Adsorbate free energies are important parameters in surface chemistry and catalysis. Because of its simplicity, the harmonic oscillator (HO) model remains the most widely used method for calculating adsorbate free energy in many fields, including microkinetic modeling. However, it is well-known that the HO method is ineffective for weak adsorption. In this study, we propose a translational model with

This work introduces a dedicated thermostatization strategy for molecular dynamics simulations of gaseous systems. The proposed thermostat is based on the stochastic canonical velocity rescaling approach by Bussi and co-workers and is capable of ensuring an equal distribution of the kinetic energy among the translational, rotational, and vibrational degrees of freedom. The outlined framework ensures

Polyphosphate nucleotides, such as ATP, ADP, GTP, and GDP, play a crucial role in modulating protein functions through binding and/or catalytically activating proteins (enzymes). However, accurately calculating the binding free energies for these charged and flexible ligands poses challenges due to slow conformational relaxation and the limitations of force fields. In this study, we examine the accuracy

The long-held assumption that the optimization of parameters for NDDO-descendant semiempirical methods may be performed without precise geometry optimization is assessed in detail; the relevant equations for the analytical evaluation of the geometry-corrected derivatives of molecular properties that account for changes in the optimum geometry are then presented. The first and second derivatives calculated

The electronic structures of lanthanide monoxides (LnO/LnO+) and monosulfides (LnS/LnS+) for all lanthanide series elements (Ln = La-Lu) have been systematically analyzed with sophisticated quantum chemical calculations. The ground electronic configuration has been determined to be Ln 4fn6s1 or 4fn+1 for the neutral molecules and Ln 4fn for the cations. The low-lying energy states resulting from spin-orbit

Classical density functional theory (DFT) provides a versatile framework to study the polymers with complex topological structure. Generally, a classical DFT describes the excess Helmholtz free energy of nonbonded chain connectivity due to excluded-volume effects and electrostatic correlations using the first-order thermodynamic perturbation theory (referred to as DFT-TPT1). Beyond first-order perturbation

Inductive program synthesis (PS) has recently begun to emerge as a useful new approach to automatically generate algorithms in quantum chemistry, as demonstrated in recent applications to the vibrational Schrödinger equation for simple model systems with one or two degrees-of-freedom. Here, we report a new physics-informed approach to inductive PS that is more conducive to the generation of discrete

Due to their computational intensity, long-range corrections of three-body interactions are particularly desirable, while there is no consensus of how to devise a cutoff scheme. A cutoff correction scheme for three-body interactions in molecular simulations is proposed that does not rest on complex integrals and can be implemented straightforwardly. For a limited number of configurations, the three-body

The successful simulation of proteins by molecular dynamics (MD) critically depends on the accuracy of the applied force field. Here, we modify the AMBER-family ff99SBnmr2 force field through improvements to the side-chain χ1 dihedral angle potentials in a residue-specific manner using conformational dihedral angle distributions from an experimental coil library as targets. Based on significant deviations

Kohn-Sham (KS) density functional theory (DFT) is an extremely popular, in-principle exact method, which can describe any many-electron system by introducing an auxiliary system of noninteracting electrons with the same density. When the number of electrons, N, changes continuously, taking on both integer and fractional values, the density has to be piecewise-linear, with respect to N. In this article

The concordant mode approach (CMA) is a promising new scheme for dramatically increasing the system size and level of theory achievable in quantum chemical computations of molecular vibrational frequencies. Here, we achieve advances in the CMA hierarchy by computations targeting CCSD(T)/cc-pVTZ (coupled cluster singles and doubles with perturbative triples using a correlation-consistent polarized-valence

Accurate calculation of solvation energies has long fascinated researchers, but complex interactions within bulk water molecules pose significant challenges. Currently, molecular solvation energy calculations are mostly based on implicit solvent approximations in which the solvent molecules are treated as continuum dielectric media. However, the implicit solvent approach is not ideal because it lacks

