AutoSolvate: A toolkit for automating quantum chemistry design and discovery of solvated molecules.

The availability of large, high-quality datasets is crucial for artificial intelligence design and discovery in chemistry. Despite the essential roles of solvents in chemistry, the rapid computational dataset generation of solution-phase molecular properties at the quantum mechanical level of theory was previously hampered by the complicated simulation procedure. Software toolkits that can automate the procedure to set up high-throughput explicit-solvent quantum chemistry (QC) calculations for arbitrary solutes and solvents in an open-source framework are still lacking. We developed AutoSolvate, an open-source toolkit, to streamline the workflow for QC calculation of explicitly solvated molecules. It automates the solvated-structure generation, force field fitting, configuration sampling, and the final extraction of microsolvated cluster structures that QC packages can readily use to predict molecular properties of interest. AutoSolvate is available through both a command line interface and a graphical user interface, making it accessible to the broader scientific community. To improve the quality of the initial structures generated by AutoSolvate, we investigated the dependence of solute-solvent closeness on solute/solvent identities and trained a machine learning model to predict the closeness and guide initial structure generation. Finally, we tested the capability of AutoSolvate for rapid dataset curation by calculating the outer-sphere reorganization energy of a large dataset of 166 redox couples, which demonstrated the promise of the AutoSolvate package for chemical discovery efforts.

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.

Ligand-to-Metal Charge-Transfer Photophysics and Photochemistry of Emissive d0 Titanocenes: A Spectroscopic and Computational Investigation.

Complexes with ligand-to-metal charge-transfer (LMCT) excited states involving d0 metals represent a new design for photocatalysts. Herein, the photochemistry and photophysics of d0 titanocenes of the type Cp2Ti(C2R)2, where C2R = ethynylphenyl (C2Ph), 4-ethynyldimethylaniline (C2DMA), or 4-ethynyltriphenylamine (C2TPA), have been investigated. Cp2Ti(C2Ph)2 and Cp2Ti(C2DMA)2 have also been characterized by single-crystal X-ray diffraction. The two aryl rings in Cp2Ti(C2DMA)2 are nearly face-to-face in the solid state, whereas they are mutually perpendicular for Cp2Ti(C2Ph)2. All three complexes are brightly emissive at 77 K but photodecompose at room temperature when irradiated into their lowest-energy absorption band. The emission wavelengths and photodecomposition quantum yields are as follows: Cp2Ti(C2Ph)2, 575 nm and 0.65; Cp2Ti(C2TPA)2, 642 nm and 0.42; Cp2Ti(C2DMA)2, 672 nm and 0.25. Extensive benchmarking of the density functional theory (DFT) model against the structural data and of the time-dependent DFT (TDDFT) model against the absorption and emission data was performed using combinations of 13 different functionals and 4 basis sets. The model that predicted the absorption and emission data with the greatest fidelity utilized MN15/LANL2DZ for both the DFT optimization and the TDDFT. Computational analysis shows that absorption involves a transition to a 1LMCT state. Whereas the spectroscopic data for Cp2Ti(C2TPA)2 and Cp2Ti(C2DMA)2 are well modeled using the optimized structure of these complexes, Cp2Ti(C2Ph)2 required averaging of the spectra from multiple rotamers involving rotation of the Ph rings. Consistent with this finding, an energy scan of all rotamers showed a very flat energetic surface, with less than 1.3 kcal/mol separating the minimum and maximum. The computational data suggest that emission occurs from a 3LMCT state. Optimization of the 3LMCT state demonstrates compression of the C-Ti-C bond angle, consistent with the known products of photodecomposition.

Quantum chemistry for molecules at extreme pressure on graphical processing units: Implementation of extreme-pressure polarizable continuum model.

Pressure plays essential roles in chemistry by altering structures and controlling chemical reactions. The extreme-pressure polarizable continuum model (XP-PCM) is an emerging method with an efficient quantum mechanical description of small- and medium-sized molecules at high pressure (on the order of GPa). However, its application to large molecular systems was previously hampered by a CPU computation bottleneck: the Pauli repulsion potential unique to XP-PCM requires the evaluation of a large number of electric field integrals, resulting in significant computational overhead compared to the gas-phase or standard-pressure polarizable continuum model calculations. Here, we exploit advances in graphical processing units (GPUs) to accelerate the XP-PCM-integral evaluations. This enables high-pressure quantum chemistry simulation of proteins that used to be computationally intractable. We benchmarked the performance using 18 small proteins in aqueous solutions. Using a single GPU, our method evaluates the XP-PCM free energy of a protein with over 500 atoms and 4000 basis functions within half an hour. The time taken by the XP-PCM-integral evaluation is typically 1% of the time taken for a gas-phase density functional theory (DFT) on the same system. The overall XP-PCM calculations require less computational effort than that for their gas-phase counterpart due to the improved convergence of self-consistent field iterations. Therefore, the description of the high-pressure effects with our GPU-accelerated XP-PCM is feasible for any molecule tractable for gas-phase DFT calculation. We have also validated the accuracy of our method on small molecules whose properties under high pressure are known from experiments or previous theoretical studies.

