Publications

The reactivity of water, a fundamental process in aqueous chemistry, is profoundly altered under nanoconfinement. The properties of the confining material determine the layer dependence of autoionization, dictating whether reactions are stabilized at the interface or in the subsurface. In weakly interacting walls (Wall A), hydroxide is destabilized at the interface and the reaction proceeds preferentially in the subsurface, whereas in strongly interacting walls (Wall B) the interfacial and subsurface states are nearly isoenergetic, reducing selectivity. This contrast arises from confinement-enforced coordination motifs where hydronium remains tri- coordinated across environments, while hydroxide is restricted to tetra coordination at the interface but adopts hypercoordinated states in the subsurface. Mechanical flexibility of the confining framework further modulates the overall thermodynamics by reducing the entropic penalty, as water molecules can explore a broader configurational space compared to rigid pores. These findings establish how layer-specific solvation and wall flexibility govern confined-water reactivity, providing molecular-level design principles for engineering dynamic nanoscale interfaces in catalysis, energy storage, and molecular separations.

Rising energy demands underscore the need for renewable energy solutions such as solar energy. Covalent organic frameworks (COFs), with their tunable compositions, structures, and photophysical properties, are promising candidates; however, a comprehensive understanding of their composition-structure–property relationships remains limited. Here, combining all-electron quantum chemistry with coarse-grained Holstein Hamiltonians, we show that although slipped-stacked configurations are generally most stable, the degree of slipping is strongly influenced by the nature of the functional groups and does not follow simple electron- donating or -withdrawing trends. While van der Waals interactions primarily drive the stacking behavior, electrostatic contributions unique to each substituent modulate its extent. Furthermore, we find that in highly symmetric lattice backbones, small substituent changes have minimal effect on electronic structure, whereas symmetry-breaking functionalization offers a robust and effective route to tune electronic, transport, and photophysical properties. While the stacking arrangement primarily governs interlayer electron coherence, its influence diminishes in the high-disorder regime. Our findings provide fundamental insights and design principles to guide the development of high-performance COFs for photocatalytic applications.

Water confined within nanometer-scale environments plays a central role in functional materials for nanofluidic and membrane-based applications, where acid–base equilibria and proton transport govern essential processes such as ion conduction, energy conversion, and chemical separations. Similar mechanisms are also fundamental to biological systems, including enzyme catalysis and cellular signaling. At sub-nanometer scales, confinement and interfacial interactions dramatically reshape the molecular landscape, challenging conventional assumptions about pH and chemical reactivity. Here, we combine density-corrected density functional theory with machine-learned interatomic potentials to investigate the autoionization of water confined to quasi-two-dimensional monolayers within sub-nanometer slit pores. We find that extreme confinement markedly suppresses water autoionization, raising the effective pKw by more than two units. This suppression originates from hydroxide ion destabilization at interfaces, driven by restricted hydrogen bonding, hindered molecular reorientation, and a breakdown of Grotthuss proton transport caused by topological frustration in the hydrogen-bond network. These findings offer a molecular-level understanding of how confinement modulates fundamental aqueous chemistry and establish guiding principles for tuning aqueous phase reactivity in nanoscale environments.

Water’s ability to autoionize into hydronium (H3O+) and hydroxide (OH–) ions dictates the acidity or basicity of aqueous solutions, influencing the reaction pathways of many chemical and biochemical processes. In this study, we determine the molecular mechanism of the autoionization process by leveraging both the computational efficiency of a deep neural network potential trained on highly accurate data calculated within density-corrected density functional theory and the ability of enhanced sampling techniques to ensure a comprehensive exploration of the underlying multidimensional free-energy landscape. By properly accounting for nuclear quantum effects, our simulations provide an accurate estimate of the autoionization constant of liquid water (pKw = 13.71 ± 0.16), offering a realistic molecular-level picture of the autoionization process and emphasizing its quantum-mechanical nature. Importantly, our simulations highlight the central role played by the Grotthuss mechanism in stabilizing solvent-separated ion pair configurations, revealing its profound impact on acid–base equilibria in aqueous environments.