The techniques of computational photodynamics are increasingly employed to unravel reaction mechanisms and interpret experiments. However, misinterpretations in nonadiabatic dynamics caused by inaccurate underlying potentials are often difficult to foresee. This work focuses on revealing the systematic errors in the nonadiabatic simulations due to the underlying potentials and suggests a thrifty approach

Proteins are dynamic entities that adopt diverse conformations, which play a pivotal role in their function. Understanding these conformations is essential, and protein collective motions, particularly those captured by normal mode (NM) and their linear combinations, provide a robust means for conformational sampling. This work introduces a novel approach to obtaining a uniformly oriented set of a

A composite method, named ANN-G3S, is introduced, adapting from G3S theory and employing distinct sets of multiplicative scale factors. An artificial neural network (ANN)-based classification model is utilized to select optimal sets of four scale factors for electronic correlation and basis set expansion terms in electronic systems. The correlation and basis set terms are scaled by four parameters

We analyze the internal conversion dynamics within the Qy and Qx excited states of both bare and functionalized porphyrins, which are known to exhibit significantly different time constants experimentally. Through the integration of two complementary approaches, static calculation of per-mode reorganization energies and nonadiabatic molecular dynamics, we achieve a comprehensive understanding of the

The prospect of constructing global electric quadrupole moment functions (EQMFs) of diatomic molecules by morphing their theoretical approximants within the framework of the reduced radial curve (RRC) approach is explored by performing model calculations for the ground electronic states of H2 and HF. The reduced quadrupole moment curves probed, constructed for a set of differently accurate theoretical

In recent years, automated reaction path search methods have established the concept of a reaction route network. The Reaction Space Projector (ReSPer) visualizes the potential energy hypersurface into a lower-dimensional subspace using principal coordinates. The main time-consuming process in ReSPer is calculating the structural distance matrix, making it impractical for complex organic reaction route

Quantum embedding methods are powerful tools to exploit the locality of electron correlation, but thus far many wave function-in-wave function methods have focused on small (e.g., minimal) basis sets. One major challenge for extended basis sets lies in defining consistent atom- or fragment-localized orbitals in spite of the larger spatial extent of the underlying atomic orbitals. In this work, we modify

Many quantum algorithms rely on a quality initial state for optimal performance. Preparing an initial state for specific applications can considerably reduce the cost of probabilistic algorithms such as the well studied quantum phase estimation (QPE). Fortunately, in the application space of quantum chemistry, generating approximate wave functions for molecular systems is well studied, and quantum

The number of solvent molecules present in the system during molecular dynamics is the balancing act between the need to remove the boundary effects present in the system and the computational cost. Application of the telescopic-solvation box scheme during the estimation of the potential of mean force (PMF) can be advantageous in situations where the contribution of solvent far from the site of interest

One ambimodal transition state can lead to the formation of multiple products. However, it remains fundamentally unknown how the energy and entropy along the post-TS pathways mediate ambimodal selectivity. Here, we investigated the energy and entropy profiles along the post-TS pathways in four [4 + 2]/[6 + 4] cycloadditions. We observe that the pathway leading to the minor product involves a more pronounced

Global potential energy surface (PES) exploration provides a unique route to predict the thermodynamic and kinetic properties of unknown materials, but the task is highly challenging for systems with tight covalent bonds. Here, we develop the local-softening stochastic surface walking (LS-SSW) method for scanning corrugated PESs. LS-SSW transforms the vibrational mode space of a system by adding pairwise

The Jahn-Teller (JT) effect, as a spontaneous symmetry-breaking mechanism arising from the coupling between electronic and nuclear degrees of freedom, is a widespread phenomenon in molecular and condensed matter systems. Here, we investigate the influence of the JT effect on the photodissociation dynamics of CF3I molecules. Based on ab initio calculation, we obtain the three-dimensional potential energy

