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

In this work, we introduce RGBChem, a novel approach for converting chemical compounds into image representations, which are subsequently used to train a convolutional neural network (CNN) to predict the HOMO-LUMO gap for compounds from the QM9 database. By modifying the arbitrary order of atoms present in .xyz files used to generate these images, it has been demonstrated that expanding the initial

In recent years, rapid improvements in computer hardware, as well as theoretical and algorithmic advances have enabled the calculation of ever larger systems in computational chemistry. In this avenue, we present efficient implementations of the scaled opposite-spin (SOS) second-order approximate coupled cluster (CC2) method and the closely related second-order algebraic diagrammatic construction (ADC(2))

We develop a statistical-mechanical model to calculate the structural properties of liquid water. The model is based on the generation of snowflake-like structures that serve as an approximation for the structure of liquid water. It is a two-dimensional model in which each water molecule has three interaction sites that can form three types of interactions, namely, hydrogen bonding, van der Waals contact

Transition-metal complexes serve as highly enantioselective homogeneous catalysts for various transformations, making them valuable in the pharmaceutical industry. Data-driven prediction models can accelerate high-throughput catalyst design but require computer-readable representations that account for conformational flexibility. This is typically achieved through high-level conformer searches, followed

We reformulated various polymer density-functional theories (PDFTs) into a unified computational framework, the same as that for self-consistent field theory (SCFT) calculations of discrete chains. We also proposed several ways to greatly improve the numerical accuracy of these PDFT calculations in both real and reciprocal space. Our preliminary results for confined tangent hard- and soft-sphere chains

The modified Hill-Sauer force field (mHSFF) proposed by Sholl and co-workers for the 8-membered ring (MR) siliceous zeolite has been modified by tuning the θ0 of ∠Si-O-Si and ∠O-Si-O and partial charges on Si and O to describe the window size of 12 MR siliceous zeolite more accurately. Proper temperature-dependent trend in minimum window size distribution was obtained using our newly proposed force

Discovering new ferroelectric materials is essential to overcoming the limitations of existing compounds, enabling innovative applications, advancing scientific understanding, and promoting environmental sustainability and technological progress. Symmetry is fundamental to the existence of ferroelectricity, and furthermore, ferroelectricity inherently requires the breaking of spatial inversion symmetry

Advanced electrochemical energy storage technologies require new electrolytes to be considered, so efficient computational characterization of ionic conductivity in a range of systems is of importance. In this manuscript we compare different equilibrium (EMD) and nonequilibrium molecular dynamics (NEMD) simulation algorithms to determine ionic conductivities. Aqueous magnesium ion batteries utilizing

Density functional tight binding (DFTB) is a quantum chemical simulation method based on an approximate density functional theory (DFT), known for its low computational cost and comparable accuracy to DFT. For several years, the application of DFTB in organic photovoltaics (OPV) has been limited by the absence of an appropriate set of parameters that adequately account for the relevant elements and

Hydrogen represents a highly promising alternative energy vehicle that facilitates the transition toward a carbon-neutral economy. However, the current hydrogen storage technologies are both inefficient and costly, which significantly limits the overall efficiency of hydrogen energy systems. The method of storing hydrogen via physical adsorption in Metal-Organic Frameworks (MOFs) has emerged as a particularly

Electrified solid-liquid interfaces play a crucial role in energy conversion, storage, photoconversion, sensors, and corrosion processes. While computational chemistry simulations can provide detailed insights into reaction mechanisms, aligning experimental and simulation results remains a significant challenge. In this work, we introduce the FDT-SJM method for ab initio molecular dynamics simulations

We propose an accurate clustering algorithm suitable for the varied and multidimensional data sets that correspond to temporal snapshots from on-the-fly nonadiabatic trajectory-based simulations of photoexcited dynamics. The algorithm approximates the underlying probability density function using variable kernel density estimation, with local maxima corresponding to cluster centers. Each data point

Utilizing the replica exchange transition interface sampling (RETIS) technique, we simulated the dynamics of sodium chloride dissociation in water. Subsequently, the resulting trajectories were analyzed using predictive power analysis (PPA), enabling the identification and quantification of collective variables (CVs) capable of forecasting the reaction occurrence. We improved the robustness of the

We present a novel approach for simulating thin film (TF) deposition from the gas phase at the atomistic scale, combining molecular dynamics (MD) and time-stamped force-bias Monte Carlo (tfMC). In this approach, MD, with its fine temporal resolution, captures fast events, such as incident atom-substrate collisions, while tfMC simulates slow relaxation processes, enhancing temporal scale coverage. The

We present a comprehensive investigation of the El-QDO embedding method [Phys. Rev. Lett. 131, 228001 (2023)], where molecular systems described through the electronic Hamiltonian are immersed in a bath of charged quantum harmonic oscillators, i.e., quantum Drude oscillators (QDOs). In the El-QDO model, the entire system of electrons and drudons─the quantum particles in the QDOs─is modeled through

In ReaxFF reactive force field conventional optimizations, the quality of the initial guesses plays a crucial role in determining the accuracy of the parametrization, particularly in high-dimensional spaces. To address this, we propose an adaptive sampling method that efficiently identifies high-quality initial guesses through uniform sampling followed by iterative refinement. Using this framework

A practical approach is put forward for a compact representation of the time evolving density matrix of the forced Morse oscillator. This approach uses the factorized product form of the unitary time evolution operator, à la Wei-Norman. This product form casts the time evolution operator in the basis of operators that form a closed Lie algebra. The further requirement that the Hamiltonian of the system

