Pub Date : 2026-01-27DOI: 10.1021/acs.jctc.5c01703
Chris E Mohn, Helmer Fjellvåg, Ponniah Vajeeston, Martin Valldor, Kristin Bergum
We benchmark exchange-correlation functionals for the calculation of fundamental band gaps of inorganic nitrides. These include conventional functionals such as the local density approximation (LDA), the generalized-gradient (Perdew-Burke-Ernzerhof) approximation (PBE), simple Slater exchange functionals (SLOC), specialized LDA/GGA-derived high local exchange (HLE16) and Armiento-Kümmel semilocal (AK13) functionals, meta-GGA functionals including TASK, the modified Becke-Johnson functional (mBJ), and Heyd-Scuseria-Ernzerhof (HSE06) hybrid functional, as well as quasiparticle GW theory. Since inorganic nitrides remain strongly under-represented in previous extensive benchmark studies, the current subdatabase contributes towards building a future large-scale balanced materials compilation of band gaps to benchmark theory. From a literature survey, we carefully collect 25 binary and 11 ternary nitrides with a focus on semiconductors spanning the periodic table, including ionic Li3N, antibixbyite-structured X3N2 (X = Be, Mg, Ca), early transition metals and lanthanides (e.g., ScN, YN, and LaN), ultrahard Th3P4-type structured M3N4 (M = Zr, Hf) compounds, promising photocatalysts Ta3N5, different polymorphs of III-V reference covalent nitrides (BN, AlN, GaN), and many M3N4 polymorphs (M = C, Si, and Ge) such as spinel-structured phases. Consistent with previous extensive benchmark tests, conventional LDA/PBE unsystematically largely underestimate band gaps with mean absolute errors (MAE) of >1.0 eV and mean absolute percentage errors (MAPE) of about 50%. Simple Slater exchange functional, SLOC, the GGA-derived AK13LDA and HLE16 functionals show improvement over LDA/PBE with MAE of 0.5-0.6 eV (MAPE ∼ 20-25%) with mBJ and HSE06 being the most accurate, with MAE = 0.30 and 0.28 eV (MAPE 12.1% and 11.1%), respectively. Strategies for the development of machine learning and the choice of appropriate exchange-correlation functionals for high-throughput large-scale material screening are discussed in light of these results.
我们将交换相关函数作为计算无机氮化物基本带隙的基准。这些包括传统的泛函，如局部密度近似（LDA）、广义梯度（Perdew-Burke-Ernzerhof）近似（PBE）、简单的Slater交换泛函（SLOC）、专门的LDA/ gga衍生的高局部交换（HLE16）和armiento - k mmel半局部（AK13）泛函、元gga泛函（包括TASK）、改进的Becke-Johnson泛函（mBJ）和Heyd-Scuseria-Ernzerhof （HSE06）混合泛函，以及准粒子GW理论。由于无机氮化物在以前广泛的基准研究中仍然严重不足，目前的子数据库有助于建立未来大规模平衡材料的带隙基准理论汇编。从文献调查中，我们仔细收集了25种二元和11种三元氮化物，重点关注半导体元素周期表，包括离子Li3N，抗碳素体结构的X3N2 (X = Be, Mg, Ca)，早期过渡金属和镧系元素（例如，ScN， YN和LaN），超硬th3p4型结构的M3N4 （M = Zr, Hf）化合物，有前途的光催化剂Ta3N5， III-V参考共价氮化物（BN, AlN, GaN）的不同多晶型，以及许多M3N4多晶型（M = C, Si, Si）。和Ge)，如尖晶石结构相。与之前广泛的基准测试一致，传统的LDA/PBE在很大程度上非系统地低估了带隙，平均绝对误差（MAE）为100 eV，平均绝对百分比误差（MAPE）约为50%。简单Slater交换功能，SLOC， gga衍生的AK13LDA和HLE16功能比LDA/PBE表现出改善，MAE为0.5-0.6 eV (MAPE ~ 20-25%)，其中mBJ和HSE06最准确，MAE分别为0.30和0.28 eV （MAPE分别为12.1%和11.1%）。根据这些结果，讨论了机器学习发展策略和选择合适的交换相关函数进行高通量大规模材料筛选。