The use of the mutual information (MI) as a measure of the entanglement in quantum systems has gained a consensus in recent years, even if there is an ongoing effort to distinguish the classical and quantum contributions contained therein. This quantity has been first introduced in condensed matter physics, in particular, in studies based on the density matrix renormalization group method. This method

Addressing the growing demand for sustainable materials, this research paves the way for the efficient consumption and sustainable production of branched polylactide (PLA). A novel hybrid modeling approach combines first-principles (FP) model with artificial neural network (ANN) for ring-opening polymerization (ROP). The hybrid ANN, trained with FP model data, demonstrated optimal performance with

The success of Kohn-Sham density functional theory in predicting electronic properties from first-principles is key to its ubiquitous presence in condensed matter research. Central to this theory is the exchange-correlation functional, which can only be written in an approximate form using a handful of exact constraints. A recent criticism of these approximations is that they are designed to give an

A density functional theory framework is developed to study electronic excited states affected by an anisotropic dielectric environment. In particular, an anisotropic dielectric screened range-separated hybrid (SRSH[r]) functional is defined and combined with an anisotropic polarizable continuum model (PCM) implemented through a generalized Poisson equation solver. We develop the SRSH-PCM(r) approach

Coarse-grained molecular dynamics (CG-MD) simulations and subsequent persistent homology (PH) analysis were performed to correlate the structure and stress-strain behavior of polymer films. During uniaxial tensile MD simulations, the first principal component of the persistence diagram obtained by principal component analysis (PCA) was in good agreement with the stress-strain curve. This indicates

In this work, the two-center Dirac equation is solved numerically using an extension of an adapted B-spline basis set method previously implemented in relativistic atomic calculations (Fischer, C. F.; Zatsarinny, O. Comput. Phys. Commun. 2009, 180, 879). The robustness of the chosen numerical method, which avoids the appearance of spurious states common in other approaches, allows us to investigate

This study evaluates the performance of doubles-corrected random phase approximation (RPA) and higher random phase approximation (HRPA) approaches in predicting nuclear magnetic resonance (NMR) coupling constants involving fluorine. Their performance is benchmarked against experimental data and compared with that of higher-level theoretical methods, specifically second-order polarization propagator

Collective variables (CVs) describing slow degrees of freedom (DOFs) in biomolecular assemblies are crucial for analyzing molecular dynamics trajectories, creating Markov models and performing CV-based enhanced sampling simulations. While time-lagged independent component analysis (tICA) and its nonlinear successor, time-lagged autoencoder (tAE), are widely used, they often struggle to capture protein

Generalizing real-world data has been one of the most difficult challenges for application of machine learning (ML) in practice. Most ML works focused on improvements in algorithms and feature representations. However, the data quality, as the foundation of ML, has been largely overlooked, also leading to the absence of data evaluation and processing methods in ML fields. Motivated by the challenge

The projected hybrid orbital (PHO) method for treatment of multiple boundary atoms introduces a novel solution for handling the covalent connection between quantum mechanical (QM) and molecular mechanical (MM) regions in QM/MM calculations. By projecting the QM basis, typically adequately large for computational accuracy, onto a secondary minimal basis set on the boundary atom, it preserves electronic

New Delhi metallo-β-lactamase 1 (NDM-1) is an enzyme involved in the drug resistance of many bacteria against most of the widely adopted antibiotics, such as penicillins, cephalosporins, and carbapenems. Consequently, inhibiting NDM-1 swiftly has gained significant interest as a strategy to counteract this bacterial defense mechanism, thereby restoring the effectiveness of antibiotics. Among the inhibitors

Constraining molecules in simulations (such as with constant bond lengths and/or angles) reduces their degrees of freedom (DoF), which in turn affects temperature calculations in those simulations. When local temperatures are measured, e.g., from a set of atoms in a subvolume or from velocities in one Cartesian direction, the result can appear to unphysically violate equipartition of the kinetic energy

