Strongly correlated systems, characterized by significant multiconfigurational character, pose a persistent challenge in quantum chemistry. While molecular orbital (MO)-based multiconfigurational self-consistent field methods such as CASSCF and CASPT2 have become standard tools for treating such systems, valence bond (VB) theory offers a conceptually distinct and chemically intuitive alternative. Rooted

Accurate potential energy surfaces (PESs) are critical for predictive molecular simulations. However, obtaining a PES at the highest levels of quantum chemical accuracy, such as CCSD(T), becomes computationally infeasible as molecular size increases. This work presents CCSD(T)-quality PESs using data-efficient techniques based on transfer learning to obtain state-of-the-art accuracy at a fraction of

Recently, much evidence has accumulated, showing that electric fields and water interfaces influence the characteristics and alignment of biomolecules and greatly boost reaction rates. The prototropic tautomerism is a fundamental process in biological systems; however, a comprehensive understanding of the electric field effects and interfacial effects on it is still lacking. In this work, we performed

The yield of a photochemical process can be maximized by optimizing the driving fields, such as in optical control, or the initial wave function, as in geometrical optimization. We combine both algorithms in an iterative process, showing very fast convergence and great improvement in the yields, as applied to driving population to the second excited state of the molecular hydrogen cation through the

The generation of chemical molecular structures is crucial for advancements in drug design, materials science, and related fields. With the rise of artificial intelligence, numerous generative models have been developed to propose promising molecular structures to specific challenges. However, exploring the vast chemical space using classical generative models demands extensive chemical structure data

On-the-fly probability enhanced sampling (OPES) has recently been introduced [Invernizzi, M.; Parrinello, M. J. Chem. Theory Comput. 2022, 18, 3988-3996], with important improvements over the highly popular metadynamics methods. In our work, we introduce a new combination of OPES with the extended-system adaptive biasing force (eABF) method. We show that the resulting OPES-eABF hybrid is highly robust

We introduce a state-interaction approach for computing g-matrices within time-dependent density functional theory (TDDFT) and the Tamm-Dancoff approximation (TDA), applied here for the first time. This method provides a detailed understanding of g-shifts by explicitly accounting for spin-orbit couplings (SOC) and excitation energies, enabling the analysis of different SOC orders and their contributions

Despite advances in the characterization of ionic liquids (ILs), elucidating their infrared (IR) spectra remains challenging due to the computational demands of ab initio methods. In this study, we employ a framework that integrates deep potential (DP) and deep Wannier (DW) models to investigate the configuration, dipole moment, and IR spectra of a 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide

All elements of a quantum algorithm for calculations of rotationally inelastic molecule + atom scattering within the framework of a mixed quantum/classical theory are outlined. In this approach, the rotational motion of the molecule is described quantum mechanically using the time-dependent Schrödinger equation, while the scattering process of two collision partners is treated classically. The matrix

Quantum computing shows great potential, but errors pose a significant challenge. This study explores new strategies for mitigating quantum errors using artificial neural networks (ANNs) and the Yang-Baxter equation (YBE). Unlike traditional error mitigation methods, which are computationally intensive, we investigate artificial error mitigation. We developed a novel method that combines ANNs for noise

Full configuration interaction (FCI) calculations have historically faced significant challenges in dealing with periodic systems. The plane-wave basis sets are valued for their efficiency and broad applicability in various computational physics and chemistry simulations. Because of their natural periodicity, the plane-wave basis sets offer a potential solution to this problem. Moreover, FCI can address

In molecular dynamics (MD), the accessible time scales are limited by the necessity to choose sufficiently small time steps so that the fastest vibrations of the system can still be modeled. Mass tensor molecular dynamics (MTMD) aims to increase the time step by augmenting the Hamiltonian with a position-dependent mass matrix. Higher masses are assigned to modes with fast vibrations. These modes are

Electrostatic interactions in systems composed of finite-sized dielectric materials extend well beyond simple point-charge approximations, particularly when many-body polarization effects become significant. This study shows that asymmetries in the size or net charge of spherical particles can trigger nontrivial phenomena, including like-charge attraction and intricate force balances involving neutral

The reduced description of the quantum dynamic processes in the condensed phase environment leads to the equation of motion with a memory kernel. Such a memory effect, termed non-Markovianity, presents more complex dynamics compared to its memoryless or Markovian counterpart, and many chemical systems have been demonstrated through numerical simulations to exhibit non-Markovian quantum dynamics. Explicitly

Zeolites are used in the chemical and separation industries for their exceptional selectivity, adsorption capacity, regenerability, and stability in gas and liquid phase processing. Here, we developed an explicit solvation method for predicting solvent/condensed phase effects on adsorption free energies in microporous media such as zeolites based on the hybrid quantum mechanical/molecular mechanical

