coarse-grained approach in atomic-scale simulations
to account for a surrounding liquid electrolyte on the level of a continuous polarizable …
to account for a surrounding liquid electrolyte on the level of a continuous polarizable …
Progress towards machine learning reaction rate constants
Quantum and classical reaction rate constant calculations come at the cost of exploring
potential energy surfaces. Due to the “curse of dimensionality”, their evaluation quickly …
potential energy surfaces. Due to the “curse of dimensionality”, their evaluation quickly …
[HTML][HTML] Multi-objective goal-directed optimization of de novo stable organic radicals for aqueous redox flow batteries
Advances in the field of goal-directed molecular optimization offer the promise of finding
feasible candidates for even the most challenging molecular design applications. One …
feasible candidates for even the most challenging molecular design applications. One …
Accurate prediction of aqueous free solvation energies using 3D atomic feature-based graph neural network with transfer learning
Graph neural network (GNN)-based deep learning (DL) models have been widely
implemented to predict the experimental aqueous solvation free energy, while its prediction …
implemented to predict the experimental aqueous solvation free energy, while its prediction …
Multi-scale Computer-aided molecular design of Ionic liquid for absorption heat transformer based on Machine learning
Absorption cycles have attracted considerable attention for utilizing renewable energy and
waste heat to achieve carbon neutrality. Absorption Heat Transformers (AHTs) using Ionic …
waste heat to achieve carbon neutrality. Absorption Heat Transformers (AHTs) using Ionic …
Quantum chemistry-driven machine learning approach for the prediction of the surface tension and speed of sound in ionic liquids
Ionic liquids (ILs) have unique solvent properties and have thus garnered significant interest.
However, exhaustive experimental determination of the physicochemical properties of ILs is …
However, exhaustive experimental determination of the physicochemical properties of ILs is …
Machine learning approach for the prediction of eutectic temperatures for metal-free deep eutectic solvents
AK Lavrinenko, IY Chernyshov… - … Sustainable Chemistry & …, 2023 - ACS Publications
Deep eutectic solvents (DESs) represent an environmentally friendly alternative to
conventional organic solvents. Their liquid range determines the areas of application, and …
conventional organic solvents. Their liquid range determines the areas of application, and …
Solvent interaction and dynamics of neurotransmitters l‐aspartic acid and l‐glutamic acid with water and ethanol
T Pooventhiran, AYA Alzahrani, KJ Rajimon… - Journal of Molecular …, 2023 - Elsevier
In this research article we report the computational solvation studies of two biologically
active neurotransmitters l-aspartic acid and l-glutamic acid. Calculations and optimizations …
active neurotransmitters l-aspartic acid and l-glutamic acid. Calculations and optimizations …
Δ-Machine learning for quantum chemistry prediction of solution-phase molecular properties at the ground and excited states
Due to the limitation of solvent models, quantum chemistry calculation of solution-phase
molecular properties often deviates from experimental measurements. Recently, Δ-machine …
molecular properties often deviates from experimental measurements. Recently, Δ-machine …
Study of interaction between different solvents and neurotransmitters dopamine, l-adrenaline, and l-noradrenaline using LED, QTAIM and AIMD
In this research article, we report the computational solvation studies of three
neurotransmitters dopamine, l-adrenaline, and l-noradrenaline are generally active …
neurotransmitters dopamine, l-adrenaline, and l-noradrenaline are generally active …