In cells, adenosine triphosphate (ATP) and guanosine triphosphate (GTP) molecules typically form tricoordinated or bicoordinated ATP·Mg2+ or GTP·Mg2+ complexes with Mg2+ ions and bind to proteins, participating in and regulating many important cellular functions. The accuracy of their force field parameters plays a crucial role in studying the function-related conformations of ATP·Mg2+ or GTP·Mg2+

Heparin is a natural highly sulfated unbranched periodic polysaccharide that plays a critical role in regulating various cellular events through interactions with its protein targets such as growth factors and cytokines. Although all-atom simulations of heparin-containing systems provide valuable insights into their structural and dynamical properties, long chains of heparin participate in many biologically

Nonadiabatic molecular dynamics (NAMD) has become an essential computational technique for studying the photophysical relaxation of molecular systems after light absorption. These phenomena require approximations that go beyond the Born-Oppenheimer approximation, and the accuracy of the results heavily depends on the electronic structure theory employed. Sophisticated electronic methods, however, make

We present enhancements in Monte Carlo simulation speed and functionality within an open-source code, gRASPA, which uses graphical processing units (GPUs) to achieve significant performance improvements compared to serial, CPU implementations of Monte Carlo. The code supports a wide range of Monte Carlo simulations, including canonical ensemble (NVT), grand canonical, NVT Gibbs, Widom test particle

Iron-sulfur clusters are crucial for biological electron transport and catalysis. Obtaining accurate geometries, energetics, manifolds of their excited electronic states, and reduction energies is important to understand their role in these processes. Using a [2Fe-2S] model complex with FeII and FeIII oxidation states, which leads to different charges, i.e., [Fe2S2(SMe)4]2-,3-,4-, we benchmarked a

Metal clusters often have a variety of possible structures, and they are calculated by a wide range of methods; however, fully converged benchmarks on the energy differences of structures and spin states that could be used to test or validate these methods are rare or nonexistent. Small lithium clusters are good candidates for such benchmarks to test different methods against well-converged relative

In this study, we present a method for selecting an arbitrary number of distinct configurations from a larger data set by applying k-means clustering to atomistic configuration fingerprints based on the CrystalNN model and radial distribution function (RDF). This approach improves the accuracy of fitting classical molecular dynamics interatomic potentials to density functional theory (DFT) data for

The propulsion mechanisms and collective dynamics of chemically powered Janus sphere dimers at the micro- and nanoscales, confined in a quasi-two-dimensional geometry, are investigated using a coarse-grained microscopic dynamical model. These active Janus dimers consist of two identical Janus spheres, featuring a catalytic cap on one hemisphere. The chemical reaction taking place on the catalytic surface

We introduce a method for reducing the number of valence states entering the calculation of screened the Coulomb interaction W in GW calculations. In this way, denoting with N the generic size of a system, the computational cost is brought from the typical O(N4) to the more favorable O(N2 ln N). The method becomes effective for large model structures. For enhancing the potentialities of our scheme

Comprehending water dynamics is crucial in various fields, such as water desalination, ion separation, electrocatalysis, and biochemical processes. While ab initio molecular dynamics (AIMD) accurately portray water's structure, computing its dynamic properties over nanosecond time scales proves cost-prohibitive. This study employs machine learning potentials (MLPs) to accurately determine the dynamic

Atom-centered neural network (ANN) potentials have shown high accuracy and computational efficiency in modeling atomic systems. A crucial step in developing reliable ANN potentials is the proper selection of atom-centered symmetry functions (ACSFs), also known as atomic features, to describe atomic environments. Inappropriate selection of ACSFs can lead to poor-quality ANN potentials. Here, we propose

Empirical parametrization underpins many scientific methodologies including certain quantum-chemistry protocols [e.g., density functional theory (DFT), machine-learning (ML) models]. In some cases, the fitting requires a large amount of data, necessitating the use of data obtained using low-cost, and thus low-quality, means. Here we examine the effect of using low-quality data on the resulting method