{"title":"Benchmarking Density Functional Theory for Accurate Calculation of Nitride Band Gaps.","authors":"Chris E Mohn, Helmer Fjellvåg, Ponniah Vajeeston, Martin Valldor, Kristin Bergum","doi":"10.1021/acs.jctc.5c01703","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c01703","url":null,"abstract":"<p><p>We benchmark exchange-correlation functionals for the calculation of fundamental band gaps of inorganic nitrides. These include conventional functionals such as the local density approximation (LDA), the generalized-gradient (Perdew-Burke-Ernzerhof) approximation (PBE), simple Slater exchange functionals (SLOC), specialized LDA/GGA-derived high local exchange (HLE16) and Armiento-Kümmel semilocal (AK13) functionals, meta-GGA functionals including TASK, the modified Becke-Johnson functional (mBJ), and Heyd-Scuseria-Ernzerhof (HSE06) hybrid functional, as well as quasiparticle <i>GW</i> theory. Since inorganic nitrides remain strongly under-represented in previous extensive benchmark studies, the current subdatabase contributes towards building a future large-scale balanced materials compilation of band gaps to benchmark theory. From a literature survey, we carefully collect 25 binary and 11 ternary nitrides with a focus on semiconductors spanning the periodic table, including ionic Li<sub>3</sub>N, antibixbyite-structured X<sub>3</sub>N<sub>2</sub> (X = Be, Mg, Ca), early transition metals and lanthanides (e.g., ScN, YN, and LaN), ultrahard Th<sub>3</sub>P<sub>4</sub>-type structured M<sub>3</sub>N<sub>4</sub> (M = Zr, Hf) compounds, promising photocatalysts Ta<sub>3</sub>N<sub>5</sub>, different polymorphs of III-V reference covalent nitrides (BN, AlN, GaN), and many M<sub>3</sub>N<sub>4</sub> polymorphs (M = C, Si, and Ge) such as spinel-structured phases. Consistent with previous extensive benchmark tests, conventional LDA/PBE unsystematically largely underestimate band gaps with mean absolute errors (MAE) of >1.0 eV and mean absolute percentage errors (MAPE) of about 50%. Simple Slater exchange functional, SLOC, the GGA-derived AK13LDA and HLE16 functionals show improvement over LDA/PBE with MAE of 0.5-0.6 eV (MAPE ∼ 20-25%) with mBJ and HSE06 being the most accurate, with MAE = 0.30 and 0.28 eV (MAPE 12.1% and 11.1%), respectively. Strategies for the development of machine learning and the choice of appropriate exchange-correlation functionals for high-throughput large-scale material screening are discussed in light of these results.</p>","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146049770","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 : 2026-01-27DOI: 10.1021/acs.jctc.5c01984
Adrian L Batista-Planas,Ernesto Quintas-Sánchez,Richard Dawes
Describing intermolecular forces is fundamental to modeling and predicting the behavior of molecular systems. In particular, long-range molecular interactions─with electrostatic, induction, and dispersion as the main components─play a critical role, especially for low-temperature and low-density regimes. Long-range interactions are often described through perturbation theory, representing the electronic charge distribution via a multipolar series of the moments and polarizability tensors corresponding to each molecule. However, while the theory is well established, obtaining the resulting analytical expressions (and their practical implementation) constitutes a highly complex and system-dependent task. To address this challenge, we developed long-range-fit (LRF), an interactive and user-friendly software package designed to automate the generation and fitting of long-range interaction terms for arbitrary molecules in nondegenerate (ground or excited) electronic states. We have derived and implemented all terms up to 15th order, without approximations, via a spherical tensor representation, with symmetry adaptation to all molecular point-group symmetries. The resulting potential energy surface is compatible with most representations of the close interaction region.
