A Polarizable QM/MM Explicit Solvent Model for Computational Electrochemistry in Water

Unraveling redox pathways of the disulfide bond in dimethyl disulfide: Ab initio modeling

CONTEXT

In cellular environments, the reduction of disulfide bonds is pivotal for protein folding and synthesis. However, the intricate enzymatic mechanisms governing this process remain poorly understood. This study addresses this gap by investigating a disulfide bridge reduction reaction, serving as a model for comprehending electron and proton transfer in biological systems. Six potential mechanisms for reducing the dimethyl disulfide (DMDS) bridge through electron and proton capture were explored. Thermodynamic and kinetic analyses elucidated the sequence of proton and electron addition. MD-PMM, a method that combines molecular dynamics simulations and quantum-chemical calculations, was employed to compute the redox potential of the mechanism. This research provides valuable insights into the mechanisms and redox potentials involved in disulfide bridge reduction within proteins, offering an understanding of phenomena that are challenging to explore experimentally.

METHODS

All calculations used the Gaussian 09 software package at the MP2/6-311 + g(d,p) theory level. Visualization of the molecular orbitals and electron densities was conducted using Gaussview6. Molecular dynamics simulations were performed using GROMACS with the CHARMM36 force field. The PyMM program (Python Program for QM/MM Simulations Based on the Perturbed Matrix Method) is used to apply the Perturbed Matrix Method to MD simulations.

Incorporating Electrolyte Correlation Effects into Variational Models of Electrochemical Interfaces

We propose a way for obtaining a classical free energy density functional for electrolytes based on a first-principle many-body partition function. Via a one-loop expansion, we include coulombic correlations beyond the conventional mean-field approximation. To examine electrochemical interfaces, we integrate the electrolyte free energy functional into a hybrid quantum-classical model. This scheme self-consistently couples electronic, ionic, and solvent degrees of freedom and incorporates electrolyte correlation effects. The derived free energy functional causes a correlation-induced enhancement in interfacial counterion density and leads to an overall increase in capacitance. This effect is partially compensated by a reduction of the dielectric permittivity of interfacial water. At larger surface charge densities, ion crowding at the interface stifles these correlation effects. While scientifically intriguing already at planar interfaces, we anticipate these correlation effects to play an essential role for electrolytes in nanoconfinement.

Density Functional Theory-Based Protocol to Calculate the Redox Potentials of First-row Transition Metal Complexes for Aqueous Redox Targeting Flow Batteries

Transition metal complexes are a promising class of redox mediators for targeting redox flow batteries due to the tunability of their electrochemical potentials. However, reliable time-efficient tools for the prediction of their reduction potentials are needed. In this work, we establish a suitable density functional theory protocol for their prediction using an initial experimental data set of aqueous iron complexes with bidentate ligands. The approach is then cross-validated using different complexes found in the redox-flow literature. We find that the solvation model affects the prediction accuracy more than the functional or basis set. The smallest errors are obtained using the COSMO-RS solvation model (mean average error (MAE)=0.24 V). With implicit solvation models, a general deviation from experimental results is observed. For a set of similar ligands, they can be corrected using simple linear regression (MAE=0.051 V for the initial set of iron complexes).

Hybrid Functional and Plane Waves based Ab Initio Molecular Dynamics Study of the Aqueous Fe2+ /Fe3+ Redox Reaction

Kohn-Sham density functional theory and plane wave basis set based ab initio molecular dynamics (AIMD) simulation is a powerful tool for studying complex reactions in solutions, such as electron transfer (ET) reactions involving Fe²⁺ /Fe³⁺ ions in water. In most cases, such simulations are performed using density functionals at the level of Generalized Gradient Approximation (GGA). The challenge in modelling ET reactions is the poor quality of GGA functionals in predicting properties of such open-shell systems due to the inevitable self-interaction error (SIE). While hybrid functionals can minimize SIE, standard plane-wave based AIMD at that level of theory is typically 150 times slower than GGA for systems containing ∼100 atoms. Among several approaches reported to speed-up AIMD simulations with hybrid functionals, the noise-stabilized MD (NSMD) procedure, together with the use of localized orbitals to compute the required exchange integrals, is an attractive option. In this work, we demonstrate the application of the NSMD approach for studying the Fe²⁺ /Fe³⁺ redox reaction in water. It is shown here that long AIMD trajectories at the level of hybrid density functionals can be obtained using this approach. Redox properties of the aqueous Fe²⁺ /Fe³⁺ system computed from these simulations are compared with the available experimental data for validation.

