Archana,Vaibhav Charde,Vijay Kumar,Anagha Ranade,Ajay K Meena,Narayanam Srikanth,Rabinarayan Acharya,Sairam S Mallajosyula
Renowned in traditional medicine for its wide-ranging therapeutic benefits, Curcuma longa exhibits a distinctive phytochemical signature dominated by curcuminoids and turmerones- two chemically diverse classes of compounds that collectively define its biological activity. These molecules possess unique structural and electronic features, such as conjugated π-systems and reactive functional groups, that challenge the accuracy of existing generalized force fields. Consequently, computational studies relying on non-specific parameters often fail to capture their subtle conformational preferences and interaction energetics. To address these limitations, this work presents the development of CHARMM-compatible all-atom force field parameters for the major phytochemicals of C. longa, enabling an accurate description of their structural, energetic, and interfacial properties. The parametrization protocol reproduces high-level quantum mechanical (QM) target data, including water-interaction energies, potential energy surface scans, and vibrational frequency calculations. The optimized parameters were rigorously validated through QM-MM geometry comparisons, crystal structure simulations, and protein-ligand molecular dynamics studies to assess accuracy, consistency, and transferability. The resulting parameter set, fully integrated within the CHARMM additive force field, facilitates reliable simulations of C. longa phytochemicals and their biomolecular interactions, thereby extending the applicability of CHARMM to complex natural product systems.
{"title":"CHARMM Force Field for Curcuma longa Phytochemicals: Towards Reliable Modeling of Curcuminoids and Turmerones in Biological Systems.","authors":" Archana,Vaibhav Charde,Vijay Kumar,Anagha Ranade,Ajay K Meena,Narayanam Srikanth,Rabinarayan Acharya,Sairam S Mallajosyula","doi":"10.1002/jcc.70351","DOIUrl":"https://doi.org/10.1002/jcc.70351","url":null,"abstract":"Renowned in traditional medicine for its wide-ranging therapeutic benefits, Curcuma longa exhibits a distinctive phytochemical signature dominated by curcuminoids and turmerones- two chemically diverse classes of compounds that collectively define its biological activity. These molecules possess unique structural and electronic features, such as conjugated π-systems and reactive functional groups, that challenge the accuracy of existing generalized force fields. Consequently, computational studies relying on non-specific parameters often fail to capture their subtle conformational preferences and interaction energetics. To address these limitations, this work presents the development of CHARMM-compatible all-atom force field parameters for the major phytochemicals of C. longa, enabling an accurate description of their structural, energetic, and interfacial properties. The parametrization protocol reproduces high-level quantum mechanical (QM) target data, including water-interaction energies, potential energy surface scans, and vibrational frequency calculations. The optimized parameters were rigorously validated through QM-MM geometry comparisons, crystal structure simulations, and protein-ligand molecular dynamics studies to assess accuracy, consistency, and transferability. The resulting parameter set, fully integrated within the CHARMM additive force field, facilitates reliable simulations of C. longa phytochemicals and their biomolecular interactions, thereby extending the applicability of CHARMM to complex natural product systems.","PeriodicalId":188,"journal":{"name":"Journal of Computational Chemistry","volume":"12 1","pages":"e70351"},"PeriodicalIF":3.0,"publicationDate":"2026-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147483761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Guozhen Sheng, Caimu Wang, Jiao Zhang, Wei Guo, Ruibin Liu
Against the backdrop of insufficient research into the microscopic reaction mechanisms of pentazole anion () salts, the present study developed a deep neural network potential (DNNP) model calibrated with first principles data. On this basis, large‐scale molecular dynamics (MD) simulations were performed to conduct an in‐depth investigation into the thermal decomposition mechanism and kinetic processes of hydroxylamine pentazole (NH 3 OHN 5 ) at the atomic scale. A highly precision DNNP model was constructed using an active learning strategy, whose predictions for energy and atomic forces showed excellent agreement with Density Functional Theory (DFT) results. MD simulations revealed that the thermal decomposition of NH 3 OHN 5 initiates with a hydrogen transfer reaction. The protonation of the reduces its ring‐opening energy barrier from 125.45 to 112.13 kJ/mol, significantly promoting the ring‐opening decomposition process. The final decomposition products were predominantly N 2 , H 2 O, and NH 3 . This research elucidates the decomposition pathways and reaction mechanism of NH 3 OHN 5 at the atomic scale, demonstrating the exceptional capability of the DNNP in simulating the reaction dynamics of energetic materials and providing a theoretical foundation for the subsequent molecular design of high‐performance, green energetic materials.