{"title":"Long-Range Fit: A Software Package for the Representation and Study of Long-Range Molecular Interactions.","authors":"Adrian L Batista-Planas,Ernesto Quintas-Sánchez,Richard Dawes","doi":"10.1021/acs.jctc.5c01984","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c01984","url":null,"abstract":"Describing intermolecular forces is fundamental to modeling and predicting the behavior of molecular systems. In particular, long-range molecular interactions─with electrostatic, induction, and dispersion as the main components─play a critical role, especially for low-temperature and low-density regimes. Long-range interactions are often described through perturbation theory, representing the electronic charge distribution via a multipolar series of the moments and polarizability tensors corresponding to each molecule. However, while the theory is well established, obtaining the resulting analytical expressions (and their practical implementation) constitutes a highly complex and system-dependent task. To address this challenge, we developed long-range-fit (LRF), an interactive and user-friendly software package designed to automate the generation and fitting of long-range interaction terms for arbitrary molecules in nondegenerate (ground or excited) electronic states. We have derived and implemented all terms up to 15th order, without approximations, via a spherical tensor representation, with symmetry adaptation to all molecular point-group symmetries. The resulting potential energy surface is compatible with most representations of the close interaction region.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"76 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146056770","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 : 2026-01-27DOI: 10.1021/acs.jctc.5c01280
Ethan R Curtis,Todd J Martínez
Molecular mechanics force fields enable atomistic simulations of complex systems that are too large for a quantum mechanical treatment. Simulation accuracy depends on the parameters employed in the force field. Every new molecule must have parameters generated for it, either by using a general force field or fitting a custom parameter set for that system. While fitting custom parameter sets can provide superior accuracy compared to a general force field, the process of single-molecule force field fitting is often tedious, expensive, and bespoke. We present an automated and iterative procedure for fitting single-molecule force fields. This program optimizes the parameters with respect to a data set of quantum mechanical (QM) calculations, runs dynamics with the new parameters to sample new conformations, computes QM energies and forces on those conformations, adds them to the data set, and returns to the parameter optimization step. In contrast to previous attempts at iterative optimization, we employ a validation set to determine convergence. Using a validation set circumvents problems with parameter convergence and flags when overfitting occurs. As an example, we find that Boltzmann sampling at 400 K is sufficient to fit a force field for a trialanine peptide, a system with a rugged potential energy surface. Last, we demonstrate the efficiency of the method by fitting a custom force field for each molecule in a library of 31 photosynthesis cofactors.
{"title":"Robust and Automated Force Field Parameterization Using Validation Sets and Active Learning.","authors":"Ethan R Curtis,Todd J Martínez","doi":"10.1021/acs.jctc.5c01280","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c01280","url":null,"abstract":"Molecular mechanics force fields enable atomistic simulations of complex systems that are too large for a quantum mechanical treatment. Simulation accuracy depends on the parameters employed in the force field. Every new molecule must have parameters generated for it, either by using a general force field or fitting a custom parameter set for that system. While fitting custom parameter sets can provide superior accuracy compared to a general force field, the process of single-molecule force field fitting is often tedious, expensive, and bespoke. We present an automated and iterative procedure for fitting single-molecule force fields. This program optimizes the parameters with respect to a data set of quantum mechanical (QM) calculations, runs dynamics with the new parameters to sample new conformations, computes QM energies and forces on those conformations, adds them to the data set, and returns to the parameter optimization step. In contrast to previous attempts at iterative optimization, we employ a validation set to determine convergence. Using a validation set circumvents problems with parameter convergence and flags when overfitting occurs. As an example, we find that Boltzmann sampling at 400 K is sufficient to fit a force field for a trialanine peptide, a system with a rugged potential energy surface. Last, we demonstrate the efficiency of the method by fitting a custom force field for each molecule in a library of 31 photosynthesis cofactors.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"64 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146056730","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 : 2026-01-26DOI: 10.1021/acs.jctc.5c01989
