Pub Date : 2026-02-05DOI: 10.1021/acs.jctc.5c01828
Viktor Zaverkin, Matheus Ferraz, Francesco Alesiani, Mathias Niepert
Universal machine-learned potentials promise transferable accuracy across compositional and vibrational degrees of freedom, yet their application to biomolecular simulations remains underexplored. This work systematically evaluates equivariant message-passing architectures trained on the SPICE-v2 data set with and without explicit long-range dispersion and electrostatics. We assess the impact of model size, training data composition, and electrostatic treatment across in- and out-of-distribution benchmark data sets, as well as molecular simulations of bulk liquid water, aqueous NaCl solutions, and biomolecules, including alanine tripeptide, the mini-protein Trp-cage, and Crambin. While larger models improve accuracy on benchmark data sets, this trend does not consistently extend to properties obtained from simulations. Predicted properties also depend on the composition of the training data set. Long-range electrostatics show no systematic impact across systems. However, for Trp-cage, their inclusion yields increased conformational variability. Our results suggest that imbalanced data sets and immature evaluation practices currently challenge the applicability of universal machine-learned potentials to biomolecular simulations.
{"title":"Performance of Universal Machine-Learned Potentials with Explicit Long-Range Interactions in Biomolecular Simulations.","authors":"Viktor Zaverkin, Matheus Ferraz, Francesco Alesiani, Mathias Niepert","doi":"10.1021/acs.jctc.5c01828","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c01828","url":null,"abstract":"<p><p>Universal machine-learned potentials promise transferable accuracy across compositional and vibrational degrees of freedom, yet their application to biomolecular simulations remains underexplored. This work systematically evaluates equivariant message-passing architectures trained on the SPICE-v2 data set with and without explicit long-range dispersion and electrostatics. We assess the impact of model size, training data composition, and electrostatic treatment across in- and out-of-distribution benchmark data sets, as well as molecular simulations of bulk liquid water, aqueous NaCl solutions, and biomolecules, including alanine tripeptide, the mini-protein Trp-cage, and Crambin. While larger models improve accuracy on benchmark data sets, this trend does not consistently extend to properties obtained from simulations. Predicted properties also depend on the composition of the training data set. Long-range electrostatics show no systematic impact across systems. However, for Trp-cage, their inclusion yields increased conformational variability. Our results suggest that imbalanced data sets and immature evaluation practices currently challenge the applicability of universal machine-learned potentials to biomolecular simulations.</p>","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146123205","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 : 2026-02-05DOI: 10.1021/acs.jctc.5c01353
Theo Juncker von Buchwald, Erik Rosendahl Kjellgren, Jacob Kongsted, Stephan P A Sauer, Sonia Coriani, Karl Michael Ziems
Linear response (LR) is an important tool in the computational chemist's toolbox. It is therefore unsurprising that the emergence of quantum computers has led to a quantum counterpart known as quantum LR (qLR). However, the current quantum era of near-term intermediate-scale quantum (NISQ) computers is dominated by noise, short decoherence times, and slow measurement speeds. It is therefore of interest to find approximations that can greatly reduce the quantum workload while only slightly impacting the quality of a method. In an effort to achieve this, we approximate the naive qLR with the singles and doubles (qLRSD) method, by either directly approximating the reduced density matrices (RDMs) or indirectly through their respective reduced density cumulants (RDCs). We present an analysis of the measurement costs associated with qLR using RDMs and report qLR results for model hydrogen ladder systems; for varying active space sizes in OCS, SeH2, and H2S; and for symmetrically stretched H2O and BeH2. Discouragingly, while approximations to the 4-body RDMs and RDCs seem to produce good results for systems at the equilibrium geometry and for some types of core excitations, they both tend to fail when the system exhibits strong correlation. All approximations to the 3-body RDMs and/or RDCs severely affect the results and cannot be applied.
