Pub Date : 2026-03-13DOI: 10.1021/acs.jctc.5c01811
Sandra Liz Simon,Nitin Kaistha,Vishal Agarwal
Mapping reaction pathways on complex potential energy surfaces (PESs) and locating transition states (TSs) is often used for understanding chemical reaction mechanism(s). The nudged elastic band (NEB) method is widely used for this purpose, but it becomes computationally expensive for large systems due to the repeated evaluation of energies and forces. We present an active learning algorithm coupled with the nudged elastic band, AL-NEB, for efficient convergence to the TS. AL-NEB constructs a surrogate PES and actively selects training points in two phases: (a) Exploration-Exploitation and (b) Renunciation. Strategies have been introduced for making the algorithm efficient and stable. We show the efficacy of the algorithm on several 2D analytical potentials, HCN isomerization, keto-enol tautomerization, and high-dimensional heptamer island diffusion (up to 525 degrees of freedom). In all cases, AL-NEB locates the "exact" TS on the chosen model chemistry with an order-of-magnitude fewer force evaluations than the standard NEB, demonstrating its scalability and efficiency.
{"title":"An Active Learning Algorithm for Identifying Transition States on a Potential Energy Surface.","authors":"Sandra Liz Simon,Nitin Kaistha,Vishal Agarwal","doi":"10.1021/acs.jctc.5c01811","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c01811","url":null,"abstract":"Mapping reaction pathways on complex potential energy surfaces (PESs) and locating transition states (TSs) is often used for understanding chemical reaction mechanism(s). The nudged elastic band (NEB) method is widely used for this purpose, but it becomes computationally expensive for large systems due to the repeated evaluation of energies and forces. We present an active learning algorithm coupled with the nudged elastic band, AL-NEB, for efficient convergence to the TS. AL-NEB constructs a surrogate PES and actively selects training points in two phases: (a) Exploration-Exploitation and (b) Renunciation. Strategies have been introduced for making the algorithm efficient and stable. We show the efficacy of the algorithm on several 2D analytical potentials, HCN isomerization, keto-enol tautomerization, and high-dimensional heptamer island diffusion (up to 525 degrees of freedom). In all cases, AL-NEB locates the \"exact\" TS on the chosen model chemistry with an order-of-magnitude fewer force evaluations than the standard NEB, demonstrating its scalability and efficiency.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"265 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147439432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We introduce a mean-field-like computational model for calculating ionization potentials (IPs) based on the pair Coupled Cluster Doubles (pCCD) wave function. Specifically, our model combines the extended Koopmans' theorem (EKT) with the advantages of a variationally orbital-optimized (oo)-pCCD ansatz. The computational cost of the EKT(pCCD) method is negligible (O(N3)) as the response 1- and 2-particle reduced density matrices used to construct the generalized Fock matrix are readily available after an oo-pCCD calculation. We benchmarked our new computational model for IPs of atoms, small molecules, and a set of organic acceptor molecules against experimental and theoretical reference data. The EKT(pCCD) model significantly improves upon the modified Koopmans' approach [J. Chem. Phys. 162, 184110 (2025)], and the obtained IPs are comparable to those of computationally more expensive IP-EOM-pCCD-based models, approaching CCSD(T) reference values (with a mean error of 0.05 eV). Most importantly, the EKT(pCCD) approach is almost independent of the basis set size, and reliable IPs are already obtained with small basis sets.