Yi Dong,Zhiqing Zhao,Ming Ma,Li He,Junjie Song,Lianghui Gao
Developing precise and transferable coarse-grained (CG) force fields (FFs) for ions and ionic surfactants in solution is challenging because solutions always contain ions with opposite charges, necessitating the simultaneous determination of multiple interaction parameters. In this study, we introduce an efficient workflow that combines global Bayesian optimization with local meta-multilinear interpolation parametrization and latin hypercube sampling to optimize CG FFs for NaCl, NaBr, and surfactants sodium dodecyl sulfate (SDS) and dodecyl trimethylammonium chloride/bromide (DTAC/DTAB). This approach is based on a polarizable CG water model, where van der Waals interactions are described using a piecewise Morse potential. The developed force fields accurately reproduced properties such as density, surface tension, dielectric constant, and osmotic pressure of salt solutions across a wide concentration range. They also successfully captured the micelle structures formed by SDS and DTAC/DTAB, along with the surface tensions at the solution/air interfaces. This work lays the groundwork for developing force fields for more complex and charged molecules.
{"title":"Bayesian Optimization of the Coarse-Grained Force Field for Ions and Ionic Surfactants Based on a Polarizable Water Model","authors":"Yi Dong,Zhiqing Zhao,Ming Ma,Li He,Junjie Song,Lianghui Gao","doi":"10.1021/acs.jctc.5c01989","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c01989","url":null,"abstract":"Developing precise and transferable coarse-grained (CG) force fields (FFs) for ions and ionic surfactants in solution is challenging because solutions always contain ions with opposite charges, necessitating the simultaneous determination of multiple interaction parameters. In this study, we introduce an efficient workflow that combines global Bayesian optimization with local meta-multilinear interpolation parametrization and latin hypercube sampling to optimize CG FFs for NaCl, NaBr, and surfactants sodium dodecyl sulfate (SDS) and dodecyl trimethylammonium chloride/bromide (DTAC/DTAB). This approach is based on a polarizable CG water model, where van der Waals interactions are described using a piecewise Morse potential. The developed force fields accurately reproduced properties such as density, surface tension, dielectric constant, and osmotic pressure of salt solutions across a wide concentration range. They also successfully captured the micelle structures formed by SDS and DTAC/DTAB, along with the surface tensions at the solution/air interfaces. This work lays the groundwork for developing force fields for more complex and charged molecules.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"30 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146044944","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 : 2026-01-26DOI: 10.1021/acs.jctc.5c02031
Yingfeng Zhang, Wei Xia, Kaifang Huang, Jin Xiao, John Z H Zhang
Accurately determining protein-ligand binding free energy is critical for drug design but requires computationally expensive quantum-mechanical (QM) calculations. Fragmentation methods can mitigate this cost, yet their accuracy hinges on properly modeling the polarizing chemical environment. Here we present an application and refinement of the Electrostatically Embedded Generalized Molecular Fractionation with Conjugate Caps (EE-GMFCC) approach, termed EE-GMFCC[P-L], for computing protein-ligand interaction energies. Our method efficiently obtains the total QM energy by linearly combining the energies of capped fragments embedded in a protein point-charge field and the pairwise interactions between non-neighboring fragments. After systematically investigating methodological parameters, including ligand charge, capping scheme, and basis set, we employed the approach to calculate interaction energies for a benchmark set of 21 protein-ligand systems. The resulting data set provides a high-accuracy standard for developing and validating more approximate computational methods in structure-based drug design.