Automated Molecular Cluster Growing for Explicit Solvation by Efficient Force Field and Tight Binding Methods

An automated and broadly applicable workflow for the description of solvation effects in an explicit manner is introduced. This method, termed quantum cluster growth (QCG), is based on the semiempirical GFN2-xTB/GFN-FF methods, enabling efficient geometry optimizations and MD simulations. Fast structure generation is provided using the intermolecular force field xTB-IFF. Additionally, the approach uses an efficient implicit solvation model for the electrostatic embedding of the growing clusters. The novel QCG procedure presents a robust cluster generation tool for subsequent application of higher-level (e.g., DFT) methods to study solvation effects on molecular geometries explicitly or to average spectroscopic properties over cluster ensembles. Furthermore, the computation of the solvation free energy with a supermolecular approach can be carried out with QCG. The underlying growing process is physically motivated by computing the leading-order solute-solvent interactions first and can account for conformational and chemical changes due to solvation for low-energy barrier processes. The conformational space is explored with the NCI-MTD algorithm as implemented in the CREST program, using a combination of metadynamics and MD simulations. QCG with GFN2-xTB yields realistic solution geometries and reasonable solvation free energies for various systems without introducing many empirical parameters. Computed IR spectra of some solutes with QCG show a better match to the experimental data compared to well-established implicit solvation models.

DFT-based QM/MM with Particle-Mesh Ewald for Direct, Long-Range Electrostatic Embedding

We present a DFT-based, QM/MM implementation with long-range electrostatic embedding achieved by direct real-space integration of the particle mesh Ewald (PME) computed electrostatic potential. The key transformation is the interpolation of the electrostatic potential from the PME grid to the DFT quadrature grid, from which integrals are easily evaluated utilizing standard DFT machinery. We provide benchmarks of the numerical accuracy with choice of grid size and real-space corrections, and demonstrate that good convergence is achieved while introducing nominal computational overhead. Furthermore, the approach requires only small modification to existing software packages, as is demonstrated with our implementation in the OpenMM and Psi4 software. After presenting convergence benchmarks, we evaluate the importance of long-range electrostatic embedding in three solute/solvent systems modeled with QM/MM. Water and BMIM/BF4 ionic liquid were considered as ``simple' and ``complex' solvents respectively, with water and p-phenylenediamine (PPD) solute molecules treated at QM level of theory. While electrostatic embedding with standard real-space truncation may introduce negligible error for simple systems such as water solute in water solvent, errors become more significant when QM/MM is applied to complex solvents such as ionic liquids. An extreme example is the electrostatic embedding energy for oxidized PPD in BMIM/BF4 for which real-space truncation produces severe error even at 2-3 nm cutoff distances. This latter example illustrates that utilization of QM/MM to compute redox potentials within concentrated electrolytes/ionic media requires carefully chosen long-range electrostatic embedding algorithms, with our presented algorithm providing a general and robust approach.

Ionization Energies and Redox Potentials of Hydrated Transition Metal Ions: Evaluation of Domain-Based Local Pair Natural Orbital Coupled Cluster Approaches

Hydrated transition metal ions are prototypical systems that can be used to model properties of transition metals in complex chemical environments. These seemingly simple systems present challenges for computational chemistry and are thus crucial in evaluations of quantum chemical methods for spin-state and redox energetics. In this work, we explore the applicability of the domain-based pair natural orbital implementation of coupled cluster (DLPNO-CC) theory to the calculation of ionization energies and redox potentials for hydrated ions of all first transition row (3d) metals in the 2+/3+ oxidation states, in connection with various solvation approaches. In terms of model definition, we investigate the construction of a minimally explicitly hydrated quantum cluster with a first and second hydration layer. We report on the convergence with respect to the coupled cluster expansion and the PNO space, as well as on the role of perturbative triple excitations. A recent implementation of the conductor-like polarizable continuum model (CPCM) for the DLPNO-CC approach is employed to determine self-consistent redox potentials at the coupled cluster level. Our results establish conditions for the convergence of DLPNO-CCSD(T) energetics and stress the absolute necessity to explicitly consider the second solvation sphere even when CPCM is used. The achievable accuracy for redox potentials of a practical DLPNO-based approach is, on average, 0.13 V. Furthermore, multilayer approaches that combine a higher-level DLPNO-CCSD(T) description of the first solvation sphere with a lower-level description of the second solvation layer are investigated. The present work establishes optimal and transferable methodological choices for employing DLPNO-based coupled cluster theory, the associated CPCM implementation, and cost-efficient multilayer derivatives of the approach for open-shell transition metal systems in complex environments.