{"title":"Thermal Decomposition Simulations of Hydroxylamine Pentazolate With Deep Neural Network Potential","authors":"Guozhen Sheng, Caimu Wang, Jiao Zhang, Wei Guo, Ruibin Liu","doi":"10.1002/jcc.70362","DOIUrl":"https://doi.org/10.1002/jcc.70362","url":null,"abstract":"Against the backdrop of insufficient research into the microscopic reaction mechanisms of pentazole anion () salts, the present study developed a deep neural network potential (DNNP) model calibrated with first principles data. On this basis, large‐scale molecular dynamics (MD) simulations were performed to conduct an in‐depth investigation into the thermal decomposition mechanism and kinetic processes of hydroxylamine pentazole (NH <jats:sub>3</jats:sub> OHN <jats:sub>5</jats:sub> ) at the atomic scale. A highly precision DNNP model was constructed using an active learning strategy, whose predictions for energy and atomic forces showed excellent agreement with Density Functional Theory (DFT) results. MD simulations revealed that the thermal decomposition of NH <jats:sub>3</jats:sub> OHN <jats:sub>5</jats:sub> initiates with a hydrogen transfer reaction. The protonation of the reduces its ring‐opening energy barrier from 125.45 to 112.13 kJ/mol, significantly promoting the ring‐opening decomposition process. The final decomposition products were predominantly N <jats:sub>2</jats:sub> , H <jats:sub>2</jats:sub> O, and NH <jats:sub>3</jats:sub> . This research elucidates the decomposition pathways and reaction mechanism of NH <jats:sub>3</jats:sub> OHN <jats:sub>5</jats:sub> at the atomic scale, demonstrating the exceptional capability of the DNNP in simulating the reaction dynamics of energetic materials and providing a theoretical foundation for the subsequent molecular design of high‐performance, green energetic materials.","PeriodicalId":188,"journal":{"name":"Journal of Computational Chemistry","volume":"19 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147518917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wavefunction‐based quantum methods are some of the most accurate tools for predicting and analyzing the electronic structure of molecules, in particular for accounting for dynamical electron correlation. However, most methods of including dynamical correlation beyond the simple second‐order Møller–Plesset perturbation theory (MP2) level are too computationally expensive to apply to large molecules. Approximations which reduce scaling with system size are a potential remedy, such as the tensor hyper‐contraction (THC) technique of Hohenstein et al., but also result in additional sources of error. In this work, we correct errors in THC‐approximated methods using machine learning. Specifically, we apply THC to third‐order Møller–Plesset theory (MP3) as a simplified model for coupled cluster with single and double excitations (CCSD), and train several regression models on observed THC errors from the Main Group Chemistry Database (MGCDB84). We compare performance of multiple linear regression models and nonlinear Kernel Ridge regression models. We also investigate correlation procedures using absolute and relative corrections and evaluate the corrections for both molecule and reaction energies. We discuss the potential for using regression techniques to correct THC‐MP3 errors by comparing it to the “canonical” MP3 reference values and find the optimum technique based on accuracy. We find that nonlinear regression models reduced root mean squared errors between THC‐ and canonical MP3 by a factor of 6–9× for total molecular energies and 2–3× for reaction energies.