Machine learning methods provide a great scope for developing ab initio quality potentials for diverse systems, ranging from simple fluids to complex solids. However, these methods typically require extensive data sets for effective model training, and the accuracy of the ML potential is highly dependent on data quality, necessitating expensive ab initio calculations. To address this challenge, we

The density-based descriptors from the information-theoretic approach (ITA) are used as features for multiproperty deep learning (DL), predicting the correlation energy and physicochemical properties of molecules. In addition to response properties (molecular polarizability αiso and NMR shielding constant σiso) where ITA has been shown to work well before, we consider four conceptually distinct but

Lipid nanoparticles (LNPs), composed of ionizable amino lipids, phosphatidylcholines (PC) lipids, and cholesterol, have shown promise as delivery vehicles for therapeutic oligonucleotides in various applications, including cancer immunotherapies, cellular reprogramming, genome editing, and viral vaccines (e.g., COVID-19 vaccines). However, the molecular characterization of ionizable amino lipids and

A recently published empirical force field (herein PHAST or PHAST 2.0) is employed in its many-body dispersion-corrected form (PHAST-MBD) to examine the effects of collective dispersion interactions. Rare gases are used as a systematic way to test increasing importance of van der Waals attractions in systems dominated by repulsion-dispersion that are a challenge to extant force fields. The effects

Halogen-π interactions play a pivotal role in molecular recognition processes, drug design, and therapeutic strategies, providing unique opportunities for enhancing and fine-tuning the binding affinity and specificity of pharmaceutical agents. The present study systematically benchmarks various combinations of quantum mechanical (QM) methods and basis sets to characterize halogen-π interactions in

Materials design stands to be one of the most promising applications of quantum computing. However, the presence of noise in near-term quantum devices restricts quantum simulations of materials to shallow circuits. In this work, we present circuit-efficient variational quantum eigensolver (VQE) simulations of periodic materials using qubit-encoded wave functions based on Adaptive Derivative-Assembled

Spin-projected unrestricted Hartree-Fock (SUHF) theory is a valuable method that effectively addresses static correlation. To further enhance its accuracy, it is important to augment it with dynamic correlation. Based on SUHF theory, we propose a pair-density functional theory, namely, SU-PDFT, which formally follows the concept of multiconfiguration pair-density functional theory (MC-PDFT). SU-PDFT

Collective variable (CV) and generalized ensemble-based enhanced sampling methods are widely used for accelerating barrier-crossing events and enhancing conformational sampling in molecular dynamics simulations. Temperature-accelerated molecular dynamics (TAMD)/driven-adiabatic free energy dynamics (d-AFED) uses extended variables thermostated at high temperature to achieve better exploration of conformational

Depletion forces are relevant in a variety of contexts such as the phase behavior of colloid-polymer or colloid-depletant mixtures and clustering of inclusions in mobile brushes. They arise from the tendency to minimize the volume of the depletion zone formed around colloidal particles or inclusions. In comparison to depletion interactions widely studied for colloidal particles or polymers in a suspension

The emergence of Foundational Machine Learning Interatomic Potential (FMLIP) models trained on extensive data sets motivates attempts to transfer data between different ML architectures. Using a common battery electrolyte solvent as a test case, we examine the extent to which training data optimized for one machine-learning method may be reused by a different learning algorithm, aiming to accelerate

The real-time equation-of-motion coupled cluster (RT-EOM-CC) method has been shown to accurately predict the core and valence photoelectron spectral functions for a variety of small to moderately sized molecular systems. Previous many-body implementations included single and double CC excitations. Here, we extend the approach to include full triples CC excitations. To reduce the computational demand

The non-Markovian stochastic Schrödinger equation (NMSSE) offers a promising approach for open quantum simulations owing to its low scaling complexity and suitability for parallel computing. However, its application at low temperatures faces significant convergence challenges. While short-time evolution converges quickly, long-time evolution requires a much larger number of stochastic trajectories

The complex absorbing potential (CAP) technique is one of the commonly used non-Hermitian quantum mechanics approaches for characterizing electronic resonances. CAP, combined with various electronic structure methods, has shown promising results in quantifying the energies and widths of electronic resonances in molecular systems. While CAP-based methods can be used to map complex potential energy surfaces

We propose a new computational scheme for calculating the phonon band diagrams of molecular crystals. In conventional computational models, the vibrational unit is typically an atom, which leads to high computational costs and less intuitive vibrational mode analyses, especially when dealing with molecular crystals. In the current study, we utilized a coarse-graining (CG) scheme that treats molecules

Easily tunable and processable, porous organic polymers (POPs) have found increasing utility in various applications. Molecular modeling and simulations are invaluable tools in polymer science but remain under-reported in the POP literature. Accurate modeling and simulation of these materials could boost the discovery of high-performance POPs and allow for a more thorough contribution to big data.