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.

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 medium. Originating in molecular chemistry with finite solutes, implicit solvation techniques are now increasingly used in the context of first-principles modeling of electrochemistry and electrocatalysis at extended (often metallic) electrodes. The prevalent ansatz to model the latter electrodes and the reactive surface chemistry at them through slabs in periodic boundary condition supercells brings its specific challenges. Foremost this concerns the difficulty of describing the entire double layer forming at the electrified solid–liquid interface (SLI) within supercell sizes tractable by commonly employed density functional theory (DFT). We review liquid solvation methodology from this specific application angle, highlighting in particular its use in the widespread ab initio thermodynamics approach to surface catalysis. Notably, implicit solvation can be employed to mimic a polarization of the electrode’s electronic density under the applied potential and the concomitant capacitive charging of the entire double layer beyond the limitations of the employed DFT supercell. Most critical for continuing advances of this effective methodology for the SLI context is the lack of pertinent (experimental or high-level theoretical) reference data needed for parametrization.

Deciphering Cryptic Behavior in Bimetallic Transition-Metal Complexes with Machine Learning

We demonstrate an alternative, data-driven approach to uncovering structure-property relationships for the rational design of heterobimetallic transition-metal complexes that exhibit metal-metal bonding. We tailor graph-based representations of the metal-local environment for these complexes for use in multiple linear regression and kernel ridge regression (KRR) models. We curate a set of 28 experimentally characterized complexes to develop a multiple linear regression model for oxidation potentials. We achieve good accuracy (mean absolute error of 0.25 V) and preserve transferability to unseen experimental data with a new ligand structure. We also train a KRR model on a subset of 330 structurally characterized heterobimetallics to predict the degree of metal-metal bonding. This KRR model predicts relative metal-metal bond lengths in the test set to within 5%, and analysis of key features reveals the fundamental atomic contributions (e.g., the valence electron configuration) that most strongly influence the behavior of these complexes. Our work provides guidance for rational bimetallic design, suggesting that properties, including the formal shortness ratio, should be transferable from one period to another.

Connecting Gas-Phase Computational Chemistry to Condensed Phase Kinetic Modeling: The State-of-the-Art

In recent decades, quantum chemical calculations (QCC) have increased in accuracy, not only providing the ranking of chemical reactivities and energy barriers (e.g., for optimal selectivities) but also delivering more reliable equilibrium and (intrinsic/chemical) rate coefficients. This increased reliability of kinetic parameters is relevant to support the predictive character of kinetic modeling studies that are addressing actual concentration changes during chemical processes, taking into account competitive reactions and mixing heterogeneities. In the present contribution, guidelines are formulated on how to bridge the fields of computational chemistry and chemical kinetics. It is explained how condensed phase systems can be described based on conventional gas phase computational chemistry calculations. Case studies are included on polymerization kinetics, considering free and controlled radical polymerization, ionic polymerization, and polymer degradation. It is also illustrated how QCC can be directly linked to material properties.

Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning

Transition-metal complexes are attractive targets for the design of catalysts and functional materials. The behavior of the metal-organic bond, while very tunable for achieving target properties, is challenging to predict and necessitates searching a wide and complex space to identify needles in haystacks for target applications. This review will focus on the techniques that make high-throughput search of transition-metal chemical space feasible for the discovery of complexes with desirable properties. The review will cover the development, promise, and limitations of "traditional" computational chemistry (i.e., force field, semiempirical, and density functional theory methods) as it pertains to data generation for inorganic molecular discovery. The review will also discuss the opportunities and limitations in leveraging experimental data sources. We will focus on how advances in statistical modeling, artificial intelligence, multiobjective optimization, and automation accelerate discovery of lead compounds and design rules. The overall objective of this review is to showcase how bringing together advances from diverse areas of computational chemistry and computer science have enabled the rapid uncovering of structure-property relationships in transition-metal chemistry. We aim to highlight how unique considerations in motifs of metal-organic bonding (e.g., variable spin and oxidation state, and bonding strength/nature) set them and their discovery apart from more commonly considered organic molecules. We will also highlight how uncertainty and relative data scarcity in transition-metal chemistry motivate specific developments in machine learning representations, model training, and in computational chemistry. Finally, we will conclude with an outlook of areas of opportunity for the accelerated discovery of transition-metal complexes.

Properties of the Pt(111)/electrolyte electrochemical interface studied with a hybrid DFT-solvation approach

Self-consistent modeling of the interface between solid metal electrode and liquid electrolyte is a crucial challenge in computational electrochemistry. In this contribution, we adopt the effective screening medium reference interaction site method (ESM-RISM) to study the charged interface between a Pt(111) surface that is partially covered with chemisorbed oxygen and an aqueous acidic electrolyte. This method proves to be well suited to describe the chemisorption and charging state of the interface at controlled electrode potential. We present an in-depth assessment of the ESM-RISM parameterization and of the importance of computing near-surface water molecules explicitly at the quantum mechanical level. We found that ESM-RISM is able to reproduce some key interface properties, including the peculiar, non-monotonic charging relation of the Pt(111)/electrolyte interface. The comparison with independent theoretical models and explicit simulations of the interface reveals strengths and limitations of ESM-RISM for modeling electrochemical interfaces.

The electrochemical interface in first-principles calculations

First-principles predictions play an important role in understanding chemistry at the electrochemical interface. Electronic structure calculations are straightforward for vacuum interfaces, but do not easily account for the interfacial fields and solvation that fundamentally change the nature of electrochemical reactions. Prevalent techniques for first-principles prediction of electrochemical processes range from expensive explicit solvation using ab initio molecular dynamics, through a hierarchy of continuum solvation techniques, to neglecting solvation and interfacial field effects entirely. Currently, no single approach reliably captures all relevant effects of the electrochemical double layer in first-principles calculations. This review systematically lays out the relation between all major approaches to first-principles electrochemistry, including the key approximations and their consequences for accuracy and computational cost. Focusing on ab initio methods for thermodynamic properties of aqueous interfaces, we first outline general considerations for modeling electrochemical interfaces, including solvent and electrolyte dynamics and electrification. We then present the specifics of various explicit and implicit models of the solvent and electrolyte. Finally, we discuss the compromise between computational efficiency and accuracy, and identify key outstanding challenges and future opportunities in the wide range of techniques for first-principles electrochemistry.

How accurate are approximate quantum chemical methods at modelling solute-solvent interactions in solvated clusters?

In this paper, the performance of a wide range of DFT methods is assessed for the calculation of interaction energies of thermal clusters of a solute in water. Three different charge states (neutral, proton transfer transition state and zwitterion) of glycine were solvated by 1 to 40 water molecules as sampled from molecular dynamics simulations. While some ab initio composite methods that employ insufficiently large basis sets incurred significant errors even for a cluster containing only 5 water molecules relative to the W1X-2 benchmark, the DLPNO-CCSD(T)/CBS and DSD-PBEP86 (triple zeta basis set) levels of theory predicted very accurate interaction energies. These levels of theory were used to benchmark the performance of 16 density functionals from different rungs of Jacob's Ladder. Of the Rung 4 functionals examined, the ωB97M-V and ωB97X-V functionals stood out for predicting absolute interaction energies in 40-water clusters with mean absolute deviations (MAD) ∼4 kJ mol-1. The B3LYP-D3(BJ) functional performed exceptionally well with a MAD ∼1.7 kJ mol-1 and is the overall best performing method. Calculations of relative interaction energies allow for cancellation of systematic errors, including basis set truncation and superposition errors, and the ωB97M-V and B3LYP-D3(BJ) double zeta basis set calculations yielded relative interaction energies that are within ∼3 kJ mol-1 of the benchmark. The ONIOM approximation provides another strategy for accelerating the calculation of accurate absolute interaction energies provided that the calculations have converged with respect to the size of the "high-level-layer".