{"title":"Tensor Hypercontraction Error Correction Using Regression","authors":"Ishna Satyarth, Eric C. Larson, Devin A. Matthews","doi":"10.1002/jcc.70354","DOIUrl":"https://doi.org/10.1002/jcc.70354","url":null,"abstract":"Wavefunction‐based quantum methods are some of the most accurate tools for predicting and analyzing the electronic structure of molecules, in particular for accounting for dynamical electron correlation. However, most methods of including dynamical correlation beyond the simple second‐order Møller–Plesset perturbation theory (MP2) level are too computationally expensive to apply to large molecules. Approximations which reduce scaling with system size are a potential remedy, such as the tensor hyper‐contraction (THC) technique of Hohenstein et al., but also result in additional sources of error. In this work, we correct errors in THC‐approximated methods using machine learning. Specifically, we apply THC to third‐order Møller–Plesset theory (MP3) as a simplified model for coupled cluster with single and double excitations (CCSD), and train several regression models on observed THC errors from the Main Group Chemistry Database (MGCDB84). We compare performance of multiple linear regression models and nonlinear Kernel Ridge regression models. We also investigate correlation procedures using absolute and relative corrections and evaluate the corrections for both molecule and reaction energies. We discuss the potential for using regression techniques to correct THC‐MP3 errors by comparing it to the “canonical” MP3 reference values and find the optimum technique based on accuracy. We find that nonlinear regression models reduced root mean squared errors between THC‐ and canonical MP3 by a factor of 6–9× for total molecular energies and 2–3× for reaction energies.","PeriodicalId":188,"journal":{"name":"Journal of Computational Chemistry","volume":"8 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147471057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We propose here a description and classification of the hydrogen bond (HB) that is based on the Graphic representation of the Local electron Energy Density H(r) (GLED). A peculiar aspect of the GLED method, proposed by us in a recent study [Journal of Chemical Physics 163 (2025): 034107], is that the major character of the bond (covalent or noncovalent) can be inferred simply by the visual inspection of the plotted H(r), particularly the 3D H(r) = 0 isosurface. The analysis of the hydrogen-bonded complexes unraveled, in particular, that their bonding character is strictly related to their dissociation energy (DE), so that the GLED assignment can be used to estimate the strength of the interaction. We also found that increasing values of DE mirror, in particular, an increased degree of covalency of the interaction. We could thus propose a classification of the HB that is based on the combined use of bonding character and stability. The HB was, in particular, assigned as weak (0.5-4.5 kcal mol-1), medium (3.5-5.5 kcal mol-1), and strong (4.5-15.0 kcal mol-1) for the neutral species, and medium (8.5-13.0 kcal mol-1), strong (15.0-32.0 kcal mol-1), and very strong (30.0-70.0 kcal mol-1) for the ionic ones, respectively. For systems stabilized by more than one HB, the method allows to eye-catch in case different role of the various interactions.
{"title":"A Theoretical Investigation on the Hydrogen Bond Based on the GLED Method of Bonding Analysis.","authors":"Stefano Borocci,Felice Grandinetti,Nico Sanna,Costantino Zazza","doi":"10.1002/jcc.70348","DOIUrl":"https://doi.org/10.1002/jcc.70348","url":null,"abstract":"We propose here a description and classification of the hydrogen bond (HB) that is based on the Graphic representation of the Local electron Energy Density H(r) (GLED). A peculiar aspect of the GLED method, proposed by us in a recent study [Journal of Chemical Physics 163 (2025): 034107], is that the major character of the bond (covalent or noncovalent) can be inferred simply by the visual inspection of the plotted H(r), particularly the 3D H(r) = 0 isosurface. The analysis of the hydrogen-bonded complexes unraveled, in particular, that their bonding character is strictly related to their dissociation energy (DE), so that the GLED assignment can be used to estimate the strength of the interaction. We also found that increasing values of DE mirror, in particular, an increased degree of covalency of the interaction. We could thus propose a classification of the HB that is based on the combined use of bonding character and stability. The HB was, in particular, assigned as weak (0.5-4.5 kcal mol-1), medium (3.5-5.5 kcal mol-1), and strong (4.5-15.0 kcal mol-1) for the neutral species, and medium (8.5-13.0 kcal mol-1), strong (15.0-32.0 kcal mol-1), and very strong (30.0-70.0 kcal mol-1) for the ionic ones, respectively. For systems stabilized by more than one HB, the method allows to eye-catch in case different role of the various interactions.","PeriodicalId":188,"journal":{"name":"Journal of Computational Chemistry","volume":"5 1","pages":"e70348"},"PeriodicalIF":3.0,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147373838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Felix R S Purtscher,Armin Penz,Josef M Gallmetzer,Jakob Gamper,Thomas S Hofer
Practical considerations for the parametrization of the transition metal platinum within the third-order density-functional tight-binding (DFTB3) method are presented, enabling straightforward parametrizations of interactions between Pt and elements from the s-, p-, and d-blocks of the periodic table. The newly developed parameter set is fully compatible with the 3ob DFTB3 framework, thereby extending the chemical space accessible to DFTB and enabling rapid and reliable simulations of platinum-containing systems. The parameters were initially benchmarked against more than 1300 Pt-containing structures extracted from the Cambridge Crystallographic Data Centre, as well as over 50 reference systems optimized at the MP2/cc-pVTZ level of theory. Further validation included a challenging binuclear platinum(II) complex, QM/MM molecular dynamics (MD) simulations of Pt(II) complexes in aqueous solution, and 3d-periodic DFTB-based molecular dynamics simulations of cisplatin embedded in metal-organic framework (MOF) hosts. Analysis of the resulting trajectories demonstrates a robust and consistent description of platinum coordination environments. To facilitate reproducibility and adoption, example Python scripts covering each step of the parametrization workflow are provided as part of the Supporting Information.