{"title":"Ionization Potentials at Mean-Field Computational Cost: The Extended Koopmans' Framework for pCCD.","authors":"Seyedehdelaram Jahani,Katharina Boguslawski,Paweł Tecmer","doi":"10.1021/acs.jctc.5c01922","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c01922","url":null,"abstract":"We introduce a mean-field-like computational model for calculating ionization potentials (IPs) based on the pair Coupled Cluster Doubles (pCCD) wave function. Specifically, our model combines the extended Koopmans' theorem (EKT) with the advantages of a variationally orbital-optimized (oo)-pCCD ansatz. The computational cost of the EKT(pCCD) method is negligible (O(N3)) as the response 1- and 2-particle reduced density matrices used to construct the generalized Fock matrix are readily available after an oo-pCCD calculation. We benchmarked our new computational model for IPs of atoms, small molecules, and a set of organic acceptor molecules against experimental and theoretical reference data. The EKT(pCCD) model significantly improves upon the modified Koopmans' approach [J. Chem. Phys. 162, 184110 (2025)], and the obtained IPs are comparable to those of computationally more expensive IP-EOM-pCCD-based models, approaching CCSD(T) reference values (with a mean error of 0.05 eV). Most importantly, the EKT(pCCD) approach is almost independent of the basis set size, and reliable IPs are already obtained with small basis sets.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"106 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147439431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-12DOI: 10.1021/acs.jctc.5c02070
Diksha Dewan,Yifei Wang,Alfonso De Simone,David J Wales
Most biomolecular simulations depend on the quality of empirical force fields, and the use of hybrid restraint potentials has emerged as a promising approach. In this contribution, we extend the application of hybrid potentials to membrane proteins by developing optimized restraints derived from experimentally determined NMR data. NMR chemical shift, chemical shift anisotropy, dipolar coupling, and NOE distance information are combined with appropriately weighted empirical force fields to study two transmembrane systems, namely sarcolipin and phospholamban. To remedy the problems of rare events and broken ergodicity, the energy landscape framework, including basin-hopping global optimization and discrete path sampling, is employed for exploring the underlying energy landscapes. Much of the appeal of the hybrid potential approach is the ability to study membrane proteins in the absence of conventional explicit or implicit solvent and lipid molecules, thereby simplifying the sampling of complex biomolecular conformational spaces. Our results suggest that the hybridization of NMR constraints as penalty energies with empirical force fields improves global optimization and energy landscape analysis by excluding experimentally incompatible structures.
{"title":"Energy Landscape Analysis of Membrane Proteins Using NMR-Based Hybrid Restraint Potentials.","authors":"Diksha Dewan,Yifei Wang,Alfonso De Simone,David J Wales","doi":"10.1021/acs.jctc.5c02070","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c02070","url":null,"abstract":"Most biomolecular simulations depend on the quality of empirical force fields, and the use of hybrid restraint potentials has emerged as a promising approach. In this contribution, we extend the application of hybrid potentials to membrane proteins by developing optimized restraints derived from experimentally determined NMR data. NMR chemical shift, chemical shift anisotropy, dipolar coupling, and NOE distance information are combined with appropriately weighted empirical force fields to study two transmembrane systems, namely sarcolipin and phospholamban. To remedy the problems of rare events and broken ergodicity, the energy landscape framework, including basin-hopping global optimization and discrete path sampling, is employed for exploring the underlying energy landscapes. Much of the appeal of the hybrid potential approach is the ability to study membrane proteins in the absence of conventional explicit or implicit solvent and lipid molecules, thereby simplifying the sampling of complex biomolecular conformational spaces. Our results suggest that the hybridization of NMR constraints as penalty energies with empirical force fields improves global optimization and energy landscape analysis by excluding experimentally incompatible structures.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"56 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147439435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-12DOI: 10.1021/acs.jctc.5c01862
Nomindari Bayaraa,Maxim Secor,Marc L Descoteaux,Yu-Shan Lin
Cyclic peptides are an emerging therapeutic modality, with recent computational efforts focusing on the design of cyclic peptides that predominantly adopt a single conformation. However, many cyclic peptides adopt multiple conformations in solution, existing as structural ensembles. This conformational flexibility is often integral to their function: chameleonic switching between alternative states can enhance membrane permeability, and specific conformations may be required for molecular recognition and binding. Consequently, the ability to predict their structural ensembles is crucial for advancing the de novo design of cyclic peptide therapeutics. Here, we introduce diffusion models to efficiently and accurately predict structural ensembles of mixed-chirality cyclic peptides. The models are trained directly on molecular dynamics (MD) simulation data; in particular, each frame of the simulation becomes a single training instance in which a structure is represented as sine and cosine values of backbone dihedral angles. The trained diffusion model can not only generate MD-quality structures of cyclic peptides, but also the generated structures follow the Boltzmann distribution sampled in the MD simulation, enabling a deeper understanding of the physicochemical basis of cyclic peptide properties and allowing efficient computational design of cyclic peptides targeting biologically relevant systems.