{"title":"Accurate and Efficient Calculation of Protein-Ligand Interaction Energies Using an Electrostatically Embedded Fragmentation Method.","authors":"Yingfeng Zhang, Wei Xia, Kaifang Huang, Jin Xiao, John Z H Zhang","doi":"10.1021/acs.jctc.5c02031","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c02031","url":null,"abstract":"<p><p>Accurately determining protein-ligand binding free energy is critical for drug design but requires computationally expensive quantum-mechanical (QM) calculations. Fragmentation methods can mitigate this cost, yet their accuracy hinges on properly modeling the polarizing chemical environment. Here we present an application and refinement of the Electrostatically Embedded Generalized Molecular Fractionation with Conjugate Caps (EE-GMFCC) approach, termed EE-GMFCC[<i>P</i>-<i>L</i>], for computing protein-ligand interaction energies. Our method efficiently obtains the total QM energy by linearly combining the energies of capped fragments embedded in a protein point-charge field and the pairwise interactions between non-neighboring fragments. After systematically investigating methodological parameters, including ligand charge, capping scheme, and basis set, we employed the approach to calculate interaction energies for a benchmark set of 21 protein-ligand systems. The resulting data set provides a high-accuracy standard for developing and validating more approximate computational methods in structure-based drug design.</p>","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146049723","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 : 2026-01-26DOI: 10.1021/acs.jctc.5c01720
James H Thorpe,Peter R Franke,John F Stanton,David H Bross,Branko Ruscic
A series of approximations to CCSD contributions in computational model chemistries is presented in the context of kcal mol-1, kJ mol-1, and 20 cm-1 theoretical predictions of total atomization energies, benchmarked within the HEAT+CH4 test suite. A specific set of circumstances where MP2, without empirical scaling, may be used as an effective intermediate in the first two of these accuracy ranges was determined. However, SDQ-MP4, a method long used in pursuit of kcal mol-1 accuracy but relatively unstudied in the subchemical accuracy community, offers significant improvement over the quality of MP2 as a basis-set intermediate at significantly reduced cost compared to CCSD. Given this, we argue for SDQ-MP4 as the de facto CCSD basis-set intermediate in sub-chemical accuracy calculations when CCSD in a desired basis set becomes unaffordable. We additionally report on a "CBS-like" scheme, where MP2 and SDQ-MP4 are used in conjunction to create a "cheap" three-part approximation of large CCSD basis set limits. The data for the CCSD approximation schemes are organized in such a way that model chemistry developers can locate an analog of their current approach for the CCSD basis set limit and explore alternative intermediates that either decrease computational cost or increase computational accuracy. We also show, for a handful of molecules, that SDQ-MP4 shows promise as an effective basis-set intermediate for harmonic and fundamental frequency computations, allowing for zero-point corrections of nearly CCSD(T)/ANO1 quality using simple composite methods that only require CCSD(T)/ANO0.
{"title":"Reducing the Cost of CCSD Basis Set Extrapolation in Ab Initio Computational Thermochemistry.","authors":"James H Thorpe,Peter R Franke,John F Stanton,David H Bross,Branko Ruscic","doi":"10.1021/acs.jctc.5c01720","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c01720","url":null,"abstract":"A series of approximations to CCSD contributions in computational model chemistries is presented in the context of kcal mol-1, kJ mol-1, and 20 cm-1 theoretical predictions of total atomization energies, benchmarked within the HEAT+CH4 test suite. A specific set of circumstances where MP2, without empirical scaling, may be used as an effective intermediate in the first two of these accuracy ranges was determined. However, SDQ-MP4, a method long used in pursuit of kcal mol-1 accuracy but relatively unstudied in the subchemical accuracy community, offers significant improvement over the quality of MP2 as a basis-set intermediate at significantly reduced cost compared to CCSD. Given this, we argue for SDQ-MP4 as the de facto CCSD basis-set intermediate in sub-chemical accuracy calculations when CCSD in a desired basis set becomes unaffordable. We additionally report on a \"CBS-like\" scheme, where MP2 and SDQ-MP4 are used in conjunction to create a \"cheap\" three-part approximation of large CCSD basis set limits. The data for the CCSD approximation schemes are organized in such a way that model chemistry developers can locate an analog of their current approach for the CCSD basis set limit and explore alternative intermediates that either decrease computational cost or increase computational accuracy. We also show, for a handful of molecules, that SDQ-MP4 shows promise as an effective basis-set intermediate for harmonic and fundamental frequency computations, allowing for zero-point corrections of nearly CCSD(T)/ANO1 quality using simple composite methods that only require CCSD(T)/ANO0.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"7 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146044692","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 : 2026-01-26DOI: 10.1021/acs.jctc.5c01989