{"title":"Reduced Density Matrix and Cumulant Approximations of Quantum Linear Response.","authors":"Theo Juncker von Buchwald, Erik Rosendahl Kjellgren, Jacob Kongsted, Stephan P A Sauer, Sonia Coriani, Karl Michael Ziems","doi":"10.1021/acs.jctc.5c01353","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c01353","url":null,"abstract":"<p><p>Linear response (LR) is an important tool in the computational chemist's toolbox. It is therefore unsurprising that the emergence of quantum computers has led to a quantum counterpart known as quantum LR (qLR). However, the current quantum era of near-term intermediate-scale quantum (NISQ) computers is dominated by noise, short decoherence times, and slow measurement speeds. It is therefore of interest to find approximations that can greatly reduce the quantum workload while only slightly impacting the quality of a method. In an effort to achieve this, we approximate the naive qLR with the singles and doubles (qLRSD) method, by either directly approximating the reduced density matrices (RDMs) or indirectly through their respective reduced density cumulants (RDCs). We present an analysis of the measurement costs associated with qLR using RDMs and report qLR results for model hydrogen ladder systems; for varying active space sizes in OCS, SeH<sub>2</sub>, and H<sub>2</sub>S; and for symmetrically stretched H<sub>2</sub>O and BeH<sub>2</sub>. Discouragingly, while approximations to the 4-body RDMs and RDCs seem to produce good results for systems at the equilibrium geometry and for some types of core excitations, they both tend to fail when the system exhibits strong correlation. All approximations to the 3-body RDMs and/or RDCs severely affect the results and cannot be applied.</p>","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146117165","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 : 2026-02-04DOI: 10.1021/acs.jctc.5c01797
Eric V. Anslyn,Dmitrii E. Makarov
Cell adhesion, molecular recognition, biomolecular folding and unfolding, dynamic cross-linking in soft materials, and many other phenomena involve formation or dissociation of multiple chemical bonds. Here, we study the overall time scale required to break or form N bonds. Strictly speaking, this time scale depends on the initial conditions, e.g., the number and which bonds are formed/broken, and its estimation requires kinetic details about forming and breaking of each individual bond influenced by the larger network of other bonds. We show, however, that a simple estimate, analogous to transition-state theory in chemical kinetics, accurately predicts the mean first passage time to form or break all the bonds in terms of single-bond properties and thermodynamic properties of the network. As the thermodynamics of bond networks can often be described by well-studied statistical-mechanical models, such as the Ising model and its extensions, our theory provides a link between the global dynamics and thermodynamics of multibond arrays and networks.
{"title":"Transition State Theory for Dissociation of Dynamic Bonding Networks","authors":"Eric V. Anslyn,Dmitrii E. Makarov","doi":"10.1021/acs.jctc.5c01797","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c01797","url":null,"abstract":"Cell adhesion, molecular recognition, biomolecular folding and unfolding, dynamic cross-linking in soft materials, and many other phenomena involve formation or dissociation of multiple chemical bonds. Here, we study the overall time scale required to break or form N bonds. Strictly speaking, this time scale depends on the initial conditions, e.g., the number and which bonds are formed/broken, and its estimation requires kinetic details about forming and breaking of each individual bond influenced by the larger network of other bonds. We show, however, that a simple estimate, analogous to transition-state theory in chemical kinetics, accurately predicts the mean first passage time to form or break all the bonds in terms of single-bond properties and thermodynamic properties of the network. As the thermodynamics of bond networks can often be described by well-studied statistical-mechanical models, such as the Ising model and its extensions, our theory provides a link between the global dynamics and thermodynamics of multibond arrays and networks.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"88 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146111077","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 : 2026-02-04DOI: 10.1021/acs.jctc.5c02063
Esteban D. Gadea,Shakkira Erimban,Ignacio J. Bombau,Damian A. Scherlis,John J. Karnes,Valeria Molinero
Kinetic Monte Carlo (kMC) simulations, augmented with temporal-acceleration schemes, can efficiently handle stiff reaction-transport networks when fast processes rapidly relax to quasi-equilibrium on a fixed lattice. However, in glassy anion-exchange membranes (AEM), rare and irreversible chemical degradation events continuously reshape the nanoscale morphology, and the associated hydration and transport degrees of freedom remain far from a well-defined local equilibrium. This combination of evolving state space and nonequilibrated fast dynamics lies outside the scope of existing kMC acceleration frameworks. To address this challenge, we introduce an auxiliary-particle kinetic Monte Carlo (AP-kMC) scheme. In AP-kMC, short-lived mobile particles spawned at degradation sites execute hop, water-elimination, and decay moves, enforcing rapid local relaxation of the hydration structure while preserving the stochastic rules of kMC. Parameterized with molecular-dynamics morphologies and experimental solution degradation kinetics, AP-kMC reproduces the evolution of ion-exchange capacity, water uptake, and conductivity, and reveals a feedback loop in which poorly hydrated sites degrade first and each degradation event induces further local dehydration. The resulting thinning and fragmentation of water channels cause loss of hydrophilic percolation and abrupt conductivity collapse well before complete charge loss. AP-kMC thus reframes AEM durability as a coupled degradation–drying–percolation problem and provides a transferable strategy to simulate reactive, out-of-equilibrium polymer electrolytes where local solvation controls reactivity.