{"title":"Fast Generation of Simulation-Quality Structural Ensembles of Mixed-Chirality Cyclic Peptides via Diffusion Models.","authors":"Nomindari Bayaraa,Maxim Secor,Marc L Descoteaux,Yu-Shan Lin","doi":"10.1021/acs.jctc.5c01862","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c01862","url":null,"abstract":"Cyclic peptides are an emerging therapeutic modality, with recent computational efforts focusing on the design of cyclic peptides that predominantly adopt a single conformation. However, many cyclic peptides adopt multiple conformations in solution, existing as structural ensembles. This conformational flexibility is often integral to their function: chameleonic switching between alternative states can enhance membrane permeability, and specific conformations may be required for molecular recognition and binding. Consequently, the ability to predict their structural ensembles is crucial for advancing the de novo design of cyclic peptide therapeutics. Here, we introduce diffusion models to efficiently and accurately predict structural ensembles of mixed-chirality cyclic peptides. The models are trained directly on molecular dynamics (MD) simulation data; in particular, each frame of the simulation becomes a single training instance in which a structure is represented as sine and cosine values of backbone dihedral angles. The trained diffusion model can not only generate MD-quality structures of cyclic peptides, but also the generated structures follow the Boltzmann distribution sampled in the MD simulation, enabling a deeper understanding of the physicochemical basis of cyclic peptide properties and allowing efficient computational design of cyclic peptides targeting biologically relevant systems.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"5 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147439434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We present a software to calculate phase-resolved resonant vibrational sum-frequency generation (vSFG) susceptibility χ(2)(ω) of water and hydroxyls at planar interfaces, e.g., air/water or solid/liquid or (bio)membrane/liquid interfaces of aqueous solutions. The released code (i) reads several formats of molecular trajectories, both from ab initio (AIMD) and classical MD (CMD), (ii) calculates instantaneous surfaces to allow flexible interfaces, (iii) is written in FORTRAN, parallelized by OpenMP and optimized for memory usage, (iv) allows processing of systems up of tens of thousand atoms and for unlimited simulation time, and (v) includes many tunable processing parameters. The code and its documentation are available via GitHub. Flexible models of water and surface hydroxyl (if evaluated) (CMD or AIMD) must be used. The derivatives of the polarizability tensors and dipole moments with the change of O-H distance must be calculated externally by ab initio methods and provided as input data. We present the impact of various parameters of the MD simulations (simulation length, nonbonded interaction cutoff, size of the system, and thermostat relaxation time) as well as of the processing code (filter relaxation, cutoff of cross-terms) and provide representative results for air/water, charged quartz (101)/aqueous solution, and neutral α-alumina (0001)/aqueous solution interfaces. Further extensions are planned to distinguish signals from specific O-H or C-H bonds of interfacial molecules.
{"title":"Large-Scale Calculation of Vibrational Sum Frequency Generation Spectra of Aqueous Interfaces.","authors":"Patrik Musil,Ondřej Kroutil,Simone Pezzotti,Marie-Pierre Gaigeot,Milan Předota","doi":"10.1021/acs.jctc.5c02160","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c02160","url":null,"abstract":"We present a software to calculate phase-resolved resonant vibrational sum-frequency generation (vSFG) susceptibility χ(2)(ω) of water and hydroxyls at planar interfaces, e.g., air/water or solid/liquid or (bio)membrane/liquid interfaces of aqueous solutions. The released code (i) reads several formats of molecular trajectories, both from ab initio (AIMD) and classical MD (CMD), (ii) calculates instantaneous surfaces to allow flexible interfaces, (iii) is written in FORTRAN, parallelized by OpenMP and optimized for memory usage, (iv) allows processing of systems up of tens of thousand atoms and for unlimited simulation time, and (v) includes many tunable processing parameters. The code and its documentation are available via GitHub. Flexible models of water and surface hydroxyl (if evaluated) (CMD or AIMD) must be used. The derivatives of the polarizability tensors and dipole moments with the change of O-H distance must be calculated externally by ab initio methods and provided as input data. We present the impact of various parameters of the MD simulations (simulation length, nonbonded interaction cutoff, size of the system, and thermostat relaxation time) as well as of the processing code (filter relaxation, cutoff of cross-terms) and provide representative results for air/water, charged quartz (101)/aqueous solution, and neutral α-alumina (0001)/aqueous solution interfaces. Further extensions are planned to distinguish signals from specific O-H or C-H bonds of interfacial molecules.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"234 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147439436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-11DOI: 10.1021/acs.jctc.6c00158
Pei-Kun Yang
Structure-based virtual screening is fundamentally constrained by the combinatorial growth of configurational spaces arising from receptor conformations, ligand identities, conformations, and spatial degrees of freedom. We reformulate protein-ligand interaction energy calculation as a linear-algebraic problem defined on shared Cartesian grids. Within this framework, electrostatic and van der Waals interaction energies are expressed as inner products between receptor potential maps and ligand charge and atom-type occupancy vectors. Ligand translations and rotations are represented as unitary operations acting on independent spatial registers, enabling systematic reuse of grid information across large pose ensembles within a unified computational formulation while explicitly evaluating interaction energies for each receptor-ligand configuration via inner products. We implement inner-product estimation using the Hadamard test and validate the formulation through systematic comparisons with classical atom-based and map-based energy evaluations. Across multiple receptor-ligand systems, we demonstrate that the proposed representation preserves energetic ordering in the low-energy regime relevant to structure-based virtual screening, while remaining robust under finite-sampling conditions. By exposing the tensorized structure underlying interaction-energy evaluation, this work establishes a representation-level formulation for map-based virtual screening compatible with both classical and quantum computational paradigms.