{"title":"DFTB Parametrization at the Example of Platinum-Implementation, Validation and Practical Considerations.","authors":"Felix R S Purtscher,Armin Penz,Josef M Gallmetzer,Jakob Gamper,Thomas S Hofer","doi":"10.1002/jcc.70342","DOIUrl":"https://doi.org/10.1002/jcc.70342","url":null,"abstract":"Practical considerations for the parametrization of the transition metal platinum within the third-order density-functional tight-binding (DFTB3) method are presented, enabling straightforward parametrizations of interactions between Pt and elements from the s-, p-, and d-blocks of the periodic table. The newly developed parameter set is fully compatible with the 3ob DFTB3 framework, thereby extending the chemical space accessible to DFTB and enabling rapid and reliable simulations of platinum-containing systems. The parameters were initially benchmarked against more than 1300 Pt-containing structures extracted from the Cambridge Crystallographic Data Centre, as well as over 50 reference systems optimized at the MP2/cc-pVTZ level of theory. Further validation included a challenging binuclear platinum(II) complex, QM/MM molecular dynamics (MD) simulations of Pt(II) complexes in aqueous solution, and 3d-periodic DFTB-based molecular dynamics simulations of cisplatin embedded in metal-organic framework (MOF) hosts. Analysis of the resulting trajectories demonstrates a robust and consistent description of platinum coordination environments. To facilitate reproducibility and adoption, example Python scripts covering each step of the parametrization workflow are provided as part of the Supporting Information.","PeriodicalId":188,"journal":{"name":"Journal of Computational Chemistry","volume":"7 1","pages":"e70342"},"PeriodicalIF":3.0,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147393851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
It remains challenging to microscopically simulate chemical reaction systems in which multiple chemical reactions proceed concurrently, thereby determining the overall time evolution of the system and the structure of the resulting products. For this purpose, the Red Moon (RM) method is a promising method that describes complex reaction systems by alternately using the molecular dynamics (MD) and Monte Carlo (MC) methods. However, the efficiency of the RM method strongly depends on how frequently the reactive configurations appear during the MD procedure, which can lead to inefficiencies in some systems where such configurations are rarely sampled. To overcome this limitation, we have proposed an improved version of the RM method, the REMD-RM method, which incorporates the replica-exchange molecular dynamics (REMD) method into the RM method, and applied it to two representative model systems: (i) The propylene polymerization reaction catalyzed by C2-symmetric ansa-zirconocene complex and (ii) the radical cross-linking reaction of polypropylene. In addition to improving the efficiency of sampling reactive configurations, the REMD-RM method successfully reproduced the stereoregularity of the resulting polymer in the former case, and the temperature dependence of cross-linking reactions in the latter. Finally, we discussed the potential applicability of the REMD-RM method and the possible extension of the RM method depending on the nature of the target system.