{"title":"Kinetic Monte Carlo Framework for Coupled Degradation and Dehydration of Anion Exchange Membranes","authors":"Esteban D. Gadea,Shakkira Erimban,Ignacio J. Bombau,Damian A. Scherlis,John J. Karnes,Valeria Molinero","doi":"10.1021/acs.jctc.5c02063","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c02063","url":null,"abstract":"Kinetic Monte Carlo (kMC) simulations, augmented with temporal-acceleration schemes, can efficiently handle stiff reaction-transport networks when fast processes rapidly relax to quasi-equilibrium on a fixed lattice. However, in glassy anion-exchange membranes (AEM), rare and irreversible chemical degradation events continuously reshape the nanoscale morphology, and the associated hydration and transport degrees of freedom remain far from a well-defined local equilibrium. This combination of evolving state space and nonequilibrated fast dynamics lies outside the scope of existing kMC acceleration frameworks. To address this challenge, we introduce an auxiliary-particle kinetic Monte Carlo (AP-kMC) scheme. In AP-kMC, short-lived mobile particles spawned at degradation sites execute hop, water-elimination, and decay moves, enforcing rapid local relaxation of the hydration structure while preserving the stochastic rules of kMC. Parameterized with molecular-dynamics morphologies and experimental solution degradation kinetics, AP-kMC reproduces the evolution of ion-exchange capacity, water uptake, and conductivity, and reveals a feedback loop in which poorly hydrated sites degrade first and each degradation event induces further local dehydration. The resulting thinning and fragmentation of water channels cause loss of hydrophilic percolation and abrupt conductivity collapse well before complete charge loss. AP-kMC thus reframes AEM durability as a coupled degradation–drying–percolation problem and provides a transferable strategy to simulate reactive, out-of-equilibrium polymer electrolytes where local solvation controls reactivity.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"8 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146111136","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 : 2026-02-04DOI: 10.1021/acs.jctc.5c01872
Pei-Kun Yang
In structure-based virtual screening, evaluating the binding free energy of protein–ligand complexes requires accounting for both molecular conformations and spatial transformations, such as shifts and rotations, which can lead to an exponential increase in possible configurations. Classical computing approaches are limited in handling this combinatorial explosion, whereas quantum computing offers a promising alternative due to its inherent parallelism. In this study, we propose a quantum machine learning framework that encodes molecular features into quantum states and processes them through parametrized quantum gates, with all architectural and representational choices deliberately guided by near-term hardware feasibility and an explicit focus on minimal qubit counts, shallow circuit depth, and compact input representations. The model is implemented and optimized in PyTorch, and its predictive performance is examined under three conditions: ideal simulation, finite-shot sampling, and quantum-noise simulation. With six quantum circuit units, the model achieves a root-mean-square deviation of 2.37 kcal/mol and a Pearson correlation coefficient of 0.650. The predictions remain stable with 100,000 measurement shots, demonstrating compatibility with near-term quantum hardware. Although the introduction of noise slightly reduces absolute accuracy, the Pearson correlation coefficient remains stable, indicating that the ranking of ligand affinities is preserved. These results highlight a practical, scalable quantum approach that balances predictive power and robustness, providing a feasible pathway to accelerate virtual screening using moderately deep quantum circuits.
