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Ab Initio Valence Bond Theory for Strongly Correlated Systems. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-06-20 Chen Zhou,Xun Wu,Fuming Ying,Wei Wu
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
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Transfer Learning for Predictive Molecular Simulations: Data-Efficient Potential Energy Surfaces at CCSD(T) Accuracy. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-06-20 Silvan Käser,Jeremy O Richardson,Markus Meuwly
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
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Modulation of Electric Field and Interface on Competitive Reaction Mechanisms. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-06-20 Pengchao Zhang,Xuefei Xu
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
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Combining Optical Control and Geometrical Optimization for Efficient Control of Competing Molecular Photoinduced Processes Far from the Ground State. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-06-20 David Veintemillas,Bo Y Chang,Ignacio R Sola
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
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Exploring Chemical Space with Chemistry-Inspired Dynamic Quantum Circuits in the NISQ Era. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-06-19 Lung-Yi Chen,Tai-Yue Li,Yi-Pei Li,Nan-Yow Chen,Fengqi You
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
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Combining Fast Exploration with Accurate Reweighting in the OPES-eABF Hybrid Sampling Method. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-06-18 Andreas Hulm,Robert P Schiller,Christian Ochsenfeld
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
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State-Interaction Approach for g-Matrix Calculations in TDDFT: Ground-Excited State Couplings and beyond First-Order Spin-Orbit Effects. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-06-18 Antonio Cebreiro-Gallardo,David Casanova
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
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IR Spectra for the EMIM-TFSI Ion Pair Using Deep Potentials. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-06-16 H Oliaei,N R Aluru
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
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Mixed Quantum/Classical Theory Approach to Rotationally Inelastic Molecular Collisions Implemented on a Quantum Computer. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-06-15 Jonathan Andrade-Plascencia,Tamila Kuanysheva,Dulat Bostan,Brian K Kendrick,Dmitri Babikov
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
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Quantum Time Dynamics Mediated by the Yang-Baxter Equation and Artificial Neural Networks. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-06-13 Sahil Gulania,Yuri Alexeev,Stephen K Gray,Bo Peng,Niranjan Govind
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
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Improved Correlation Optimized Virtual Orbital Algorithm for Plane-Wave Full Configuration Interaction Calculations. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-06-13 Mingyu Qiu,Zhenlin Zhang,Zhiyuan Zhang,Yexuan Lin,Yingzhou Li,Jinlong Yang,Wei Hu
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
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Accelerating Variable Cell Shape Molecular Dynamics with a Position-Dependent Mass Matrix. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-06-13 Martin Sommer-Jörgensen,Marco Krummenacher,Stefan Goedecker
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
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Charge-Induced Polarization in Dielectric Particle Systems: A Geometry-Dependent Effect. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-06-12 Eric B Lindgren
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
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Investigating Non-Markovian Effects on Quantum Dynamics in Open Quantum Systems. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-06-12 Mariia Ivanchenko,Peter L Walters,Fei Wang
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
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Liquid Phase Modeling in Porous Media: Adsorption of Methanol and Ethanol in H-MFI in Condensed Water. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-06-11 Subrata Kumar Kundu,Muhammad Zeeshan,Panuwat Watthaisong,Andreas Heyden
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
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Locality-Sensitive Hashing-Based Data Set Reduction for Deep Potential Training. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-06-10 Anmol,Anuj Kumar Sirohi,Neha,Jayadeva,Sandeep Kumar,Tarak Karmakar
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
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Multiproperty Deep Learning of the Correlation Energy of Electrons and the Physicochemical Properties of Molecules. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-06-10 Yan Yuan,Yilin Zhao,Linling Lu,Junjie Wang,Jingbo Chen,Shubin Liu,Paul W Ayers,Dongbo Zhao
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
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In Silico Study of Ionizable Lipid Nanoparticles Using the SPICA Force Field. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-06-10 Akhil Pratap Singh,Hiroki Tanaka,Yusuke Miyazaki,Shusaku Nagano,Wataru Shinoda
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
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PHAST-MBD: Implementing Many-Body Dispersion in the PHAST 2.0 Potential, Results for Noble Gases. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-06-10 Matthew Mostrom,Adam Hogan,Logan Ritter,William Morris,Brian Space
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
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Comparison of QM Methods for the Evaluation of Halogen-π Interactions for Large-Scale Data Generation. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-06-09 Marc U Engelhardt,Markus O Zimmermann,Finn Mier,Frank M Boeckler
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