{"title":"Quantum Inner Product Scoring with Grid-Based Maps for Structure-Based Virtual Screening.","authors":"Pei-Kun Yang","doi":"10.1021/acs.jctc.6c00158","DOIUrl":"https://doi.org/10.1021/acs.jctc.6c00158","url":null,"abstract":"Structure-based virtual screening is fundamentally constrained by the combinatorial growth of configurational spaces arising from receptor conformations, ligand identities, conformations, and spatial degrees of freedom. We reformulate protein-ligand interaction energy calculation as a linear-algebraic problem defined on shared Cartesian grids. Within this framework, electrostatic and van der Waals interaction energies are expressed as inner products between receptor potential maps and ligand charge and atom-type occupancy vectors. Ligand translations and rotations are represented as unitary operations acting on independent spatial registers, enabling systematic reuse of grid information across large pose ensembles within a unified computational formulation while explicitly evaluating interaction energies for each receptor-ligand configuration via inner products. We implement inner-product estimation using the Hadamard test and validate the formulation through systematic comparisons with classical atom-based and map-based energy evaluations. Across multiple receptor-ligand systems, we demonstrate that the proposed representation preserves energetic ordering in the low-energy regime relevant to structure-based virtual screening, while remaining robust under finite-sampling conditions. By exposing the tensorized structure underlying interaction-energy evaluation, this work establishes a representation-level formulation for map-based virtual screening compatible with both classical and quantum computational paradigms.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"7 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147393906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-10DOI: 10.1021/acs.jctc.5c02010
Prince Frederick Kwao,Srivathsan Poyyapakkam Sundar,Brajesh Gupt,Ayush Asthana
Solving challenging problems in quantum chemistry is one of the most promising applications of quantum computers. Within the quantum algorithms proposed for problems in excited-state quantum chemistry, subspace-based quantum algorithms, including quantum subspace expansion (QSE), quantum equation of motion (qEOM), and quantum self-consistent equation-of-motion (q-sc-EOM), are promising for pre-fault-tolerant quantum devices. The working equation of QSE and qEOM requires solving a generalized eigenvalue equation with associated matrix elements measured on a quantum computer. Our careful analytical and numerical analysis of the standard and generalized eigenvalue problems, especially in the context of excited-state methods, shows that the errors in eigenvalues magnify drastically with an increase in the condition number of the overlap matrix when a generalized eigenvalue equation is solved in the presence of statistical sampling errors. This makes methods such as QSE unstable for errors that are unavoidable when using quantum computers. Further, at very high condition numbers of the overlap matrix, the QSE’s working equation could not be solved without any additional steps in the presence of sampling errors, as it becomes ill-conditioned. It was possible to use the thresholding technique in this case to solve the equation, but the solutions achieved had missing excited states, which may be a problem for future chemical studies. We also show that excited-state methods that have an eigenvalue equation as the working equation, such as q-sc-EOM, do not have the problems associated with the condition number and could be generally more stable to errors and therefore more suitable candidates for excited-state quantum chemistry calculations using quantum computers.