Boron-doped graphene nanoribbons are promising platforms for developing organic materials with magnetic properties. Boron dopants can be used to create localized magnetic states in nanoribbons with tunable interactions. Controlling the coherence times of these magnetic states is the very first step in designing materials for quantum computation or information storage. In this work, we address the connection

This study presents two new relativistic Gaussian basis sets without variational prolapse of double- and triple-ζ quality, RPF-2Z and RPF-3Z, along with augmented versions including additional diffuse functions, aug-RPF-2Z and aug-RPF-3Z, which are available for all s and p block elements from Hydrogen to Oganesson. The exponents of the Correlation/Polarization (C/P) functions are obtained from a polynomial

The N-representability problem consists in determining whether, for a given p-body matrix, there exists at least one N-body density matrix from which the p-body matrix can be obtained by contraction, that is, if the given matrix is a p-body reduced density matrix (p-RDM). The knowledge of all necessary and sufficient conditions for a p-body matrix to be N-representable allows the constrained minimization

A plethora of key biological events occur at the cellular membrane where the large spatiotemporal scales necessitate dimensionality reduction or coarse-graining approaches over conventional all-atom molecular dynamics simulation. Constructing coarse-grained descriptions of membranes systematically from statistical mechanical principles has largely remained challenging due to the necessity of capturing

Due to their efficient molecular design, nonfullerene acceptors (NFAs) have significantly advanced organic photovoltaics (OPVs). However, the lack of models to screen and evaluate candidate NFAs based on the resulting device performance has impeded the rapid development of high-performance molecules. This work introduces a computational framework utilizing a kinetic Monte Carlo (kMC) model to derive

A multiscale force field (FF) is developed for an aqueous solution of trivalent actinide cations An3+ (An = U, Np, Pu, Am, Cm, Bk, and Cf) by using a 12-6-4 Lennard-Jones type potential considering ion-induced dipole interaction. Potential parameters are rigorously and automatically optimized by the meta-multilinear interpolation parametrization (meta-MIP) algorithm via matching the experimental properties

A significant challenge in applying machine learning to computational chemistry, particularly considering the growing complexity of contemporary machine learning models, is the scarcity of available experimental data. To address this issue, we introduce an approach that derives molecular features from an intricate neural network-based model and applies them to a simpler conventional machine learning

Biomolecular condensates are essential in various cellular processes, and their misregulation has been demonstrated to underlie disease. Small molecules that modulate condensate stability and material properties offer promising therapeutic approaches, but mechanistic insights into their interactions with condensates remain largely lacking. We employ a multiscale approach to enable long-time, equilibrated

Geometry optimization is an important tool used for computational simulations in the fields of chemistry, physics, and material science. Developing more efficient and reliable algorithms to reduce the number of force evaluations would lead to accelerated computational modeling and materials discovery. Here, we present a delta method-based neural network-density functional theory (DFT) hybrid optimizer

A gradual but steady tide in theoretical chemistry is favoring the exploration of atomic and molecular interactions through the dynamical forces perceived and exerted by the particles of a system. By integrating the quantum mechanical force operator over all the spin and all but one of the spatial coordinates of the electrons, the Ehrenfest force density field reveals these forces directly and is separable

Locating plausible transition paths and enhanced sampling of rare events are fundamental to understanding the functional dynamics of biomolecules. Here, a constraint-based constant advance replicas (CAR) formalism of reaction paths is reported for identifying the most probable transition path (MPTP) between two given states. We derive the temporal-integrated effective dynamics governing the projected

Vibrational non-Condon effects, which describe how molecular vibrational transitions are influenced by a system's rotational and translational degrees of freedom, are often overlooked in spectroscopy studies of biological macromolecules. In this work, we explore these effects in the modeling of infrared (IR) spectra for nucleic acids in the 1600-1800 cm-1 region. Through electronic structure calculations