In constructing finite models of enzyme active sites for quantum-chemical calculations, atoms at the periphery of the model must be constrained to prevent unphysical rearrangements during geometry relaxation. A simple fixed-atom or “coordinate-lock” approach is commonly employed but leads to undesirable artifacts in the form of small imaginary frequencies. These preclude evaluation of finite- temperature free-energy corrections, limiting thermochemical calculations to enthalpies only. Full-dimensional vibrational frequency calculations are possible by replacing the fixed-atom constraints with harmonic confining potentials. Here, we compare that approach to an alternative strategy in which fixed-atom contributions to the Hessian are simply omitted. While the latter strategy does eliminate imaginary frequencies, it tends to underestimate both the zero-point energy and the vibrational entropy while introducing artificial rigidity. Harmonic confining potentials eliminate imaginary frequencies and provide a flexible means to construct active-site models that can be used in unconstrained geometry relaxations, affording better convergence of reaction energies and barrier heights with respect to the model size, as compared to models with fixed-atom constraints.

The microviscosity of intracellular environments plays an important role in monitoring cellular function. Thus, the capability of detecting changes in viscosity can be utilized for the detection of different disease states. Viscosity-sensitive fluorescent molecular rotors are potentially excellent probes for these applications; however, the predictable relationships between chemical structural features and viscosity sensitivity are poorly understood. Here, we investigate a set of arylcyanoamide-based fluorescent probes and the effect of small aliphatic substituents on their viscosity sensitivity. We found that the location of the substituents and the type of π- network of the fluorophore can significantly affect the viscosity sensitivity of these fluorophores. Computational analysis supported the notion that the excited state rotational energy barrier plays a dominant role in the relative viscosity sensitivity of these fluorophores. These findings provide valuable insight into the design of molecular rotor-based fluorophores for viscosity measurement.

We assess the performance of different dispersion models for several popular density functionals across a diverse set of noncovalent systems, ranging from the benzene dimer to molecular crystals. By analyzing the interaction energies and their individual components, we demonstrate that there exists variability across different systems for empirical dispersion models, which are calibrated for reproducing the interaction energies of specific systems. Thus, parameter fitting may undermine the underlying physics, as dispersion models rely on error compensation among the different components of the interaction energy. Energy decomposition analyses reveal that, the accuracy of revPBE-D3 for some aqueous systems originates from significant compensation between dispersion and charge transfer energies. However, revPBE-D3 is less accurate in describing systems where error compensation is incomplete, such as the benzene dimer. Such cases highlight the propensity for unpredictable behavior in various dispersion-corrected density functionals across a wide range of molecular systems, akin to the behavior of force fields. On the other hand, we find that SCAN-rVV10, a targeted-dispersion approach, affords significant reductions in errors associated with the lattice energies of molecular crystals, while it has limited accuracy in reproducing structural properties. Given the ubiquitous nature of noncovalent interactions and the key role of density functional theory in computational sciences, the future development of dispersion models should prioritize the faithful description of the dispersion energy, a shift that promises greater accuracy in capturing the underlying physics across diverse molecular and extended systems.

Density functional theory (DFT) has been applied to modeling molecular interactions in water for over three decades. The ubiquity of water in chemical and biological processes demands a unified understanding of its physics, from the single molecule to the thermodynamic limit and everything in between. Recent advances in the development of data-driven and machine-learning potentials have accelerated simulation of water and aqueous systems with DFT accuracy. However, anomalous properties of water in the condensed phase, where a rigorous treatment of both local and non-local many-body (MB) interactions is in order, are often unsatisfactory or partially missing in DFT models of water. In this review, we discuss the modeling of water and aqueous systems based on DFT and provide a comprehensive description of a general theoretical/computational framework for the development of data-driven many-body potentials from DFT reference data. This framework, coined MB-DFT, readily enables efficient many-body molecular dynamics (MD) simulations of small molecules, in both gas and condensed phases, while preserving the accuracy of the underlying DFT model. Theoretical considerations are emphasized, including the role that the delocalization error plays in MB-DFT potentials of water and the possibility to elevate DFT and MB-DFT to near-chemical-accuracy through a density-corrected formalism. The development of the MB-DFT framework is described in detail, along with its application in MB-MD simulations and recent extension to the modeling of reactive processes in solution within a quantum mechanics/MB molecular mechanics (QM/MB-MM) scheme, using water as a prototypical solvent. Finally, we identify open challenges and discuss future directions for MB-DFT and theory. Theories around delocalization error and strategies to elevate DFT... QM/MB-MM simulations in condensed phases.