Exploring Coupled Redox and pH Processes with a Force-Field-Based Approach: Applications to Five Different Systems

Coupled redox and pH-driven processes are at the core of many important biological mechanisms. As the distribution of protonation and redox states in a system is associated with the pH and redox potential of the solution, having efficient computational tools that can simulate under these conditions becomes very important. Such tools have the potential to provide information that complement and drive experiments. In previous publications we have presented the implementation of the constant pH and redox potential molecular dynamics (C(pH,E)MD) method in AMBER and we have shown how multidimensional replica exchange can be used to significantly enhance the convergence efficiency of our simulations. In the current work, after an improvement in our C(pH,E)MD approach that allows a given residue to be simultaneously pH- and redox-active, we have employed our methodologies to study five different systems of interest in the literature. We present results for capped tyrosine dipeptide, two maquette systems containing one pH- and redox-active tyrosine (α₃Y and peptide A), and two proteins that contain multiple heme groups (diheme cytochrome c from Rhodobacter sphaeroides and Desulfovibrio vulgaris Hildenborough cytochrome c₃). We show that our results can provide new insights into previous theoretical and experimental findings by using a fully force-field-based and GPU-accelerated approach, which allows the simulations to be executed with high computational performance.

"Ion Solvation Spectra": Free Energy Analysis of Solvation Structures of Multivalent Cations in Aprotic Solvents

Using advanced molecular dynamics free energy sampling techniques-both classical and ab initio-we analyze the solvation structures of multivalent cations in aprotic solvents. In contrast to previous studies of mono- and bivalent ions in organic solvents, mainly performed using hybrid cluster-continuum quantum chemistry calculations that rely on the assumption of uniqueness of ion solvation free energies, here we find that monatomic bivalent cations may have multiple well-defined minima, as previously reported only for water, or plateaus of free energy with respect to the ion-solvent coordination. These observations are generalized in the concept of the "ion solvation spectrum" to highlight the rich phenomenology related to ion solvation as opposed to the normally expected free energy profiles with a single coordination minimum. Specifically, we show that a single chemical species may exhibit a multiplicity of distinctly different electrochemical properties. Using one- and two-dimensional projections of the free energy landscape, we analyze the stability of ion solvation structures and reveal minimum free energy pathways for ion (de-)solvation with low-dimensional approximations to associated kinetic barriers. Unexpectedly, we show that in some cases the process of opening the first ion solvation shell, by removing a solvent molecule, may actually drive the ion into a free energy basin with a higher coordination number. Our study highlights some deficiencies of conventional methodologies for studying ion solvation as a path to determine redox potentials and provides experimentally testable predictions.

Multistep Explicit Solvation Protocol for Calculation of Redox Potentials

The calculation of molecular redox potentials in aqueous solution presents a challenge to quantum chemistry due to the need to calculate charged, open-shell species experiencing large solvent effects. Traditionally, redox potentials are calculated via the use of density functional theory and continuum solvation methods, but such protocols have been found to often suffer from large errors, particularly in the case of aqueous solution. While explicit solvation models hold promise of higher accuracy to describe solvent effects in general, their complicated use and lack of well-defined, reliable protocols has hindered their adoption. In this study, we present an explicit-solvation-based approach for the calculation of molecular redox potentials. We combine the use of affordable semiempirical QM/MM molecular dynamics (making use of the recently proposed GFN-xTB method by Grimme et al.) for both redox states and use the linear response approximation to relate vertical ionization energies to the adiabatic redox potential. Simulation length, averaging over snapshots, and accounting for bulk and polarization effects are systematically evaluated using phenol as a working example. We find that it is crucial to reliably account for bulk solvation effects in these calculations, as well as polarization effects which we divide up into short-range and long-range contributions. The short-range polarization contribution is accounted for via QM-region expansion, while the long-range contribution is accounted for via Drude-polarizable QM/MM. Our multistep protocol has been coded to be used in a fully automatic way in a local version of Chemshell. It has been evaluated on a test set of oxidation potentials of organic molecules and found to give gas-solution redox shifts with a mean absolute error of 0.13 eV with respect to experiment, compared to mean absolute errors of 0.26 and 0.21 eV with CPCM and SMD continuum models, respectively.