{"title":"Generalized Eigenvalue Problem in Subspace-Based Excited-State Methods for Quantum Computers","authors":"Prince Frederick Kwao,Srivathsan Poyyapakkam Sundar,Brajesh Gupt,Ayush Asthana","doi":"10.1021/acs.jctc.5c02010","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c02010","url":null,"abstract":"Solving challenging problems in quantum chemistry is one of the most promising applications of quantum computers. Within the quantum algorithms proposed for problems in excited-state quantum chemistry, subspace-based quantum algorithms, including quantum subspace expansion (QSE), quantum equation of motion (qEOM), and quantum self-consistent equation-of-motion (q-sc-EOM), are promising for pre-fault-tolerant quantum devices. The working equation of QSE and qEOM requires solving a generalized eigenvalue equation with associated matrix elements measured on a quantum computer. Our careful analytical and numerical analysis of the standard and generalized eigenvalue problems, especially in the context of excited-state methods, shows that the errors in eigenvalues magnify drastically with an increase in the condition number of the overlap matrix when a generalized eigenvalue equation is solved in the presence of statistical sampling errors. This makes methods such as QSE unstable for errors that are unavoidable when using quantum computers. Further, at very high condition numbers of the overlap matrix, the QSE’s working equation could not be solved without any additional steps in the presence of sampling errors, as it becomes ill-conditioned. It was possible to use the thresholding technique in this case to solve the equation, but the solutions achieved had missing excited states, which may be a problem for future chemical studies. We also show that excited-state methods that have an eigenvalue equation as the working equation, such as q-sc-EOM, do not have the problems associated with the condition number and could be generally more stable to errors and therefore more suitable candidates for excited-state quantum chemistry calculations using quantum computers.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"45 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147383800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-09DOI: 10.1021/acs.jctc.5c01657
Craig Daniels,Nathan V Roberts,Kyungjoo Kim,Habib N Najm
Kinetic Monte Carlo (KMC) simulations are broadly used to investigate chemical and materials systems where a balance between atomic detail and diffusion or reaction time scales is needed. Here we present KinCat, an open-source 2D KMC package designed for use in lattice-KMC studies of surface kinetics in heterogeneous catalytic systems. It is written in C++ and uses Kokkos to facilitate use on a variety of shared-memory CPU/GPU/accelerator systems. We demonstrate the performance scaling of KinCat on GPU and CPU architectures, using CO oxidation on RuO2 as a model system. KinCat efficiently manages large lattice KMC simulations using a parallel domain-decomposition algorithm.
{"title":"KinCat: Kinetic Monte Carlo Parallel Computations of Surface Kinetics in Heterogeneous Catalysis.","authors":"Craig Daniels,Nathan V Roberts,Kyungjoo Kim,Habib N Najm","doi":"10.1021/acs.jctc.5c01657","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c01657","url":null,"abstract":"Kinetic Monte Carlo (KMC) simulations are broadly used to investigate chemical and materials systems where a balance between atomic detail and diffusion or reaction time scales is needed. Here we present KinCat, an open-source 2D KMC package designed for use in lattice-KMC studies of surface kinetics in heterogeneous catalytic systems. It is written in C++ and uses Kokkos to facilitate use on a variety of shared-memory CPU/GPU/accelerator systems. We demonstrate the performance scaling of KinCat on GPU and CPU architectures, using CO oxidation on RuO2 as a model system. KinCat efficiently manages large lattice KMC simulations using a parallel domain-decomposition algorithm.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"15 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147373846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-09DOI: 10.1021/acs.jctc.5c01939
Jingcheng Dai,Atharva Vidwans,Eric H. Wan,Alexander X. Miller,Micheline B. Soley
Recent advancements in quantum algorithms have reached a state where we can consider how to capitalize on quantum and classical computational resources to accelerate molecular resonance state identification. Here, we identify molecular resonances with a method that combines quantum computing with classical high-throughput computing (HTC). This algorithm, which we term qDRIVE (the quantum deflation resonance identification variational eigensolver), exploits the complex absorbing potential formalism to distill the problem of molecular resonance identification into a network of hybrid quantum-classical variational quantum eigensolver tasks and harnesses HTC resources to execute these interconnected but independent tasks both asynchronously and in parallel, a strategy that minimizes wall time to completion. We show qDRIVE successfully identifies resonance energies and wave functions in simulated quantum processors with current and planned specifications, which bodes well for qDRIVE’s ultimate application in disciplines ranging from photocatalysis to quantum control and places a spotlight on the potential offered by integrated heterogeneous quantum computing/HTC approaches in computational chemistry.