{"title":"A Hardware-Feasible Quantum Machine Learning Framework for Structure-Based Virtual Screening","authors":"Pei-Kun Yang","doi":"10.1021/acs.jctc.5c01872","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c01872","url":null,"abstract":"In structure-based virtual screening, evaluating the binding free energy of protein–ligand complexes requires accounting for both molecular conformations and spatial transformations, such as shifts and rotations, which can lead to an exponential increase in possible configurations. Classical computing approaches are limited in handling this combinatorial explosion, whereas quantum computing offers a promising alternative due to its inherent parallelism. In this study, we propose a quantum machine learning framework that encodes molecular features into quantum states and processes them through parametrized quantum gates, with all architectural and representational choices deliberately guided by near-term hardware feasibility and an explicit focus on minimal qubit counts, shallow circuit depth, and compact input representations. The model is implemented and optimized in PyTorch, and its predictive performance is examined under three conditions: ideal simulation, finite-shot sampling, and quantum-noise simulation. With six quantum circuit units, the model achieves a root-mean-square deviation of 2.37 kcal/mol and a Pearson correlation coefficient of 0.650. The predictions remain stable with 100,000 measurement shots, demonstrating compatibility with near-term quantum hardware. Although the introduction of noise slightly reduces absolute accuracy, the Pearson correlation coefficient remains stable, indicating that the ranking of ligand affinities is preserved. These results highlight a practical, scalable quantum approach that balances predictive power and robustness, providing a feasible pathway to accelerate virtual screening using moderately deep quantum circuits.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"1 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146111083","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 : 2026-02-04DOI: 10.1021/acs.jctc.5c01849
Robert A. Lang,Shashank G. Mehendale,Ilya G. Ryabinkin,Artur F. Izmaylov
We introduce the multistate iterative qubit coupled cluster (MS-iQCC) method, a quantum-inspired algorithm that runs efficiently on classical hardware and is designed to predict both ground and excited electronic states of molecules. Accurate excited-state energetics are essential for interpreting spectroscopy and chemical reactivity, but standard electronic structure methods are either too computationally expensive for larger systems or lose reliability in the presence of strong electron correlation. MS-iQCC addresses this challenge by simultaneously optimizing multiple electronic states in a single, state-averaged procedure that treats ground and excited states on equal footing. This removes the energetic bias that is introduced when excited states are computed one at a time and constrained to remain orthogonal to previously optimized states. The approach supports multireference electronic structure by allowing multideterminantal initial guesses and by adaptively building a compact exponential ansatz from a pool of qubit excitation generators. We apply MS-iQCC to H4, H2O, N2, and C2, including strongly correlated geometries, and observe robust convergence of all targeted state energies to chemically meaningful accuracy across their potential energy surfaces.
{"title":"Multistate Iterative Qubit Coupled Cluster (MS-iQCC): A Quantum-Inspired, State-Averaged Approach to Ground- And Excited-State Energies","authors":"Robert A. Lang,Shashank G. Mehendale,Ilya G. Ryabinkin,Artur F. Izmaylov","doi":"10.1021/acs.jctc.5c01849","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c01849","url":null,"abstract":"We introduce the multistate iterative qubit coupled cluster (MS-iQCC) method, a quantum-inspired algorithm that runs efficiently on classical hardware and is designed to predict both ground and excited electronic states of molecules. Accurate excited-state energetics are essential for interpreting spectroscopy and chemical reactivity, but standard electronic structure methods are either too computationally expensive for larger systems or lose reliability in the presence of strong electron correlation. MS-iQCC addresses this challenge by simultaneously optimizing multiple electronic states in a single, state-averaged procedure that treats ground and excited states on equal footing. This removes the energetic bias that is introduced when excited states are computed one at a time and constrained to remain orthogonal to previously optimized states. The approach supports multireference electronic structure by allowing multideterminantal initial guesses and by adaptively building a compact exponential ansatz from a pool of qubit excitation generators. We apply MS-iQCC to H4, H2O, N2, and C2, including strongly correlated geometries, and observe robust convergence of all targeted state energies to chemically meaningful accuracy across their potential energy surfaces.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"295 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146111078","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 : 2026-02-04DOI: 10.1021/acs.jctc.5c01269