{"title":"On the Bona Fide Sampling of Reaction Candidates in Red Moon Method by Replica-Exchange Molecular Dynamics Method: REMD-RM Method and Its Efficacy in Polymerization and Cross-Linking Reactions of Polypropylene.","authors":"Kentaro Matsumoto,Yuichi Tanaka,Seiryu Umetani,Takashi Nakano,Masataka Nagaoka","doi":"10.1002/jcc.70341","DOIUrl":"https://doi.org/10.1002/jcc.70341","url":null,"abstract":"It remains challenging to microscopically simulate chemical reaction systems in which multiple chemical reactions proceed concurrently, thereby determining the overall time evolution of the system and the structure of the resulting products. For this purpose, the Red Moon (RM) method is a promising method that describes complex reaction systems by alternately using the molecular dynamics (MD) and Monte Carlo (MC) methods. However, the efficiency of the RM method strongly depends on how frequently the reactive configurations appear during the MD procedure, which can lead to inefficiencies in some systems where such configurations are rarely sampled. To overcome this limitation, we have proposed an improved version of the RM method, the REMD-RM method, which incorporates the replica-exchange molecular dynamics (REMD) method into the RM method, and applied it to two representative model systems: (i) The propylene polymerization reaction catalyzed by C2-symmetric ansa-zirconocene complex and (ii) the radical cross-linking reaction of polypropylene. In addition to improving the efficiency of sampling reactive configurations, the REMD-RM method successfully reproduced the stereoregularity of the resulting polymer in the former case, and the temperature dependence of cross-linking reactions in the latter. Finally, we discussed the potential applicability of the REMD-RM method and the possible extension of the RM method depending on the nature of the target system.","PeriodicalId":188,"journal":{"name":"Journal of Computational Chemistry","volume":"72 1","pages":"e70341"},"PeriodicalIF":3.0,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147368485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kai Wang,Yiming Zhang,Hanyu Du,Chaoyong Wang,Linyuan Lian,Shuai Xu
Magnetic germanium-based clusters are attracting increasing attention due to their tunable structures and properties, quantum size effects, and potential applications in spintronics and multifunctional materials. Here, we report the geometric structures, electronic properties, and magnetic characteristics of VMnGen (n = 3-18) clusters. The V and Mn atoms tend to stay adjacent and become encapsulated by Ge atoms. Small-sized clusters (n ≤ 9) preferentially adopt bipyramid-based structures, while starting from n = 10, a structure with one fully encapsulated transition metal atom emerges and persists up to n = 16, eventually evolving into fully endohedral structures where both TM atoms are completely wrapped in the larger clusters (n = 17-18). The result indicates that the Cr atom consistently acts as an electron donor in small clusters with n = 3-10, and as an electron acceptor for sizes n = 11-18, whereas the Mn atom always serves as an electron acceptor, except at size n = 11. The average binding energy of these clusters increases with cluster size n, suggesting higher stability for larger clusters. The second-order energy difference indicates that clusters of sizes 6, 10, and 12 exhibit distinct local maxima, suggesting higher relative stability. Among these VMnGen (n = 3-18) clusters, VMn tends to exhibit antiferromagnetic coupling for sizes n = 3, 4, 6, 10, 11, 13, 14, and 18, while the remaining clusters are non-magnetic.