Density functional theory (DFT) is the most widely used electronic structure method, due to its simplicity and cost effectiveness. The accuracy of a DFT calculation depends not only on the choice of the density functional approximation (DFA) adopted but also on the electron density produced by the DFA. SCAN is a modern functional that satisfies all known constraints for meta-GGA functionals. The density-driven errors, defined as energy errors arising from errors of the self-consistent DFA electron density, can hinder SCAN from achieving chemical accuracy in some systems, including water. Density-corrected DFT (DC-DFT) can alleviate this shortcoming by adopting a more accurate electron density which, in most applications, is the electron density obtained at the Hartree–Fock level of theory due to its relatively low computational cost. In this work, we present extensive calculations aimed at determining the accuracy of the DC-SCAN functional for various aqueous systems. DC-SCAN (SCAN@HF) shows remarkable consistency in reproducing reference data obtained at the coupled cluster level of theory, with minimal loss of accuracy. Density-driven errors in the description of ionic aqueous clusters are thoroughly investigated. By comparison with the orbital-optimized CCD density in the water dimer, we find that the self-consistent SCAN density transfers a spurious fraction of an electron across the hydrogen bond to the hydrogen atom (H*, covalently bound to the donor oxygen atom) from the acceptor (OA) and donor (OD) oxygen atoms, while HF makes a much smaller spurious transfer in the opposite direction, consistent with DC-SCAN (SCAN@HF) reduction of SCAN overbinding due to delocalization error. While LDA seems to be the conventional extreme of density delocalization error, and HF the conventional extreme of (usually much smaller) density localization error, these two densities do not quite yield the conventional range of density-driven error in energy differences. Finally, comparisons of the DC-SCAN results with those obtained with the Fermi-Löwdin orbital self-interaction correction (FLOSIC) method show that DC-SCAN represents a more accurate approach to reducing density-driven errors in SCAN calculations of ionic aqueous clusters. While the HF density is superior to that of SCAN for noncompact water clusters, the opposite is true for the compact water molecule with exactly 10 electrons.

The delicate interplay between functional-driven and density-driven errors in density functional theory (DFT) has hindered traditional density functional approximations (DFAs) from providing an accurate description of water for over 30 years. Recently, the deep- learned DeepMind 21 (DM21) functional has been shown to overcome the limitations of traditional DFAs as it is free of delocalization error. To determine if DM21 can enable a molecular-level description of the physical properties of aqueous systems within Kohn– Sham DFT, we assess the accuracy of the DM21 functional for neutral, protonated, and deprotonated water clusters. We find that the ability of DM21 to accurately predict the energetics of aqueous clusters varies significantly with cluster size. Additionally, we introduce the many-body MB-DM21 potential derived from DM21 data within the many-body expansion of the energy and use it in simulations of liquid water as a function of temperature at ambient pressure. We find that size-dependent functional-driven errors identified in the analysis of the energetics of small clusters calculated with the DM21 functional result in the MB-DM21 potential systematically overestimating the hydrogen-bond strength and, consequently, predicting a more ice-like local structure of water at room temperature.