Yi Dong,Zhiqing Zhao,Ming Ma,Li He,Junjie Song,Lianghui Gao
Developing precise and transferable coarse-grained (CG) force fields (FFs) for ions and ionic surfactants in solution is challenging because solutions always contain ions with opposite charges, necessitating the simultaneous determination of multiple interaction parameters. In this study, we introduce an efficient workflow that combines global Bayesian optimization with local meta-multilinear interpolation parametrization and latin hypercube sampling to optimize CG FFs for NaCl, NaBr, and surfactants sodium dodecyl sulfate (SDS) and dodecyl trimethylammonium chloride/bromide (DTAC/DTAB). This approach is based on a polarizable CG water model, where van der Waals interactions are described using a piecewise Morse potential. The developed force fields accurately reproduced properties such as density, surface tension, dielectric constant, and osmotic pressure of salt solutions across a wide concentration range. They also successfully captured the micelle structures formed by SDS and DTAC/DTAB, along with the surface tensions at the solution/air interfaces. This work lays the groundwork for developing force fields for more complex and charged molecules.
{"title":"Bayesian Optimization of the Coarse-Grained Force Field for Ions and Ionic Surfactants Based on a Polarizable Water Model","authors":"Yi Dong,Zhiqing Zhao,Ming Ma,Li He,Junjie Song,Lianghui Gao","doi":"10.1021/acs.jctc.5c01989","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c01989","url":null,"abstract":"Developing precise and transferable coarse-grained (CG) force fields (FFs) for ions and ionic surfactants in solution is challenging because solutions always contain ions with opposite charges, necessitating the simultaneous determination of multiple interaction parameters. In this study, we introduce an efficient workflow that combines global Bayesian optimization with local meta-multilinear interpolation parametrization and latin hypercube sampling to optimize CG FFs for NaCl, NaBr, and surfactants sodium dodecyl sulfate (SDS) and dodecyl trimethylammonium chloride/bromide (DTAC/DTAB). This approach is based on a polarizable CG water model, where van der Waals interactions are described using a piecewise Morse potential. The developed force fields accurately reproduced properties such as density, surface tension, dielectric constant, and osmotic pressure of salt solutions across a wide concentration range. They also successfully captured the micelle structures formed by SDS and DTAC/DTAB, along with the surface tensions at the solution/air interfaces. This work lays the groundwork for developing force fields for more complex and charged molecules.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"68 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146044941","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 : 2026-01-26DOI: 10.1021/acs.jctc.5c01602
Mengsa Wang,Yuzhi Zhou,Han Wang
In this paper, we introduce ZORANet, a Fermionic neural network framework for the relativistic calculations based on scalar zeroth-order regular approximation (ZORA), which greatly expands the scope of deep neural network-based electronic structure methods. To address the numerical difficulties specific to the relativistic calculations, the electron–nucleus cusp correction scheme and an importance sampling technique have been carefully implemented to ensure training stability and reduce statistical errors. In addition, two variants, namely, scaled ZORANet and atomic ZORANet, have been developed to mitigate the notorious gauge-dependent error of ZORA. For benchmarks, we first study the hydrogen-like systems, in which both the original and scaled ZORA energies closely agree with the exact ZORA and Dirac results, respectively. Second, we calculate the atomic ionization potentials and electron affinities for the first two rows of elements. The ZORANet results show overall improvement compared to FermiNet, particularly for the second-row elements, with average deviations within chemical accuracy of the experimental values. For the dissociation energies of simple hydrides, atomic ZORANet again outperforms FermiNet, and reaches the same level of accuracy as state-of-the-art relativistic effective core potentials. Our results demonstrate that ZORANet is a highly accurate all-electron method capable of simultaneously treating electron correlation and relativistic effects, while maintaining computational costs comparable to FermiNet. Lastly, we give some prospects on the future development of ZORANet, which helps to further build it as a more integral ab initio relativistic quantum chemistry method.