{"title":"An Investigation of the Structural, Electronic, and Magnetic Properties of VMnGen (n = 3-18) Clusters: Insights From Theoretical Calculations.","authors":"Kai Wang,Yiming Zhang,Hanyu Du,Chaoyong Wang,Linyuan Lian,Shuai Xu","doi":"10.1002/jcc.70345","DOIUrl":"https://doi.org/10.1002/jcc.70345","url":null,"abstract":"Magnetic germanium-based clusters are attracting increasing attention due to their tunable structures and properties, quantum size effects, and potential applications in spintronics and multifunctional materials. Here, we report the geometric structures, electronic properties, and magnetic characteristics of VMnGen (n = 3-18) clusters. The V and Mn atoms tend to stay adjacent and become encapsulated by Ge atoms. Small-sized clusters (n ≤ 9) preferentially adopt bipyramid-based structures, while starting from n = 10, a structure with one fully encapsulated transition metal atom emerges and persists up to n = 16, eventually evolving into fully endohedral structures where both TM atoms are completely wrapped in the larger clusters (n = 17-18). The result indicates that the Cr atom consistently acts as an electron donor in small clusters with n = 3-10, and as an electron acceptor for sizes n = 11-18, whereas the Mn atom always serves as an electron acceptor, except at size n = 11. The average binding energy of these clusters increases with cluster size n, suggesting higher stability for larger clusters. The second-order energy difference indicates that clusters of sizes 6, 10, and 12 exhibit distinct local maxima, suggesting higher relative stability. Among these VMnGen (n = 3-18) clusters, VMn tends to exhibit antiferromagnetic coupling for sizes n = 3, 4, 6, 10, 11, 13, 14, and 18, while the remaining clusters are non-magnetic.","PeriodicalId":188,"journal":{"name":"Journal of Computational Chemistry","volume":"6 1","pages":"e70345"},"PeriodicalIF":3.0,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147368482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Laccases are multi-copper oxidase enzymes that oxidize a wide range of aromatic and non-aromatic compounds using molecular oxygen, producing water as the sole byproduct and making them attractive biocatalysts for green chemistry. However, the ability of laccases to oxidize specific substrates depends on a complex interplay of molecular structure, enzyme properties, redox potential, and environmental context, making laccase-substrate compatibility hard to predict. We apply machine learning models to pre-screen laccase-substrate combinations, streamlining experimental workflows. We evaluate four classical classifiers and a transformer-based model (ChemBERTa) on three in-house curated datasets of aromatic substrates with oxidation profiles for distinct laccases. Overall, the tested models achieve comparable performance, with random forest (RFC) demonstrating more stability across different data splits and laccases. This assessment is complemented by RFC feature-importance and ChemBERTa attention analyses, which highlight molecular features associated with oxidation outcomes. We also introduce a lightweight tool to visualize ChemBERTa predictions by mapping SMILES attributions onto molecular graphs. These findings provide a robust, interpretable framework for accelerating laccase-substrate discovery.
{"title":"Machine Learning Prediction of Laccase-Catalyzed Oxidation of Aromatic Compounds Using Curated Enzyme-Specific Datasets.","authors":"Yulia Kulagina,Christian Goldhahn,Ramon Weishaupt,Mark Schubert","doi":"10.1002/jcc.70344","DOIUrl":"https://doi.org/10.1002/jcc.70344","url":null,"abstract":"Laccases are multi-copper oxidase enzymes that oxidize a wide range of aromatic and non-aromatic compounds using molecular oxygen, producing water as the sole byproduct and making them attractive biocatalysts for green chemistry. However, the ability of laccases to oxidize specific substrates depends on a complex interplay of molecular structure, enzyme properties, redox potential, and environmental context, making laccase-substrate compatibility hard to predict. We apply machine learning models to pre-screen laccase-substrate combinations, streamlining experimental workflows. We evaluate four classical classifiers and a transformer-based model (ChemBERTa) on three in-house curated datasets of aromatic substrates with oxidation profiles for distinct laccases. Overall, the tested models achieve comparable performance, with random forest (RFC) demonstrating more stability across different data splits and laccases. This assessment is complemented by RFC feature-importance and ChemBERTa attention analyses, which highlight molecular features associated with oxidation outcomes. We also introduce a lightweight tool to visualize ChemBERTa predictions by mapping SMILES attributions onto molecular graphs. These findings provide a robust, interpretable framework for accelerating laccase-substrate discovery.","PeriodicalId":188,"journal":{"name":"Journal of Computational Chemistry","volume":"263 1","pages":"e70344"},"PeriodicalIF":3.0,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147368483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Semi-empirical quantum-chemical methods such as extended tight-binding (xTB) models are widely used for large-scale simulations. Despite their popularity, their accuracy for transition-metal containing systems is lower than, for example, closed-shell organic molecules. In this work, we extend the Q-Chem-xTB framework with a geometric direct minimization (GDM) scheme for robust self-consistent convergence and Hubbard correction (+ U $$ U $$ ) to improve the description of local interactions and reduce self-interaction errors similar to those characteristic of density-functional theory calculations for transition-metal complexes. The Hubbard correction term is integrated self-consistently within the xTB Hamiltonian, allowing shell-specific U $$ U $$ values for each atom. The performance of Q-Chem-xTB+ U $$ U $$ is assessed for four benchmark sets of iron complexes, focusing on their spin-state energetics. Sensitivity and optimization analyses of the spin parameters show that parameter tuning alone cannot systematically reduce the error or consistently recover correct spin ground-state predictions across different datasets. In contrast, introducing the + U $$ U $$ correction yields significant error reduction and improved electronic linearity with respect to fractional occupation, demonstrating that the correction fulfills its intended role of reducing self-interaction error. However, the optimized U $$ U $$ values remain system-dependent, and the resulting improvements are only partially transferable. As a side effect, the + U $$ U $$ correction stabilizes the self-consistent field optimization by widening the HOMO-LUMO gap, thereby overcoming convergence instabilities of the conventional direct inversion of the iterative subspace (DIIS) scheme at low electronic temperatures.