We investigate the interplay between functional-driven and density-driven errors in different density functional approximations within density functional theory (DFT) and the implications of these errors for simulations of water with DFT-based data-driven potentials. Specifically, we quantify density-driven errors in two widely used dispersion-corrected functionals derived within the generalized gradient approximation (GGA), namely BLYP-D3 and revPBE-D3, and two modern meta-GGA functionals, namely strongly constrained and appropriately normed (SCAN) and B97M-rV. The effects of functional-driven and density-driven errors on the interaction energies are first assessed for the water clusters of the BEGDB dataset. Further insights into the nature of functional-driven errors are gained from applying the absolutely localized molecular orbital energy decomposition analysis (ALMO-EDA) to the interaction energies, which demonstrates that functional-driven errors are strongly correlated with the nature of the interactions. We discuss cases where density- corrected DFT (DC-DFT) models display higher accuracy than the original DFT models and cases where reducing the density-driven errors leads to larger deviations from the reference energies due to the presence of large functional-driven errors. Finally, molecular dynamics simulations are performed with data-driven many-body potentials derived from DFT and DC-DFT data to determine the effect that minimizing density-driven errors has on the description of liquid water. Besides rationalizing the performance of widely used DFT models of water, we believe that our findings unveil fundamental relations between the shortcomings of some common DFT approximations and the requirements for accurate descriptions of molecular interactions, which will aid the development of a consistent, DFT-based framework for the development of data-driven and machine-learned potentials for simulations of condensed- phase systems.

Density functional theory (DFT) has been extensively used to model the properties of water. Albeit maintaining a good balance between accuracy and efficiency, no density functional has so far achieved the degree of accuracy necessary to correctly predict the properties of water across the entire phase diagram. Here, we present density-corrected SCAN (DC-SCAN) calculations for water which, minimizing density-driven errors, elevate the accuracy of the SCAN functional to that of “gold standard” coupled-cluster theory. Building upon the accuracy of DC-SCAN within a many-body formalism, we introduce a data-driven many-body potential energy function, MB-SCAN(DC), that quantitatively reproduces coupled cluster reference values for interaction, binding, and individual many- body energies of water clusters. Importantly, molecular dynamics simulations carried out with MB-SCAN(DC) also reproduce the properties of liquid water, which thus demonstrates that MB-SCAN(DC) is effectively the first DFT-based model that correctly describes water from the gas to the liquid phase.

We present a general framework for the development of data-driven many-body (MB) potential energy functions (MB-QM PEFs) that represent the interactions between small molecules at an arbitrary quantum-mechanical (QM) level of theory. As a demonstration, a family of MB-QM PEFs for water is rigorously derived from density functionals belonging to different rungs across Jacob’s ladder of approximations within density functional theory (MB-DFT) and from Møller–Plesset perturbation theory (MB-MP2). Through a systematic analysis of individual MB contributions to the interaction energies of water clusters, we demonstrate that all MB-QM PEFs preserve the same accuracy as the corresponding ab initio calculations, with the exception of those derived from density functionals within the generalized gradient approximation (GGA). The differences between the DFT and MB-DFT results are traced back to density- driven errors that prevent GGA functionals from accurately representing the underlying molecular interactions for different cluster sizes and hydrogen-bonding arrangements. We show that this shortcoming may be overcome, within the MB formalism, by using density-corrected functionals (DC-DFT) that provide a more consistent representation of each individual MB contribution. This is demonstrated through the development of a MB-DFT PEF derived from DC-PBE-D3 data, which more accurately reproduce the corresponding ab initio results.

This article summarizes technical advances contained in the fifth major release of the Q-Chem quantum chemistry program package, covering developments since 2015. A comprehensive library of exchange–correlation functionals, along with a suite of correlated many-body methods, continues to be a hallmark of the Q-Chem software. The many-body methods include novel variants of both coupled-cluster and configuration-interaction approaches along with methods based on the algebraic diagrammatic construction and variational reduced density-matrix methods. Methods highlighted in Q-Chem 5 include a suite of tools for modeling core-level spectroscopy, methods for describing metastable resonances, methods for computing vibronic spectra, the nuclear–electronic orbital method, and several different energy decomposition analysis techniques. High-performance capabilities including multithreaded parallelism and support for calculations on graphics processing units are described. Q-Chem boasts a community of well over 100 active academic developers, and the continuing evolution of the software is supported by an “open teamware” model and an increasingly modular design.