Malte Schäffner,Colin A. Smith,Robert Tampé,Helmut Grubmüller
The ATPase ABCE1, a member of the ubiquitous ATP-Binding Cassette protein superfamily, is essential in eukaryotic and archaeal ribosome recycling. It comprises a pair of homologous nucleotide-binding domains (NBDs), each containing a consensus nucleotide-binding site (NBS), where ATP hydrolysis takes place. Each of these sites can be in either an open or closed conformation. Despite the near symmetry of the two NBDs, and quite unexpectedly, their hydrolysis kinetics are highly asymmetric. While substitution of the catalytic glutamate (E238Q) in NBSI reduced the overall turnover rate of the ATPase by a factor of 2, as one might expect, the corresponding substitution in NBSII (E485Q) shows a so far unexplained 10-fold increase. To address this issue, we used Markov models to study how such a drastic asymmetry can arise. Specifically, we asked whether this observation can be explained without previously proposed direct allosteric interactions, such as electrostatic interactions, between the two NBSs. Indeed, using a Bayesian approach, we found Markov models that quantitatively predict the experimentally observed kinetics, as well as additional steady-state ATP occupancy data, both without such direct allosteric interaction. In particular, our results show that the observed remarkable asymmetry is fully explained by the structure-induced property that opening and closing always involves both NBSs. These models can explain the unexpected fast kinetics of the mutant of NBSII in terms of a drastic population shift due to the mutation, which circumvents a kinetic trap state that slows wild-type kinetics. Our Bayesian Markov approach may help to quantitatively explain similar nonintuitive Braess-type kinetics also in other enzymes where chemical/conformation coupling is essential.
{"title":"Braess’ Paradox in Enzyme Kinetics: Asymmetry from Population Balance without Direct Cooperativity","authors":"Malte Schäffner,Colin A. Smith,Robert Tampé,Helmut Grubmüller","doi":"10.1021/acs.jctc.5c01269","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c01269","url":null,"abstract":"The ATPase ABCE1, a member of the ubiquitous ATP-Binding Cassette protein superfamily, is essential in eukaryotic and archaeal ribosome recycling. It comprises a pair of homologous nucleotide-binding domains (NBDs), each containing a consensus nucleotide-binding site (NBS), where ATP hydrolysis takes place. Each of these sites can be in either an open or closed conformation. Despite the near symmetry of the two NBDs, and quite unexpectedly, their hydrolysis kinetics are highly asymmetric. While substitution of the catalytic glutamate (E238Q) in NBSI reduced the overall turnover rate of the ATPase by a factor of 2, as one might expect, the corresponding substitution in NBSII (E485Q) shows a so far unexplained 10-fold increase. To address this issue, we used Markov models to study how such a drastic asymmetry can arise. Specifically, we asked whether this observation can be explained without previously proposed direct allosteric interactions, such as electrostatic interactions, between the two NBSs. Indeed, using a Bayesian approach, we found Markov models that quantitatively predict the experimentally observed kinetics, as well as additional steady-state ATP occupancy data, both without such direct allosteric interaction. In particular, our results show that the observed remarkable asymmetry is fully explained by the structure-induced property that opening and closing always involves both NBSs. These models can explain the unexpected fast kinetics of the mutant of NBSII in terms of a drastic population shift due to the mutation, which circumvents a kinetic trap state that slows wild-type kinetics. Our Bayesian Markov approach may help to quantitatively explain similar nonintuitive Braess-type kinetics also in other enzymes where chemical/conformation coupling is essential.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"9 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146111082","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}
The study explores how well machine learning and structural fingerprints can predict spectroscopic properties of ice (OH vibrational frequencies and 1H chemical shifts). A large theoretical data set (55 ice polymorphs, 1010 DFT data points both for the vibrations and for the NMR shifts) and a smaller cross-validation set are employed. The Message Passing Atomic Cluster Expansion (MACE) model performs the best, with high accuracy (root-mean-square deviation, RMSD, of 0.06 ppm for chemical shifts and ∼10 cm–1 for vibrational frequencies). Simpler descriptors like ACSF and SOAP, when paired with suitable regressors, nearly match MACE’s performance. At the other end of the complexity scale, it is found that using the simplest possible physics-based descriptor of the environment (a single H-bond distance) yields RMSD values three times as large for the vibrations and four times as large for the proton chemical shift compared to the MACE model. Depending on the context, those RMSD values may still be considered modest and useful, considering the gain in simplicity and transparency.