Quantum chemical studies of redox properties and conformational changes of a four-center iron CO2 reduction electrocatalyst

Two calculated structures of the [Fe₄N(CO)12]^3– CO₂ reduction electrocatalyst in its twice reduced state connected by a CO dissociation pathway are studied.

The CO₂ reduction electrocatalyst [Fe₄N(CO)₁₂]^– (abbrev. 1^–) reduces CO₂ to HCO₂^– in a two-electron, one-proton catalytic cycle. Here, we employ ab initio calculations to estimate the first two redox potentials of 1^– and explore the pathway of a side reaction involving CO dissociation from 1^3–. Using the BP86 density functional approximation, the redox potentials were computed with a root mean squared error of 0.15 V with respect to experimental data. High temperature Born–Oppenheimer molecular dynamics was employed to discover a reaction pathway of CO dissociation from 1^3– with a reaction energy of +10.6 kcal mol^–1 and an activation energy of 18.8 kcal mol^–1; including harmonic free energy terms, this yields ΔG_sep = 1.4 kcal mol^–1 for fully separated species and ΔG^‡ = +17.4 kcal mol^–1, indicating CO dissociation is energetically accessible at ambient conditions. The analogous dissociation pathway from 1^2– has a reaction energy of 22.1 kcal mol^–1 and an activation energy of 22.4 kcal mol^–1 (ΔG_sep = 12.8 kcal mol^–1, ΔG^‡ = +18.1 kcal mol^–1). Our computed harmonic vibrational analysis of [Fe₄N(CO)₁₁]^3– or 2^3– reveals a distinct CO-stretching peak red-shifted from the main CO-stretching band, pointing to a possible vibrational signature of dissociation. Multi-reference CASSCF calculations are used to check the assumptions of the density functional approximations that were used to obtain the majority of the results.

Thermodynamic and kinetic hydricities of metal-free hydrides

Thermodynamic and kinetic hydricities provide useful guidelines for the design of hydride donors with desirable properties for catalytic chemical reductions.

Quantum chemical approaches to [NiFe] hydrogenase

The mechanism by which [NiFe] hydrogenase catalyses the oxidation of molecular hydrogen is a significant yet challenging topic in bioinorganic chemistry. With far-reaching applications in renewable energy and carbon mitigation, significant effort has been invested in the study of these complexes. In particular, computational approaches offer a unique perspective on how this enzyme functions at an electronic and atomistic level. In this article, we discuss state-of-the art quantum chemical methods and how they have helped deepen our comprehension of [NiFe] hydrogenase. We outline the key strategies that can be used to compute the (i) geometry, (ii) electronic structure, (iii) thermodynamics and (iv) kinetic properties associated with the enzymatic activity of [NiFe] hydrogenase and other bioinorganic complexes.

KECSA-Movable Type Implicit Solvation Model (KMTISM)

Computation of the solvation free energy for chemical and biological processes has long been of significant interest. The key challenges to effective solvation modeling center on the choice of potential function and configurational sampling. Herein, an energy sampling approach termed the “Movable Type” (MT) method, and a statistical energy function for solvation modeling, “Knowledge-based and Empirical Combined Scoring Algorithm” (KECSA) are developed and utilized to create an implicit solvation model: KECSA-Movable Type Implicit Solvation Model (KMTISM) suitable for the study of chemical and biological systems. KMTISM is an implicit solvation model, but the MT method performs energy sampling at the atom pairwise level. For a specific molecular system, the MT method collects energies from prebuilt databases for the requisite atom pairs at all relevant distance ranges, which by its very construction encodes all possible molecular configurations simultaneously. Unlike traditional statistical energy functions, KECSA converts structural statistical information into categorized atom pairwise interaction energies as a function of the radial distance instead of a mean force energy function. Within the implicit solvent model approximation, aqueous solvation free energies are then obtained from the NVT ensemble partition function generated by the MT method. Validation is performed against several subsets selected from the Minnesota Solvation Database v2012. Results are compared with several solvation free energy calculation methods, including a one-to-one comparison against two commonly used classical implicit solvation models: MM-GBSA and MM-PBSA. Comparison against a quantum mechanics based polarizable continuum model is also discussed (Cramer and Truhlar’s Solvation Model 12).