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Adaptive Variational Quantum Simulations of Periodic Materials Using Qubit-Encoded Wave Functions. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-06-09 Xiaopeng Li,Yi Fan,Jie Liu,Zhenyu Li,Jinlong Yang
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
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Pair-Density Functional Theory Based on the Spin-Projected Unrestricted Hartree-Fock Method. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-06-09 Shirong Wang,Xin Xu
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
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Solute Tempered Adiabatic Free Energy Dynamics for Enhancing Conformational Space Sampling. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-06-05 Shitanshu Bajpai,Charlles R A Abreu,Nisanth N Nair,Mark E Tuckerman
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
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Variations of the Depletion Zones around Inclusions Explain the Complexity of Brush-Induced Depletion Interactions. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-06-05 Daeseong Yong,Ji Woong Yu,Bae-Yeun Ha,Changbong Hyeon
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
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Transferability of Data Sets between Machine-Learned Interatomic Potential Algorithms. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-06-05 Samuel P Niblett,Panagiotis Kourtis,Ioan-Bogdan Magdău,Clare P Grey,Gábor Csányi
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
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Efficient Variable-Time Implementation of the RT-EOM-CCSDT Approach for Core and Valence Ionization Spectral Functions. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-06-04 F D Vila,J J Kas,J J Rehr,K Kowalski
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
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Simulating Open Quantum Dynamics with a Neural Network-Enhanced Non-Markovian Stochastic Schrödinger Equation. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-06-04 Kaihan Lin,Xing Gao
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
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Correction to "A Set of Quantum-Mechanically Derived Force Fields for Natural and Synthetic Retinal Photoswitches". J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-06-03 Razan E Daoud,Simone Veglianti,Anna Piras,Abderrahmane Semmeq,Samuele Giannini,Giacomo Prampolini,Daniele Padula
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Analytic Nuclear Gradients for Complex Potential Energy Surfaces: A Projected CAP Approach. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-06-03 Soubhik Mondal,Ksenia B Bravaya
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
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Size-Reduction of Phonon Band Calculation for Coarse-Grained Molecular Crystals Using "Independent Stiffness Approximation". J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-06-03 Yue Wang,Masataka Seshimo,Hirohiko Houjou
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
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PolyPal: A Python Package for Molecular Dynamics Simulation of Amorphous Polymers. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-06-03 Molly C Warndorf,Timothy M Swager,Alfredo Alexander-Katz
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.
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The PHAST 2.0 Force Field for General Small Molecule and Materials Simulations. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-06-03 Adam Hogan,Logan Ritter,Brian Space
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
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Grid-Context Convolutional Model for Efficient Molecular Surface Construction from Point Clouds. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-06-03 Yongxian Wu,Ray Luo
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
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Accuracies of the One-Electron and On-Top Two-Electron Densities Obtained with the Full Configuration-Interaction Employing Commonly Used Basis Sets. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-06-02 Filip Pra̧tnicki,Krzysztof Strasburger
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
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Physical Prior Mean-Driven Bayesian Committee Molecular Dynamics (BCMD): From Born-Oppenheimer Dynamics to Curvature-Guided Non-Adiabatic Dynamics. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-06-02 Chong Teng,Junwei Lucas Bao
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
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Analytical Gradient Theory for Density-Fitted Exact Two-Component Hartree-Fock, State-Specific Complete Active Space Self-Consistent Field, and Second-Order Møller-Plesset Perturbation Theories. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-05-29 Jae Woo Park
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
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Multi-Level Coupled-Cluster Description of Crystal Lattice Energies. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-05-29 Krystyna Syty,Grzegorz Czekało,Khanh Ngoc Pham,Marcin Modrzejewski
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
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MA(R/S)TINI 3: An Enhanced Coarse-Grained Force Field for Accurate Modeling of Cyclic Peptide Self-Assembly and Membrane Interactions. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-05-28 Alfonso Cabezón,Rebeca Garcia-Fandino,Ángel Piñeiro
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
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Approximation to Second Order N-Electron Valence State Perturbation Theory: Limiting the Wave Function within Singles. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-05-28 Yang Guo,Katarzyna Pernal
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
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A Quantum Computational Method for Corrosion Inhibition. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-05-28 Naman Jain,Rosa Di Felice
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
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Trajectory Retracing of the Packaging and Ejection Processes of Coaxially Spooled DNA. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-05-27 Chung Bin Park,Bong June Sung