{"title":"Molecular Resonance Identification in Complex Absorbing Potentials via Integrated Quantum Computing and High-Throughput Computing","authors":"Jingcheng Dai,Atharva Vidwans,Eric H. Wan,Alexander X. Miller,Micheline B. Soley","doi":"10.1021/acs.jctc.5c01939","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c01939","url":null,"abstract":"Recent advancements in quantum algorithms have reached a state where we can consider how to capitalize on quantum and classical computational resources to accelerate molecular resonance state identification. Here, we identify molecular resonances with a method that combines quantum computing with classical high-throughput computing (HTC). This algorithm, which we term qDRIVE (the quantum deflation resonance identification variational eigensolver), exploits the complex absorbing potential formalism to distill the problem of molecular resonance identification into a network of hybrid quantum-classical variational quantum eigensolver tasks and harnesses HTC resources to execute these interconnected but independent tasks both asynchronously and in parallel, a strategy that minimizes wall time to completion. We show qDRIVE successfully identifies resonance energies and wave functions in simulated quantum processors with current and planned specifications, which bodes well for qDRIVE’s ultimate application in disciplines ranging from photocatalysis to quantum control and places a spotlight on the potential offered by integrated heterogeneous quantum computing/HTC approaches in computational chemistry.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"79 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147383801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-09DOI: 10.1021/acs.jctc.5c01852
Lexin Ding,Markus Reiher
Neural quantum states (NQS) are a promising ansatz for solving many-body quantum problems due to their inherent expressiveness. Yet this expressiveness can only be harnessed efficiently for treating identical particles if the suitable physical knowledge is hardwired into the neural network itself. For electronic structure, NQS based on backflow determinants have been shown to be a powerful ansatz for capturing strong correlation. By contrast, the analogue for bosons, backflow permanents, is unpractical due to the steep cost of computing the matrix permanent and due to the lack of particle conservation in common bosonic problems. To circumvent these obstacles, we introduce a modal backflow (MBF) NQS design and demonstrate its efficacy by solving the anharmonic vibrational problem. To accommodate the demand of high accuracy in spectroscopic calculations, we implement a selected-configuration scheme for evaluating physical observables and gradients, replacing the standard stochastic approach based on Monte Carlo sampling. A vibrational self-consistent field calculation is conveniently carried out within the MBF network, which serves as a pretraining step to accelerate and stabilize the optimization. In applications to both artificial and ab initio Hamiltonians, we find that the MBF network is capable of delivering spectroscopically accurate zero-point energies and vibrational transitions in all anharmonic regimes.
{"title":"Modal Backflow Neural Quantum States for Anharmonic Vibrational Calculations","authors":"Lexin Ding,Markus Reiher","doi":"10.1021/acs.jctc.5c01852","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c01852","url":null,"abstract":"Neural quantum states (NQS) are a promising ansatz for solving many-body quantum problems due to their inherent expressiveness. Yet this expressiveness can only be harnessed efficiently for treating identical particles if the suitable physical knowledge is hardwired into the neural network itself. For electronic structure, NQS based on backflow determinants have been shown to be a powerful ansatz for capturing strong correlation. By contrast, the analogue for bosons, backflow permanents, is unpractical due to the steep cost of computing the matrix permanent and due to the lack of particle conservation in common bosonic problems. To circumvent these obstacles, we introduce a modal backflow (MBF) NQS design and demonstrate its efficacy by solving the anharmonic vibrational problem. To accommodate the demand of high accuracy in spectroscopic calculations, we implement a selected-configuration scheme for evaluating physical observables and gradients, replacing the standard stochastic approach based on Monte Carlo sampling. A vibrational self-consistent field calculation is conveniently carried out within the MBF network, which serves as a pretraining step to accelerate and stabilize the optimization. In applications to both artificial and ab initio Hamiltonians, we find that the MBF network is capable of delivering spectroscopically accurate zero-point energies and vibrational transitions in all anharmonic regimes.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"28 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147383802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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