Accurate Prediction of One-Electron Reduction Potentials in Aqueous Solution by Variable-Temperature H-Atom Addition/Abstraction Methodology

A robust and efficient theoretical approach for calculation of the reduction potentials of charged species in aqueous solution is presented. Within this approach, the reduction potential of a charged complex (with a charge |n| ≥ 2) is probed by means of the reduction potential of its neutralized (protonated/deprotonated) cognate, employing one or several H-atom addition/abstraction thermodynamic cycles. This includes a separation of one-electron reduction from protonation/deprotonation through the temperature dependence. The accuracy of the method has been assessed for the set of 15 transition-metal complexes that are considered as highly challenging systems for computational electrochemistry. Unlike the standard computational protocol(s), the presented approach yields results that are in excellent agreement with experimental electrochemical data. Last but not least, the applicability and limitations of the approach are thoroughly discussed.

Probing the role of electrostatics of polypeptide main-chain in protein folding by perturbing N-terminal residue stereochemistry: DFT study with oligoalanine models

Energetics of folding (ΔH_E→F, in kcal mol⁻¹) from the extended (E) structure to the folded (F) structure for Ia and Ib critically depend on the geometrical relationship between the backbone peptide units of the polypeptide structure.

Redox potentials of aryl derivatives from hybrid functional based first principles molecular dynamics

We report the redox potentials of a set of organic aryl molecules, including quinones, juglone, tyrosine and tryptophan, calculated using a first principles molecular dynamics (FPMD) based method.

Acidity constants and redox potentials of uranyl ions in hydrothermal solutions

We report a first principles molecular dynamics (FPMD) study of the structures, acidity constants (pK_a) and redox potentials (E⁰) of uranyl (UO₂²⁺) from ambient conditions to 573 K.

Ultrafast photo-induced ligand solvolysis of cis-[Ru(bipyridine)2(nicotinamide)2](2+): experimental and theoretical insight into its photoactivation mechanism

Mechanistic insight into the photo-induced solvent substitution reaction of cis-[Ru(bipyridine)2(nicotinamide)2](2+) (1) is presented. Complex 1 is a photoactive species, designed to display high cytotoxicity following irradiation, for potential use in photodynamic therapy (photochemotherapy). In Ru(II) complexes of this type, efficient population of a dissociative triplet metal-centred ((3)MC) state is key to generating high quantum yields of a penta-coordinate intermediate (PCI) species, which in turn may form the target species: a mono-aqua photoproduct [Ru(bipyridine)2(nicotinamide)(H2O)](2+) (2). Following irradiation of 1, a thorough kinetic picture is derived from ultrafast UV/Vis transient absorption spectroscopy measurements, using a 'target analysis' approach, and provides both timescales and quantum yields for the key processes involved. We show that photoactivation of 1 to 2 occurs with a quantum yield ≥0.36, all within a timeframe of ~400 ps. Characterization of the excited states involved, particularly the nature of the PCI and how it undergoes a geometry relaxation to accommodate the water ligand, which is a keystone in the efficiency of the photoactivation of 1, is accomplished through state-of-the-art computation including complete active space self-consistent field methods and time-dependent density functional theory. Importantly, the conclusions here provide a detailed understanding of the initial stages involved in this photoactivation and the foundation required for designing more efficacious photochemotherapy drugs of this type.

Numerical study on the partitioning of the molecular polarizability into fluctuating charge and induced atomic dipole contributions

In order to carry out a detailed analysis of the molecular static polarizability, which is the response of the molecule to a uniform external electric field, the molecular polarizability was computed using the finite-difference method for 21 small molecules, using density functional theory. Within nine charge population schemes (Löwdin, Mulliken, Becke, Hirshfeld, CM5, Hirshfeld-I, NPA, CHELPG, MK-ESP) in common use, the charge fluctuation contribution is found to dominate the molecular polarizability, with its ratio ranging from 59.9% with the Hirshfeld or CM5 scheme to 96.2% with the Mulliken scheme. The Hirshfeld-I scheme is also used to compute the other contribution to the molecular polarizability coming from the induced atomic dipoles, and the atomic polarizabilities in eight small molecules and water pentamer are found to be highly anisotropic for most atoms. Overall, the results suggest that (a) more emphasis probably should be placed on the charge fluctuation terms in future polarizable force field development and (b) an anisotropic polarizability might be more suitable than an isotropic one in polarizable force fields based entirely or partially on the induced atomic dipoles.