{"title":"Relativistic Fermionic Neural Networks Based on Zeroth-Order Regular Approximation: ZORANet","authors":"Mengsa Wang,Yuzhi Zhou,Han Wang","doi":"10.1021/acs.jctc.5c01602","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c01602","url":null,"abstract":"In this paper, we introduce ZORANet, a Fermionic neural network framework for the relativistic calculations based on scalar zeroth-order regular approximation (ZORA), which greatly expands the scope of deep neural network-based electronic structure methods. To address the numerical difficulties specific to the relativistic calculations, the electron–nucleus cusp correction scheme and an importance sampling technique have been carefully implemented to ensure training stability and reduce statistical errors. In addition, two variants, namely, scaled ZORANet and atomic ZORANet, have been developed to mitigate the notorious gauge-dependent error of ZORA. For benchmarks, we first study the hydrogen-like systems, in which both the original and scaled ZORA energies closely agree with the exact ZORA and Dirac results, respectively. Second, we calculate the atomic ionization potentials and electron affinities for the first two rows of elements. The ZORANet results show overall improvement compared to FermiNet, particularly for the second-row elements, with average deviations within chemical accuracy of the experimental values. For the dissociation energies of simple hydrides, atomic ZORANet again outperforms FermiNet, and reaches the same level of accuracy as state-of-the-art relativistic effective core potentials. Our results demonstrate that ZORANet is a highly accurate all-electron method capable of simultaneously treating electron correlation and relativistic effects, while maintaining computational costs comparable to FermiNet. Lastly, we give some prospects on the future development of ZORANet, which helps to further build it as a more integral ab initio relativistic quantum chemistry method.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"395 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146044943","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 : 2026-01-26DOI: 10.1021/acs.jctc.5c01721
Ziyong Chen,Jonathan Lam,Vivian Wing-Wah Yam
Leveraging our recent development, which incorporates hole and particle information into the multi-channel molecular orbital image (MolOrbImage), to generate exceptional accuracy (mean absolute error, MAE < 0.1 eV) in predicting excited-state energies of practical photofunctional materials containing several hundred atoms, we have advanced the implementation of a new approach to overcome the high computational cost of mean-field ground-state calculations that limits its application in high-throughput materials discovery. In this work, low-cost approaches for generating approximate orbitals, including the superposition of atomic densities technique and the semiempirical tight-binding method, have been employed to construct cost-effective multi-channel MolOrbImages. By connecting with a convolutional neural network, the performance of our model is evaluated for both small organic molecules (MAE < 0.1 eV) and practical photofunctional materials (MAE < 0.14 eV). Perturbation analysis of MolOrbImages highlights the importance of frontier orbital energies, which further motivates the adoption of transfer learning techniques to reduce prediction errors in excited-state energies.