Classical, empirical molecular simulation has become increasingly important in chemistry due to its ability to accurately model and resolve experimental phenomena on the atomic scale. Still, many challenges remain including obtaining subkilojoules per mole accuracy while maintaining speed, computational efficiency, and transferability to novel and heterogeneous chemistries. Further, a distinct lack

Accurate and efficient molecular surface representation is essential in computational chemistry, impacting applications such as enzymology, rational drug design, and molecular recognition. Traditional approaches, including solvent-accessible surface (SAS) and solvent-excluded surface (SES), are widely used but often suffer from computational inefficiencies and limited adaptability to complex molecular

Accuracy studies of the one-electron density, the two-electron on-top density, and the on-top ratio were conducted. The ground-state wave function of the dihydrogen molecule was approximated by a linear combination of explicitly correlated Gaussian functions, and the results juxtaposed with those stemming from the full Configuration Interaction (CI) method with commonly used correlation-consistent

Molecular dynamics (MD), when combined with high-accuracy quantum mechanics for energy and atomic force evaluations, is an indispensable tool extensively used to uncover in-depth atomistic details of chemical processes. However, the computational cost of first-principles predictions can be prohibitive. Here, we introduce a direct dynamics method, Bayesian Committee molecular dynamics (BCMD), which

The exact two-component (X2C) relativistic quantum chemistry calculations can be used to describe scalar relativistic effects and spin-orbit couplings at reasonable computational cost. However, they have limited applicability to wave function-based quantum chemistry methods, particularly geometric optimizations and dynamics simulations, owing to the high computational demands of these methods in sizable

The many-body expansion (MBE) of the lattice energy enables an ab initio description of molecular solids using correlated wave function approximations. However, the practical application of MBE requires computing the large number of n-body contributions efficiently. To this end, we employ a multi-level coupled-cluster approach which adapts the approximation level based on interaction type and intermolecular

Self-assembled nanotubes (SCPNs) formed by alternating chirality α-Cyclic Peptides (d,l-α-CPs) have presented interesting biological applications, such as antimicrobial activity or ion transmembrane transport. Due to difficulties to follow these processes with experimental techniques, Molecular Dynamics (MD) simulations have been commonly used to understand the mechanism that led to their biological

Inspired by the linearized adiabatic connection (AC0) theory, an approximation to second-order N-electron valence state perturbation theory (NEVPT2) has been developed, termed NEVPT within singles (NEVPTS). This approach utilizes amplitudes derived from approximate single-excitation wave functions, requiring only 3rd-order reduced density matrices (RDMs). Consequently, it avoids the computational bottleneck

We present a hybrid classical-quantum computational pipeline for the determination of adsorption energies of a benzotriazole molecule on an aluminum alloy surface relevant for the transport industry, in particular to address the corrosion problem. The molecular adsorbate and substrate alloy were selected by interrogating molecular and materials databases, in search for desired criteria. The protocol

The coaxial spool structure of DNA has been regarded as an equilibrium conformation inside of a viral capsid. It has also been accepted that the DNA conformation inside the viral capsid should correlate strongly with the ejection of DNA out of the viral capsid. However, how the coaxial spool structure of DNA would affect the ejection kinetics remains elusive at the molecular level. In this study, we

Glasses are widely used for their various applications, which arise from their inherent lack of long-range ordering. This characteristic makes it challenging to describe their atomic properties. To facilitate and accelerate glass research, computational simulations, such as molecular dynamics or Monte Carlo simulations, are commonly employed to model the structure of these amorphous materials. However

The global potential energy surface (PES) search of large molecular systems remains a significant challenge in chemistry due to "the curse of dimensionality". To address this, here we develop a rigid-body chain method in the framework of a stochastic surface walking (SSW) global optimization method, termed rigid-body chain SSW (RC-SSW). Based on the angle-axis representation for a single rigid body

The study of intrinsically disordered proteins (IDPs) and their role in biomolecular condensate formation has become a critical area of research, offering insights into fundamental biological processes and therapeutic development. Here, we present IPAMD (Intrinsically disordered Protein Aggregation Molecular Dynamics), a plugin-based software designed to simulate the formation dynamics of biomolecular

Molecular dynamics simulations are nowadays one of the key methods to investigate the (thermo)dynamics of protein-ligand binding at atomic resolution. The calculation of binding free energies of charged species is an encountered problem in molecular dynamic simulations. This is due to the approximation of the long-range electrostatic interaction. Here, we explore the discrepancies and biases of different

Machine learning (ML) potentials have become a well-established tool for providing inexpensive, yet quantum-mechanically accurate, atomistic simulations. Here, we extend our current modeling procedure, based on Gaussian process regression, to include derivative observations into the ML models. We directly compare three system-energy modeling approaches based on quantum mechanically derived quantities:

Metal-organic frameworks (MOFs) are versatile materials with tunable properties, enabling their application in diverse fields. Flexible MOFs, characterized by their dynamic response to external stimuli, have gained significant attention for their gas storage and energy storage capabilities. However, predicting their flexibility, especially in wine-rack frameworks undergoing phase transitions, remains

Proton transfer plays a crucial role in various chemical and biological processes, yet accurately and efficiently describing such reactions remains challenging due to nuclear quantum effects (NQEs). In this work, we employ constrained nuclear-electronic orbital molecular dynamics (CNEO-MD), a method that inherently incorporates NQEs into classical dynamics to investigate double proton transfer in the

Time-dependent orbital-free density functional theory (TD-OFDFT) is a promising method for investigating electronic dynamics in large metallic systems. One key component in TD-OFDFT is the dynamic kinetic energy potential (DKEP), which contains the memory effect missed in the adiabatic OFDFT. In this work, we developed a new DKEP based on a density-dependent kernel that is nonlocal in both space and

Accurate solutions for ab initio quantum chemistry are critical for understanding chemistry on the atomic scale. Recently, optimization methods for neural quantum states (NQSs) based on deep neural networks have shown great potential for improving computational accuracy over traditional methods, but they inevitably introduce the challenge of substantial computational costs. To address this challenge

We use the numerically accurate multiple Davydov ansatz with thermo-field dynamics (mDA-TFD) methodology to simulate quantum evolution of the conical intersection (CI)-driven singlet fission (SF) system strongly coupled to an optical cavity. We comprehensively analyze the impact of temperature, cavity-mode frequencies and lifetimes, vibrational coupling, and averaged numbers of photons pumped into

A new computational protocol utilizing mixed-reference spin-flip time-dependent density functional theory (MRSF-TDDFT) and the DTCAM-STG exchange-correlation functional has been developed to identify materials with inverted singlet-triplet (INVEST) energy levels. This protocol surpasses existing quantum chemical methods in both accuracy and computational efficiency for predicting ΔEST, addressing challenges

The localized orbital scaling correction (LOSC) method, which was developed for eliminating the delocalization error in density functional approximations (DFAs), is extended to the linear-response regime for calculating excitation energies with time-dependent density functional theory (TDDFT). Corrections to the exchange-correlation kernel are derived within the frozen-orbitalet approximation. Extensive

Locating the transition states (TS) for the conformational changes of biomacromolecules is among the major tasks of biomolecular simulations, as they are the bottlenecks of motion encoding key mechanistic insights. However, identifying the short-lived TSs from (even abundant) simulation data has been a long-standing challenge due to the high dimensionality of the molecules. Gentlest ascent dynamics

Water exhibits several anomalous properties that shape the way water functions in biological and chemical environments. For example, the unusually large dielectric constant of water can be traced back to dipolar cross-correlations, which are manifestations of both its dipole moment and the extended hydrogen bond (HB) network. However, a common platform that explores the origins of such cross-correlations

We have developed the second-order complete active space perturbation theory (CASPT2) and N-electron valence state perturbation theory (NEVPT2) based on the adaptive sampling configuration interaction self-consistent field (ASCI-SCF) reference function. Our method directly calculates the intermediate matrices needed for the CASPT2 and NEVPT2 calculations to reduce the memory required for storing the

Calculating accurate IR spectra from molecular dynamics simulations is crucial for understanding structural dynamics and benchmarking simulations. While machine learning has accelerated such calculations, leveraging finite-cluster data to compute condensed-phase IR spectra remains unexplored. In this work, we address a fundamental question: Can a machine learning model trained exclusively on electronic

Accurate prediction of lithium-ion battery capacity before material synthesis is crucial for accelerating battery material discovery. The capacity can be theoretically determined by integrating open-circuit voltage vs state of charge (OCV-SoC) curves of electrode materials. OCV-SoC curves are traditionally computed using first-principles methods, either through geometry optimization (GO) with density

Accurate modeling of protein-protein complex structures is essential for understanding biological mechanisms. Hydrogen-deuterium exchange (HDX) experiments provide valuable insights into binding interfaces. Incorporating HDX data into protein complex modeling workflows offers a promising approach to improve prediction accuracy. Here, we developed HDXRank, a graph neural network (GNN)-based framework

Novel, robust, computationally efficient, and reliable theoretical methods are indispensable for the large-scale modeling of desired molecular properties. One such example is the orbital optimized pair coupled-cluster doubles (oo-pCCD) ansatz and its various CC extensions, which range from closed-shell ground- and excited-state models to open-shell variants. Specifically, the ionization-potential equation-of-motion