We present an imaginary time path-integral formalism for molecular systems including nuclear and electronic degrees of freedom based on the previous work of [Schmidt, J. R.; Tully, J. C. J. Chem. Phys. 2007, 127, 094103]. To sample the resulting path integral expression efficiently, a path integral Monte Carlo scheme is proposed, allowing the computation of finite temperature equilibrium properties

We introduce a new domain-based charge-transfer analysis tool exploiting the locality of pair Coupled Cluster Doubles orbitals. Unique features of the proposed model include the ability to monitor the direction of the charge flow between different parts or moieties of the system and its quantitative evaluation. We assess the predictive power of our new method for selected dye candidates of dye-sensitized

There is an ever-increasing interest in studying the properties and main characteristics of entangled atomic and molecular quantum states. As a matter of fact, merging two different areas of research like information theory and quantum physics/chemistry gives new insights to understand from a different framework some of the most basic quantum phenomena. In line with this, the calculation and analysis

Aggregation-induced emission (AIE) has become a key focus in luminescent material development, with substituent modulation being a critical strategy for expanding AIE systems. The S1/S0 minimum energy conical intersection (MECI) significantly influences molecular photophysical properties, making it essential for understanding the AIE phenomenon. Here, we employ anthracene derivatives, known for their

Many intrinsically disordered proteins (IDPs) can undergo a liquid-liquid phase separation (LLPS) in water, depending on solution conditions (temperature, pH, and ionic strength). There are two types of LLPS that are controlled by temperature: those occurring above a lower critical solution temperature (LCST) and those occurring below an upper critical solution temperature (UCST). IDP coarse-grained

Accurately modeling enzyme reactions through direct machine learning/molecular mechanics simulations remains challenging in describing the electrostatic coupling between the QM and MM subsystems. In this work, we proposed a reweighting ME (mechanic embedding) REANN (recursively embedded atom neural network) method that trains the potential and point charges of the QM subsystem in vacuo. The charge

Excitonic effects in the optical absorption spectra of solids can be described with time-dependent density-functional theory (TDDFT) in the linear-response regime, using a simple class of approximate, long-range corrected (LRC) exchange-correlation functionals. It was recently demonstrated that the LRC approximation can also be employed in real-time TDDFT to describe exciton dynamics. Here, we investigate

Machine learning interatomic potentials (MLIPs) offer a promising alternative to traditional force fields and ab initio methods for simulating complex materials such as oxide glasses. In this work, we present the first evaluation of the pretrained MACE (Multi-ACE) model [D.P. Kovács et al., J. Chem. Phys. 159(2023), 044118] for silicate glasses, using sodium silicates as a test case. We compare its

Reactive potentials serve as essential tools for investigating chemical reactions with moderate computational costs. However, traditional reactive potentials often depend on fixed, semiempirical parameters, which limits their accuracy and transferability. Overcoming these limitations can significantly expand the applicability of reactive potentials, enabling the simulation of a broader range of reactions

Monoclonal antibodies are utilized in a wide range of biomedical applications. The NIST monoclonal antibody is a resource for developing analysis methods for monoclonal antibody based biopharmaceutical platforms. Techniques from topological data analysis quantify structural features such as loops and tunnels which are not easily measured by classical data analysis methods. In this paper, we introduce

Mechanical force has been increasingly recognized to play crucial roles in regulating various cellular processes, which has inspired wide interest in elucidating the biophysical mechanism underlying these mechanobiological processes. In this work, we investigate the mechanical regulation of enzyme catalysis by developing a residue-resolved computational model capable of describing the full catalytic

Strongly correlated (SC) systems present significant challenges for classical quantum chemistry methods. Quantum computing, particularly the variational quantum eigensolver (VQE), offers a promising framework to address these challenges by inherently supporting exponentially large configuration spaces. However, its application to SC systems remains limited due to the single-reference nature of the

Efficient sampling in biomolecular simulations is critical for accurately capturing the complex dynamic behaviors of biological systems. Adaptive sampling techniques aim to improve efficiency by focusing computational resources on the most relevant regions of the phase space. In this work, we present a framework for identifying the optimal sampling policy through metric-driven ranking. Our approach

Elucidating collective variables (CVs) for biomolecular dynamics is crucial for understanding numerous biological processes. By leveraging the tensor-train data structure, a multilinear version of the AMUSE (Algorithm for Multiple Unknown Signals) algorithm for Koopman approximation (AMUSEt) was recently developed to identify CVs for biomolecular dynamics. To find slow CVs, AMUSEt transforms input

Although cross sections for double photoionization (DPI) are much smaller than single photoionization cross sections, DPI by a single photon is a sensitive means of probing correlated electron dynamics. We extend a rigorous method for computing double ionization amplitudes in both time-independent and time-dependent computational formalisms by eliminating the requirement that the one-electron testing

Harnessing a static magnetic field to drive molecular vibrations presents a promising avenue for controlling chemical processes. However, the coupling of nuclear dynamics with an external magnetic field has largely been explored only through classical approximations. In this work, we introduce a time-dependent quantum dynamics formalism based on London nuclear-electronic orbitals, enabling the simulation

We have implemented the fragment-based polarizable density embedding (PDE) model within a relativistic framework building on the eXact 2-Component (X2C) relativistic Hamiltonian, thereby taking the PDE method to a relativistic framework. The PDE model provides a robust solution to the electron-leakage problem, and we show that this newly implemented model offers an accurate way to model solvated systems

Active learning using models built on binding potency predictions from free energy perturbation (AL-FEP) has been proposed as a method for generating machine learning models capable of predicting biochemical potency for early-stage lead optimization where limited measured data are available. Two applications of AL-FEP are described here for different bromodomain inhibitor series that were developed