Femtosecond stimulated Raman spectroscopy (FSRS) is an ultrafast pump–probe technique designed to elucidate excited-state molecular dynamics by means of vibrational spectroscopy. We present a first-principles protocol for the simulation of FSRS that integrates ab initio molecular dynamics with computational resonance Raman spectroscopy. Theoretical calculations can monitor the time-dependent evolution of specific vibrational modes and thus provide insight into the nature of the motion responsible for the experimental FSRS signal, and we apply this technique to study quaterthiophene derivatives. The S1 state of two different quaterthiophene derivatives relaxes via in-phase and out-of-phase stretching modes whose frequencies are coupled to the dihedral backbone angle, such that the spectral evolution reflects the excited-state relaxation toward a planar conformation. The simulated spectra aid in confirming the experimental assignment of the vibrational modes that are probed in the existing FSRS experiments on quaterthiophenes.

Quantum-chemical studies of enzymatic reaction mechanisms sometimes use truncated active-site models as simplified alternatives to mixed quantum mechanics molecular mechanics (QM/MM) procedures. Eliminating the MM degrees of freedom reduces the complexity of the sampling problem, but the trade-off is the need to introduce geometric constraints in order to prevent structural collapse of the model system during geometry optimizations that do not contain a full protein backbone. These constraints may impair the efficiency of the optimization, and care must be taken to avoid artifacts such as imaginary vibrational frequencies. We introduce a simple alternative in which terminal atoms of the model system are placed in soft harmonic confining potentials rather than being rigidly constrained. This modification is simple to implement and straightforward to use in vibrational frequency calculations, unlike iterative constraint-satisfaction algorithms, and allows the optimization to proceed without constraint even though the practical result is to fix the anchor atoms in space. The new approach is more efficient for optimizing minima and transition states, as compared to the use of fixed-atom constraints, and also more robust against unwanted imaginary frequencies. We illustrate the method by application to several enzymatic reaction pathways where entropy makes a significant contribution to the relevant reaction barriers. The use of confining potentials correctly describes reaction paths and facilitates calculation of both vibrational zero-point and finite-temperature entropic corrections to barrier heights.

According to the conventional picture, the aqueous or “hydrated” electron, e–(aq), occupies an excluded volume (cavity) in the structure of liquid water. However, simulations with certain one-electron models predict a more delocalized spin density for the unpaired electron, with no distinct cavity structure. It has been suggested that only the latter (non-cavity) structure can explain the hydrated electron’s resonance Raman spectrum, although this suggestion is based on calculations using empirical frequency maps developed for neat liquid water, not for e–(aq). All-electron ab initio calculations presented here demonstrate that both cavity and non- cavity models of e–(aq) afford significant red-shifts in the O–H stretching region. This effect is nonspecific and arises due to electron penetration into frontier orbitals of the water molecules. Only the conventional cavity model, however, reproduces the splitting of the H–O–D bend (in isotopically mixed water) that is observed experimentally and arises due to the asymmetric environments of the hydroxyl moieties in the electron’s first solvation shell. We conclude that the cavity model of e–(aq) is more consistent with the measured resonance Raman spectrum than is the delocalized, non-cavity model, despite previous suggestions to the contrary. Furthermore, calculations with hybrid density functionals and with Hartree–Fock theory predict that non-cavity liquid geometries afford only unbound (continuum) states for an extra electron, whereas in reality this energy level should lie more than 3 eV below vacuum level. As such, the non-cavity model of e–(aq) appears to be inconsistent with available vibrational spectroscopy, photoelectron spectroscopy, and quantum chemistry.

Density-functional approximations developed in the past decade necessitate the use of quadrature grids that are far more dense than those required to integrate older generations of functionals. This category of difficult-to-integrate functionals includes meta- generalized gradient approximations, which depend on orbital gradients and/or the Laplacian of the density, as well as functionals based on B97 and the popular “Minnesota” class of functionals, each of which contain complicated and/or oscillatory expressions for the exchange inhomogeneity factor. Following a strategy introduced previously by Gill and co-workers to develop the relatively sparse “SG-0” and “SG-1” standard quadrature grids, we introduce two higher-quality grids that we designate SG-2 and SG-3, obtained by systematically “pruning” medium- and high-quality atom-centered grids. The pruning procedure affords computational speedups approaching a factor of two for hybrid functionals applied to systems of ~100 atoms, without significant loss of accuracy. The grid dependence of several popular density functionals is characterized for various properties.