Redox potentials and acidity constants from density functional theory based molecular dynamics

CONSPECTUS: All-atom methods treat solute and solvent at the same level of electronic structure theory and statistical mechanics. All-atom computation of acidity constants (pKa) and redox potentials is still a challenge. In this Account, we review such a method combining density functional theory based molecular dynamics (DFTMD) and free energy perturbation (FEP) methods. The key computational tool is a FEP based method for reversible insertion of a proton or electron in a periodic DFTMD model system. The free energy of insertion (work function) is computed by thermodynamic integration of vertical energy gaps obtained from total energy differences. The problem of the loss of a physical reference for ionization energies under periodic boundary conditions is solved by comparing with the proton work function computed for the same supercell. The scheme acts as a computational hydrogen electrode, and the DFTMD redox energies can be directly compared with experimental redox potentials. Consistent with the closed shell nature of acid dissociation, pKa estimates computed using the proton insertion/removal scheme are found to be significantly more accurate than the redox potential calculations. This enables us to separate the DFT error from other sources of uncertainty such as finite system size and sampling errors. Drawing an analogy with charged defects in solids, we trace the error in redox potentials back to underestimation of the energy gap of the extended states of the solvent. Accordingly the improvement in the redox potential as calculated by hybrid functionals is explained as a consequence of the opening up of the bandgap by the Hartree-Fock exchange component in hybrids. Test calculations for a number of small inorganic and organic molecules show that the hybrid functional implementation of our method can reproduce acidity constants with an uncertainty of 1-2 pKa units (0.1 eV). The error for redox potentials is in the order of 0.2 V.

Computational electrochemistry: prediction of liquid-phase reduction potentials

This article reviews recent developments and applications in the area of computational electrochemistry. Our focus is on predicting the reduction potentials of electron transfer and other electrochemical reactions and half-reactions in both aqueous and nonaqueous solutions. Topics covered include various computational protocols that combine quantum mechanical electronic structure methods (such as density functional theory) with implicit-solvent models, explicit-solvent protocols that employ Monte Carlo or molecular dynamics simulations (for example, Car-Parrinello molecular dynamics using the grand canonical ensemble formalism), and the Marcus theory of electronic charge transfer. We also review computational approaches based on empirical relationships between molecular and electronic structure and electron transfer reactivity. The scope of the implicit-solvent protocols is emphasized, and the present status of the theory and future directions are outlined.

What can density functional theory tell us about artificial catalytic water splitting?

Water splitting by artificial catalysts is a critical process in the production of hydrogen gas as an alternative fuel. In this paper, we examine the essential role of theoretical calculations, with particular focus on density functional theory (DFT), in understanding the water-splitting reaction on these catalysts. First, we present an overview of DFT thermochemical calculations on water-splitting catalysts, addressing how these calculations are adapted to condensed phases and room temperature. We show how DFT-derived chemical descriptors of reactivity can be surprisingly good estimators for reactive trends in water-splitting catalysts. Using this concept, we recover trends for bulk catalysts using simple model complexes for at least the first-row transition-metal oxides. Then, using the CoPi cobalt oxide catalyst as a case study, we examine the usefulness of simulation for predicting the kinetics of water splitting. We demonstrate that the appropriate treatment of solvent effects is critical for computing accurate redox potentials with DFT, which, in turn, determine the rate-limiting steps and electrochemical overpotentials. Finally, we examine the ability of DFT to predict mechanism, using ruthenium complexes as a focal point for discussion. Our discussion is intended to provide an overview of the current strengths and weaknesses of the state-of-the-art DFT methodologies for condensed-phase molecular simulation involving transition metals and also to guide future experiments and computations toward the understanding and development of novel water-splitting catalysts.

A double-QM/MM method for investigating donor–acceptor electron-transfer reactions in solution

A double-QM/MM method can explore the distant-dependent phenomena of outer-sphere electron transfer processes. This method allows easy control of donor–acceptor spin-charge densities within the full-reaction scheme.