Assessing Solvation Effects on Chemical Reactions with Globally Optimized Solvent Clusters

On the brink of self-hydration: the water heptadecamer

For pure, neutral, isolated molecular clusters, (H2O)17 marks the transition from structures with all water molecules on the cluster surface to water self-hydration, i.e., cluster structures around one central water molecule. Getting this right with water model potentials turns out to be challenging. Even the best water potentials currently available, which reproduce collective properties very well, still deliver contradicting results for (H2O)17, when different low-energy isomers from global structure optimizations are examined. Interestingly, ab initio quantum chemistry also struggles with the only seemingly simple question if (H2O)17 is all-surface or water-centered. Hence, although the long history of water potential development may be entering its final phase, it is not quite finished yet.

Comparison of Implicit and Explicit Solvent Approaches in Ab Initio Evaluation of Thermochemistry in Solution: Application in Studying Boron Isotope Fractionation in Water

Evaluation of thermochemistry in solution plays a key role in numerous fields. For this task, the solvent effects are commonly included in theoretical computations based on either implicit or explicit solvent approaches. In the present study, we evaluate and compare the performance of some of the most widely applied methods based on these two approaches. For studying the solvent effect on thermochemistry with an explicit solvent, we demonstrate that partial normal mode analysis with frozen geometry of solvent molecules for multiple solute-solvent configurations can yield quite accurate and reliable results for a drastically reduced computational cost. As a case study, we consider the evaluation of the equilibrium constant for the boron isotope exchange between boric acid and borate (k_3-4) in pure and saline water which is of high geochemical importance. Employing three different rigorous and high-precision theoretical approaches, we provide a reliable estimation of k_3-4 which is a value within 1.028 to 1.030 for both pure and saline water which is in excellent agreement with experimental data.

A Statistical Perspective on Microsolvation

The lack of a procedure to determine equilibrium thermodynamic properties of a small system interacting with a bath is frequently seen as a weakness of conventional statistical mechanics. A typical example for such a small system is a solute surrounded by an explicit solvation shell. One way to approach this problem is to enclose the small system of interest in a large bath of explicit solvent molecules, considerably larger than the system itself. The explicit inclusion of the solvent degrees of freedom is obviously limited by the available computational resources. A potential remedy to this problem is a microsolvation approach where only a few explicit solvent molecules are considered and surrounded by an implicit solvent bath. Still, the sampling of the solvent degrees of freedom is challenging with conventional grand canonical Monte Carlo methods, since no single chemical potential for the solvent molecules can be defined in the realm of small-system thermodynamics. In this work, a statistical thermodynamic model based on the grand canonical ensemble is proposed that avoids the conventional system size limitations and accurately characterizes the properties of the system of interest subject to the thermodynamic constraints of the bath. We extend an existing microsolvation approach to a generalized multibath "microstatistical" model and show that the previously derived approaches result as a limit of our model. The framework described here is universal and we validate our method numerically for a Lennard-Jones model fluid.

Bridging the Experiment-Calculation Divide: Machine Learning Corrections to Redox Potential Calculations in Implicit and Explicit Solvent Models

Prediction of redox potentials is essential for catalysis and energy storage. Although density functional theory (DFT) calculations have enabled rapid redox potential predictions for numerous compounds, prominent errors persist compared to experimental measurements. In this work, we develop machine learning (ML) models to reduce the errors of redox potential calculations in both implicit and explicit solvent models. Training and testing of the ML correction models are based on the diverse ROP313 data set with experimental redox potentials measured for organic and organometallic compounds in a variety of solvents. For the implicit solvent approach, our ML models can reduce both the systematic bias and the number of outliers. ML corrected redox potentials also demonstrate less sensitivity to DFT functional choice. For the explicit solvent approach, we significantly reduce the computational costs by embedding the microsolvated cluster in implicit bulk solvent, obtaining converged redox potential results with a smaller solvation shell. This combined implicit-explicit solvent model, together with GPU-accelerated quantum chemistry methods, enabled rapid generation of a large data set of explicit-solvent-calculated redox potentials for 165 organic compounds, allowing detailed investigation of the error sources in explicit solvent redox potential calculations.

Globally Optimized Molecular Embeddings for Dynamic Reaction Solvate Shell Optimization and Active Site Design

We demonstrate how a full QM/MM derivatization of the recently developed GOCAT model can be utilized in the global optimization of molecular embeddings. To this end, we provide two distinct examples: An $$\text {S}_\text {N}2$$ S N 2 reaction, and one enzymatic example of recent interest, the ketosteroid isomerase. These serve us to highlight the advantages of such an approach and sketch the roadmap for further improvements.

Systematic microsolvation approach with a cluster-continuum scheme and conformational sampling

Solvation is a notoriously difficult and nagging problem for the rigorous theoretical description of chemistry in the liquid phase. Successes and failures of various approaches ranging from implicit solvation modeling through dielectric continuum embedding and microsolvated quantum chemical modeling to explicit molecular dynamics highlight this situation. Here, we focus on quantum chemical microsolvation and discuss an explicit conformational sampling ansatz to make this approach systematic. For this purpose, we introduce an algorithm for rolling and automated microsolvation of solutes. Our protocol takes conformational sampling and rearrangements in the solvent shell into account. Its reliability is assessed by monitoring the evolution of the spread and average of the observables of interest.

Globally Optimal Catalytic Fields - Inverse Design of Abstract Embeddings for Maximum Reaction Rate Acceleration

The search for, and understanding of, good catalysts for chemical reactions is a central issue for chemists. Here, we present first steps toward developing a general computational framework to better support this task. This framework combines efficient, unbiased global optimization techniques with an abstract representation of the catalytic environment, to shrink the search space. To analyze the resulting catalytic embeddings, we employ dimensionality reduction and clustering techniques. This not only provides an inverse design approach to new catalytic embeddings but also illuminates the actual interactions behind catalytic effects. All this is illustrated here with a strictly electrostatic model for the environment and with two versions of a selected example reaction. We close with detailed discussions of future improvements of our framework.