Insights into the charge-transfer character of electronic transitions in RCp2Ti(C2Fc)2 complexes using solvatochromism, resonance Raman spectroscopy, and TDDFT.

A series of complexes with low-energy FeII to TiIV metal-to-metal charge-transfer (MMCT) transitions, Cp2Ti(C2Fc)2, Cp*2Ti(C2Fc)2, and MeOOCCp2Ti(C2Fc)2, was investigated using solvatochromism and resonance Raman spectroscopy (RRS) augmented with time-dependent density functional theory (TDDFT) calculations in order to interrogate the nature of the CT transitions. Computational models were benchmarked against the experimental UV-Vis spectra and B3LYP/6-31G(d) was found to most faithfully represent the spectra. The energy of the MMCT transition was measured in 15 different solvents and a multivariate fit to the Catalán solvent parameters - solvent polarizability (SP), solvent dipolarity (SdP), solvent basicity (SB), and solvent acidity (SA) - was performed. The effect of SP indicates a greater degree of electron delocalization in the excited state (ES) than the ground state (GS). The small negative solvatochromism with respect to SdP indicates a smaller dipole moment in the ES than the GS. The effect of SB is consistent with charge-transfer to Ti. Upon excitation into the MMCT absorption band, the RRS data show enhancement of the alkyne stretching modes and of the out-of-plane bending modes of the cyclopentadienyl ring connected to Fe and the alkyne bridge. This is consistent with changes in the oxidation states of Ti and Fe, respectively. The higher-energy transitions (350-450 nm) show enhancement of vibrational modes consistent with ethnylcyclopentadienyl to Ti ligand-to-metal charge transfer (LMCT). The RRS data is consistent with the TDDFT predicted character of these transitions. TDDFT suggests that the lowest-energy transition in Cp2Ti(C2Fc)2CuI, where CuI is coordinated between the alkynes, retains its FeII to TiIV MMCT character, in agreement with the RRS data, but that the lowest-energy transitions have significant CuI to Ti character. For Cp2Ti(C2Fc)2CuI, excitation into the low-energy MMCT absorption band results in selective enhancement of the symmetric alkynyl stretching mode.

Water-Mediated Peptide Bond Formation in the Gas Phase: A Model Prebiotic Reaction.

The emergence of life on the prebiotic Earth must have involved the formation of polypeptides, yet the polymerization of amino acids is thermodynamically unfavorable under biologically relevant aqueous conditions because amino acids are zwitterions in solution and because of the production of a water molecule through a condensation reaction. Many mechanisms for overcoming this thermodynamic unfavorability have been proposed, but the role of gas phase water clusters has not been investigated. We present the thermodynamics of the water-mediated gas phase dimerization reaction of glycine as a model for the atmospheric polymerization of amino acids prior to the emergence of biological machinery. We hypothesize that atmospheric aerosols may have played a major role in the prebiotic formation of peptide bonds by providing the thermodynamic driving force to facilitate increasingly stable linear oligopeptides. In addition, we hypothesize that small aerosols orient amino acids on their surfaces, thus providing the correct molecular orientations to funnel the reaction pathways of peptides through transition states that lead eventually to polypeptide products. Using density functional theory and a thorough configurational sampling technique, we show that the thermodynamic spontaneity of the linear dimerization of glycine in the gas phase can be driven by the addition of individual water molecules.

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry.

The computational study of the formation and growth of atmospheric aerosols requires an accurate Gibbs free energy surface, which can be obtained from gas phase electronic structure and vibrational frequency calculations. These quantities are valid for those atmospheric clusters whose geometries correspond to a minimum on their potential energy surfaces. The Gibbs free energy of the minimum energy structure can be used to predict atmospheric concentrations of the cluster under a variety of conditions such as temperature and pressure. We present a computationally inexpensive procedure built on a genetic algorithm-based configurational sampling followed by a series of increasingly accurate screening calculations. The procedure starts by generating and evolving the geometries of a large set of configurations using semi-empirical models then refines the resulting unique structures at a series of high-level ab initio levels of theory. Finally, thermodynamic corrections are computed for the resulting set of minimum-energy structures and used to compute the Gibbs free energies of formation, equilibrium constants, and atmospheric concentrations. We present the application of this procedure to the study of hydrated glycine clusters under ambient conditions.