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
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Multistep Approach for Simulating Raman Spectra of Amorphous Materials: The Case of Li3PS4 Glass Electrolyte. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-05-27 Jakub Pawelko,Eric Furet,Gwenael Duplaix-Rata,Nicolas Perrin,Xavier Rocquefelte
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
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Global Optimization of Large Molecular Systems Using Rigid-Body Chain Stochastic Surface Walking. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-05-27 Tong Guan,Xin-Tian Xie,Xiao-Jie Zhang,Cheng Shang,Zhi-Pan Liu
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
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IPAMD: A Plugin-Based Software for Biomolecular Condensate Simulations. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-05-27 Xiao-Yang Liu,You-Liang Zhu,Yu-Ze Jiang,Shao-Kang Shi,Li Zhao,Zhong-Yuan Lu
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
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Probing Limitations of Co-Alchemical Charge Changes in Free-Energy Calculations. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-05-26 Nadine Grundschober,Dražen Petrov
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
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Impact of Derivative Observations on Gaussian Process Machine Learning Potentials: A Direct Comparison of Three Modeling Approaches. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-05-23 Yulian T Manchev,Paul L A Popelier
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:
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Refining a Generic Force Field for Predicting Phase Transitions in Wine-Rack Metal-Organic Frameworks. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-05-23 Dohoon Kim,Yunsung Lim,Jihan Kim
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
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Finite-Temperature Double Proton Transfer in Formic Acid Dimer via Constrained Nuclear-Electronic Orbital Molecular Dynamics: Lower Barriers and Enhanced Rates from Nuclear Quantum Delocalization. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-05-22 Yuzhe Zhang,Zhe Liu,Yang Yang
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
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Time-Dependent Orbital-Free Density Functional Theory: A New Development of the Dynamic Kinetic Energy Potential. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-05-20 Xu Zhang,Chen Huang
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
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Expectation-Maximization-Based Optimization of Neural Quantum States for Ab Initio Quantum Chemistry. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-05-20 Shen Fang,Hongkun Dou,Zeyu Li,Wang Han,Qingfei Fu,Lijun Yang
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
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Temperature Effects in Singlet Fission under Vibrational Strong Couplings in a Cavity. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-05-20 Xiao Wang,Kewei Sun,Haomin Xiao,Yang Zhao,Maxim F Gelin
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
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Addressing the High-Throughput Screening Challenges of Inverted Singlet-Triplet Materials by MRSF-TDDFT. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-05-20 Alireza Lashkaripour,Woojin Park,Mohsen Mazaherifar,Cheol Ho Choi
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
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Localized Orbital Scaling Correction to Linear-Response Time-Dependent Density Functional Approximations. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-05-20 Ye Li,Chen Li
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
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Locating Transition States for Biomolecular Dynamics via Invertible Dimensionality Reduction. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-05-20 Jianyu Yang,Huanlei Guo,Song Liu,Kun Xi,Qiang Wu,Yixun Li,Kuo Fang,Kaiyi Zhou,Chang Su,Bing-Yi Jing,Hao Wu,Lizhe Zhu
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
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Dipolar Cross-Correlations in Aqueous Systems: How Surfaces Influence Water's Action at a Distance. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-05-19 Sayantan Mondal,Saumyak Mukherjee,Biman Bagchi
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
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Second-Order Complete Active Space Perturbation Theory (CASPT2) and N-Electron Valence State Perturbation Theory (NEVPT2) Based on Adaptive Sampling Configuration Interaction Self-Consistent Field (ASCI-SCF). J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-05-19 Kyeong Su Min,Jae Woo Park
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
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Computing Bulk Phase IR Spectra from Finite Cluster Data via Equivariant Neural Networks. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-05-17 Aman Jindal,Philipp Schienbein,Banshi Das,Dominik Marx
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
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Accurate Prediction of Open-Circuit Voltages of Lithium-Ion Batteries via Delta Learning. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-05-15 Wai Yuet Chiu,Chongzhi Zhang,Rongzhi Gao,Ziyang Hu,GuanHua Chen
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
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HDXRank: A Deep Learning Framework for Ranking Protein Complex Predictions with Hydrogen-Deuterium Exchange Data. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-05-14 Liyao Wang,Andrejs Tučs,Songting Ding,Koji Tsuda,Adnan Sljoka
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
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Frozen-Pair-Type pCCD-Based Methods and Their Double Ionization Variants to Predict Properties of Prototypical BN-Doped Light Emitters. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-05-14 Ram Dhari Pandey,Matheus Morato F de Moraes,Katharina Boguslawski,Pawel Tecmer
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