扩展紧密结合（xTB）模型等半经验量子化学方法被广泛用于大规模模拟。尽管它们很受欢迎，但它们对含有过渡金属的系统的精度低于，例如，闭壳有机分子。在这项工作中，我们用几何直接最小化（GDM）方案扩展了q - chemm - xtb框架，用于鲁棒自洽收敛和Hubbard校正（+ U $$ U $$），以改进局部相互作用的描述，并减少类似于过渡金属配合物密度泛函理论计算的自相互作用误差。哈伯德校正项自一致地集成在xTB哈密顿量中，允许每个原子的特定壳层U $$ U $$值。对Q-Chem-xTB+ U $$ U $$的性能进行了四组基准铁配合物的评估，重点关注它们的自旋态能量学。对自旋参数的敏感性和优化分析表明，单靠参数调整不能系统地减少误差，也不能在不同的数据集上一致地恢复正确的自旋基态预测。相比之下，引入+ U $$ U $$校正可以显著降低误差，并改善分数占位的电子线性度，表明该校正实现了减少自相互作用误差的预期作用。然而，优化后的U $$ U $$值仍然依赖于系统，因此所得到的改进只能部分地转移。作为副作用，+ U $$ U $$修正通过扩大HOMO-LUMO间隙来稳定自一致场优化，从而克服了传统迭代子空间直接反演（DIIS）方案在低温下的收敛不稳定性。
{"title":"Extensions to Extended Tight-Binding Methods for Transition-Metal Containing Systems.","authors":"Siyavash Moradi,Rebecca Tomann,Martin Head-Gordon,Christopher J Stein","doi":"10.1002/jcc.70346","DOIUrl":"https://doi.org/10.1002/jcc.70346","url":null,"abstract":"Semi-empirical quantum-chemical methods such as extended tight-binding (xTB) models are widely used for large-scale simulations. Despite their popularity, their accuracy for transition-metal containing systems is lower than, for example, closed-shell organic molecules. In this work, we extend the Q-Chem-xTB framework with a geometric direct minimization (GDM) scheme for robust self-consistent convergence and Hubbard correction (+ U $$ U $$ ) to improve the description of local interactions and reduce self-interaction errors similar to those characteristic of density-functional theory calculations for transition-metal complexes. The Hubbard correction term is integrated self-consistently within the xTB Hamiltonian, allowing shell-specific U $$ U $$ values for each atom. The performance of Q-Chem-xTB+ U $$ U $$ is assessed for four benchmark sets of iron complexes, focusing on their spin-state energetics. Sensitivity and optimization analyses of the spin parameters show that parameter tuning alone cannot systematically reduce the error or consistently recover correct spin ground-state predictions across different datasets. In contrast, introducing the + U $$ U $$ correction yields significant error reduction and improved electronic linearity with respect to fractional occupation, demonstrating that the correction fulfills its intended role of reducing self-interaction error. However, the optimized U $$ U $$ values remain system-dependent, and the resulting improvements are only partially transferable. As a side effect, the + U $$ U $$ correction stabilizes the self-consistent field optimization by widening the HOMO-LUMO gap, thereby overcoming convergence instabilities of the conventional direct inversion of the iterative subspace (DIIS) scheme at low electronic temperatures.","PeriodicalId":188,"journal":{"name":"Journal of Computational Chemistry","volume":"55 1","pages":"e70346"},"PeriodicalIF":3.0,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147381336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Matt Hugget, Angela Dellai, Philippe Aurel, Marilù G. Maraldi, Marc de Wergifosse, Frédéric Castet
Predicting the nonlinear optical (NLO) properties of large, disordered supramolecular aggregates is challenging because fully capturing dynamic fluctuations and intermolecular interactions requires a quantum‐mechanical treatment of aggregation effects that remains out of reach for conventional computational methods. In the simplified time‐dependent density functional theory (sTD‐DFT) framework, we present a fully automated protocol for optimally tuning the Mataga‐Nishimoto‐Ohno‐Klopman (MNOK) expressions of the two‐electron integrals, enabling the prediction of NLO responses at a fraction of the cost of conventional TD‐DFT. Using ground‐state molecular orbitals from either DFT or extended tight‐binding (xTB) calculations, the