{"title":"Machine-Learning Ice Spectra: From 1 to 256 Features","authors":"Shokirbek Shermukhamedov,Jolla Kullgren,Daniel Sethio,Kersti Hermansson","doi":"10.1021/acs.jctc.5c01413","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c01413","url":null,"abstract":"The study explores how well machine learning and structural fingerprints can predict spectroscopic properties of ice (OH vibrational frequencies and 1H chemical shifts). A large theoretical data set (55 ice polymorphs, 1010 DFT data points both for the vibrations and for the NMR shifts) and a smaller cross-validation set are employed. The Message Passing Atomic Cluster Expansion (MACE) model performs the best, with high accuracy (root-mean-square deviation, RMSD, of 0.06 ppm for chemical shifts and ∼10 cm–1 for vibrational frequencies). Simpler descriptors like ACSF and SOAP, when paired with suitable regressors, nearly match MACE’s performance. At the other end of the complexity scale, it is found that using the simplest possible physics-based descriptor of the environment (a single H-bond distance) yields RMSD values three times as large for the vibrations and four times as large for the proton chemical shift compared to the MACE model. Depending on the context, those RMSD values may still be considered modest and useful, considering the gain in simplicity and transparency.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"17 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146111079","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 : 2026-02-04DOI: 10.1021/acs.jctc.5c01892
Daniel F. Calero-Osorio,Paul W. Ayers
We show how to add the effects of residual electron correlation to a reference seniority-zero wave function by transforming the true electronic Hamiltonian into seniority-zero form. The transformation is treated via the Baker–Campbell–Hausdorff (BCH) expansion, and the seniority-zero structure of the reference is exploited to evaluate the first three commutators exactly; the remaining contributions are handled with a recursive commutator approximation, as is typical in canonical transformation methods. By choosing a seniority-zero reference and using parallel computation, this method is practical for small- to medium-sized systems. Numerical tests show high accuracy, with errors ∼10–4 Hartree.
{"title":"Seniority-Zero Canonical Transformation Theory: Error Reduction via Late Truncation","authors":"Daniel F. Calero-Osorio,Paul W. Ayers","doi":"10.1021/acs.jctc.5c01892","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c01892","url":null,"abstract":"We show how to add the effects of residual electron correlation to a reference seniority-zero wave function by transforming the true electronic Hamiltonian into seniority-zero form. The transformation is treated via the Baker–Campbell–Hausdorff (BCH) expansion, and the seniority-zero structure of the reference is exploited to evaluate the first three commutators exactly; the remaining contributions are handled with a recursive commutator approximation, as is typical in canonical transformation methods. By choosing a seniority-zero reference and using parallel computation, this method is practical for small- to medium-sized systems. Numerical tests show high accuracy, with errors ∼10–4 Hartree.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"8 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146111081","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 : 2026-02-04DOI: 10.1021/acs.jctc.5c01817
Arnab Bachhar,Nicholas J. Mayhall
Transition metal complexes present significant challenges for electronic structure theory due to strong electron correlation arising from partially filled d-orbitals. We compare our recently developed Tensor Product Selected Configuration Interaction (TPSCI) with Density Matrix Renormalization Group (DMRG) for computing exchange coupling constants in six transition metal systems, including dinuclear Cr, Fe, and Mn complexes and a tetranuclear Ni-cubane. TPSCI uses a locally correlated tensor product state basis to capture electronic structure efficiently while maintaining interpretability. From calculations on active spaces ranging from (22e,29o) to (42e,49o), we find that TPSCI consistently yields higher variational energies than DMRG due to truncation of local cluster states, but provides magnetic exchange coupling constants (J) generally within 10–30 cm–1 of DMRG results. Key advantages include natural multistate capability enabling direct J extrapolation with smaller statistical errors, and computational efficiency for challenging systems. However, cluster state truncation represents a fundamental limitation requiring careful convergence testing, particularly for large local cluster dimensions. We identify specific failure cases where current truncation schemes break down, highlighting the need for improved cluster state selection methods and distributed memory implementations to realize TPSCI’s full potential for strongly correlated systems.
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