{"title":"Cost-Effective Multi-Channel MolOrbImage for Machine-Learned Excited-State Properties of Practical Photofunctional Materials.","authors":"Ziyong Chen,Jonathan Lam,Vivian Wing-Wah Yam","doi":"10.1021/acs.jctc.5c01721","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c01721","url":null,"abstract":"Leveraging our recent development, which incorporates hole and particle information into the multi-channel molecular orbital image (MolOrbImage), to generate exceptional accuracy (mean absolute error, MAE < 0.1 eV) in predicting excited-state energies of practical photofunctional materials containing several hundred atoms, we have advanced the implementation of a new approach to overcome the high computational cost of mean-field ground-state calculations that limits its application in high-throughput materials discovery. In this work, low-cost approaches for generating approximate orbitals, including the superposition of atomic densities technique and the semiempirical tight-binding method, have been employed to construct cost-effective multi-channel MolOrbImages. By connecting with a convolutional neural network, the performance of our model is evaluated for both small organic molecules (MAE < 0.1 eV) and practical photofunctional materials (MAE < 0.14 eV). Perturbation analysis of MolOrbImages highlights the importance of frontier orbital energies, which further motivates the adoption of transfer learning techniques to reduce prediction errors in excited-state energies.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"21 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146044693","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 : 2026-01-23DOI: 10.1021/acs.jctc.5c01745
Da Zheng,Junfeng Wang,Hongqiang Cui,Dinglin Zhang,Guohui Li
The crystallization kinetics of zeolites (e.g., ZSM-5) have scientific and industrial significance for chemical engineering. However, their nucleation mechanism at the microscopic level remains unclear because nucleation is the rare event in the complex multicomponent system. Here, we developed a coarse-grained reactive zeolite assembly model (CG-ReZAM) that captures the dynamic microscopic details of silicate polymerization with organic structure-directing agents (OSDAs). Taking the MFI-type zeolite as the case study, the four characteristic stages of silicate polymerization are reproduced: oligomerization, ring formation, cluster aggregation, and aging. We further introduced the cluster-weighted root-mean-square deviation (cRMSD) as a collective variable (CV) combined with the ratcheting scheme to enhance sampling of nucleation events. This approach accelerates the formation of ordered MFI-type zeolites within feasible time scales and reveals the full transformation pathway from amorphous aggregates to ordered crystals. During growth, we observed the ordered arrangements of tetrapropylammonium (TPA+) cations within the growing framework, confirming their structure-directing role in stabilizing long-range order. The critical nucleus size extracted from mean first-passage time (MFPT) analysis is consistent with experimental observations and further demonstrates the reliability of our method of combining our CG-ReZAM with an enhanced-sampling strategy for zeolite crystallization. Overall, this framework offers valuable theoretical insights into supporting the rational design of zeolite materials for applications in catalysis and separation.
{"title":"Combining Coarse-Grained Reactive Molecular Dynamics with an Enhanced-Sampling Method for MFI Zeolite Crystallization.","authors":"Da Zheng,Junfeng Wang,Hongqiang Cui,Dinglin Zhang,Guohui Li","doi":"10.1021/acs.jctc.5c01745","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c01745","url":null,"abstract":"The crystallization kinetics of zeolites (e.g., ZSM-5) have scientific and industrial significance for chemical engineering. However, their nucleation mechanism at the microscopic level remains unclear because nucleation is the rare event in the complex multicomponent system. Here, we developed a coarse-grained reactive zeolite assembly model (CG-ReZAM) that captures the dynamic microscopic details of silicate polymerization with organic structure-directing agents (OSDAs). Taking the MFI-type zeolite as the case study, the four characteristic stages of silicate polymerization are reproduced: oligomerization, ring formation, cluster aggregation, and aging. We further introduced the cluster-weighted root-mean-square deviation (cRMSD) as a collective variable (CV) combined with the ratcheting scheme to enhance sampling of nucleation events. This approach accelerates the formation of ordered MFI-type zeolites within feasible time scales and reveals the full transformation pathway from amorphous aggregates to ordered crystals. During growth, we observed the ordered arrangements of tetrapropylammonium (TPA+) cations within the growing framework, confirming their structure-directing role in stabilizing long-range order. The critical nucleus size extracted from mean first-passage time (MFPT) analysis is consistent with experimental observations and further demonstrates the reliability of our method of combining our CG-ReZAM with an enhanced-sampling strategy for zeolite crystallization. Overall, this framework offers valuable theoretical insights into supporting the rational design of zeolite materials for applications in catalysis and separation.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"3 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033996","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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