transferability of the optimized parameters is validated across isolated molecules, small model aggregates, and supramolecular clusters representative of azobenzene self‐assembled monolayers (SAMs) extracted from molecular dynamics (MD) simulations. The results show that sTD‐DFT reliably reproduces TD‐DFT NLO responses and allows the treatment of large, disordered aggregates. Comparison of cluster‐ and fragment‐based NLO responses shows that aggregation significantly reduces the second‐harmonic generation (SHG) signal in azobenzene SAMs. Furthermore, comparing full sTD‐DFT calculations with those relying on an electrostatic embedding of the environment reveals that both the trans / cis NLO contrast and the anisotropy of the SHG responses can differ substantially when all molecules are treated on an equal quantum‐mechanical footing. These results demonstrate that combining MD simulations with optimally tuned sTD‐DFT provides a practical strategy for evaluating NLO responses in complex supramolecular systems, fully capturing aggregation effects at an all‐atom quantum mechanical (AQM) level.
{"title":"Simplified Time‐Dependent DFT for all‐Atom Simulations of Second Harmonic Generation Responses: A Case Study on Photoswitchable Azobenzene Monolayers","authors":"Matt Hugget, Angela Dellai, Philippe Aurel, Marilù G. Maraldi, Marc de Wergifosse, Frédéric Castet","doi":"10.1002/jcc.70336","DOIUrl":"https://doi.org/10.1002/jcc.70336","url":null,"abstract":"Predicting the nonlinear optical (NLO) properties of large, disordered supramolecular aggregates is challenging because fully capturing dynamic fluctuations and intermolecular interactions requires a quantum‐mechanical treatment of aggregation effects that remains out of reach for conventional computational methods. In the simplified time‐dependent density functional theory (sTD‐DFT) framework, we present a fully automated protocol for optimally tuning the Mataga‐Nishimoto‐Ohno‐Klopman (MNOK) expressions of the two‐electron integrals, enabling the prediction of NLO responses at a fraction of the cost of conventional TD‐DFT. Using ground‐state molecular orbitals from either DFT or extended tight‐binding (xTB) calculations, the transferability of the optimized parameters is validated across isolated molecules, small model aggregates, and supramolecular clusters representative of azobenzene self‐assembled monolayers (SAMs) extracted from molecular dynamics (MD) simulations. The results show that sTD‐DFT reliably reproduces TD‐DFT NLO responses and allows the treatment of large, disordered aggregates. Comparison of cluster‐ and fragment‐based NLO responses shows that aggregation significantly reduces the second‐harmonic generation (SHG) signal in azobenzene SAMs. Furthermore, comparing full sTD‐DFT calculations with those relying on an electrostatic embedding of the environment reveals that both the <jats:italic>trans</jats:italic> / <jats:italic>cis</jats:italic> NLO contrast and the anisotropy of the SHG responses can differ substantially when all molecules are treated on an equal quantum‐mechanical footing. These results demonstrate that combining MD simulations with optimally tuned sTD‐DFT provides a practical strategy for evaluating NLO responses in complex supramolecular systems, fully capturing aggregation effects at an all‐atom quantum mechanical (AQM) level.","PeriodicalId":188,"journal":{"name":"Journal of Computational Chemistry","volume":"50 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146778452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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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