1
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Gutierrez-Cardenas J, Gibbas BD, Whitaker K, Kaledin M, Kaledin AL. A Low-Order Permutationally Invariant Polynomial Approach to Learning Potential Energy Surfaces Using the Bond-Order Charge-Density Matrix: Application to C n Clusters for n = 3-10, 20. J Phys Chem A 2024; 128:7703-7713. [PMID: 39205486 PMCID: PMC11407436 DOI: 10.1021/acs.jpca.4c04281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/04/2024]
Abstract
A representation for learning potential energy surfaces (PESs) in terms of permutationally invariant polynomials (PIPs) using the Hartree-Fock expression for electronic energy is proposed. Our approach is based on the one-electron core Hamiltonian weighted by the configuration-dependent elements of the bond-order charge density matrix (CDM). While the previously reported model used an s-function Gaussian basis for the CDM, the present formulation is expanded with p-functions, which are crucial for describing chemical bonding. Detailed results are demonstrated on linear and cyclic Cn clusters (n = 3-10) trained on extensive B3LYP/aug-cc-pVTZ data. The described method facilitates PES learning by reducing the root mean squared error (RMSE) by a factor of 5 relative to the s-function formulation and by a factor of 20 relative to the conventional PIP approach. This is equivalent to using CDM and an sp basis with a PIP of order M to achieve the same RMSE as with the conventional method with a PIP of order M + 2. Implications for large-scale problems are discussed using the case of the PES of the C20 fullerene in full permutational symmetry.
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Affiliation(s)
- Jose Gutierrez-Cardenas
- Department of Chemistry & Biochemistry, Kennesaw State University, 370 Paulding Ave NW ,Box#1203,Kennesaw 30144, Georgia
| | - Benjamin D Gibbas
- Department of Chemistry & Biochemistry, Kennesaw State University, 370 Paulding Ave NW ,Box#1203,Kennesaw 30144, Georgia
| | - Kyle Whitaker
- Department of Chemistry & Biochemistry, Kennesaw State University, 370 Paulding Ave NW ,Box#1203,Kennesaw 30144, Georgia
| | - Martina Kaledin
- Department of Chemistry & Biochemistry, Kennesaw State University, 370 Paulding Ave NW ,Box#1203,Kennesaw 30144, Georgia
| | - Alexey L Kaledin
- Cherry L. Emerson Center for Scientific Computation and Department of Chemistry, Emory University, 1515 Dickey Drive ,Atlanta 30322, Georgia
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2
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Schatz GC, Wodtke AM, Yang X. Spiers Memorial Lecture: New directions in molecular scattering. Faraday Discuss 2024; 251:9-62. [PMID: 38764350 DOI: 10.1039/d4fd00015c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/21/2024]
Abstract
The field of molecular scattering is reviewed as it pertains to gas-gas as well as gas-surface chemical reaction dynamics. We emphasize the importance of collaboration of experiment and theory, from which new directions of research are being pursued on increasingly complex problems. We review both experimental and theoretical advances that provide the modern toolbox available to molecular-scattering studies. We distinguish between two classes of work. The first involves simple systems and uses experiment to validate theory so that from the validated theory, one may learn far more than could ever be measured in the laboratory. The second class involves problems of great complexity that would be difficult or impossible to understand without a partnership of experiment and theory. Key topics covered in this review include crossed-beams reactive scattering and scattering at extremely low energies, where quantum effects dominate. They also include scattering from surfaces, reactive scattering and kinetics at surfaces, and scattering work done at liquid surfaces. The review closes with thoughts on future promising directions of research.
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Affiliation(s)
- George C Schatz
- Dept of Chemistry, Northwestern University, Evanston, Illinois 60208, USA
| | - Alec M Wodtke
- Institute for Physical Chemistry, Georg August University, Goettingen, Germany
- Max Planck Institute for Multidisciplinary Natural Sciences, Goettingen, Germany.
- International Center for the Advanced Studies of Energy Conversion, Georg August University, Goettingen, Germany
| | - Xueming Yang
- Dalian Institute for Chemical Physics, Chinese Academy of Sciences, Dalian, China
- Department of Chemistry, College of Science, Southern University of Science and Technology, Shenzhen, China
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3
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Liu Y, Ončák M, Meyer J, Ard SG, Shuman NS, Viggiano AA, Guo H. Multistate Dynamics and Kinetics of CO 2 Activation by Ta + in the Gas Phase: Insights into Single-Atom Catalysis. J Am Chem Soc 2024; 146:14182-14193. [PMID: 38741473 DOI: 10.1021/jacs.4c03192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
The activation of carbon dioxide (CO2) by a transition-metal cation in the gas phase is a unique model system for understanding single-atom catalysis. The mechanism of such reactions is often attributed to a "two-state reactivity" model in which the high-energy barrier of a spin state correlating with ground-state reactants is avoided by intersystem crossing (ISC) to a different spin state with a lower barrier. However, such a "spin-forbidden" mechanism, along with the corresponding dynamics, has seldom been rigorously examined theoretically, due to the lack of global potential energy surfaces (PESs). In this work, we report full-dimensional PESs of the lowest-lying quintet, triplet, and singlet states of the TaCO2+ system, machine-learned from first-principles data. These PESs and the corresponding spin-orbit couplings enable us to provide an extensive theoretical characterization of the dynamics and kinetics of the reaction between the tantalum cation (Ta+) and CO2, which have recently been investigated experimentally at high collision energies using crossed beams and velocity map imaging, as well as at thermal energies using a selected-ion flow tube apparatus. The multistate quasi-classical trajectory simulations with surface hopping reproduce most of the measured product translational and angular distributions, shedding valuable light on the nonadiabatic reaction dynamics. The calculated rate coefficients from 200 to 600 K are also in good agreement with the latest experimental measurements. More importantly, these calculations revealed that the reaction is controlled by intersystem crossing, rather than potential barriers.
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Affiliation(s)
- Yang Liu
- Department of Chemistry and Chemical Biology, Center for Computational Chemistry, University of New Mexico, Albuquerque, New Mexico 87131, United States
| | - Milan Ončák
- Institut für Ionenphysik und Angewandte Physik, Universität Innsbruck, Technikerstra. 25, 6020 Innsbruck, Austria
| | - Jennifer Meyer
- Fachbereich Chemie und Forschungszentrum OPTIMAS, RPTU Kaiserslautern-Landau, Erwin-Schrödinger Str. 52, 67663 Kaiserslautern, Germany
| | - Shaun G Ard
- Air Force Research Laboratory, Space Vehicles Directorate, Kirtland Air Force Base, New Mexico 87117, United States
| | - Nicholas S Shuman
- Air Force Research Laboratory, Space Vehicles Directorate, Kirtland Air Force Base, New Mexico 87117, United States
| | - Albert A Viggiano
- Air Force Research Laboratory, Space Vehicles Directorate, Kirtland Air Force Base, New Mexico 87117, United States
| | - Hua Guo
- Department of Chemistry and Chemical Biology, Center for Computational Chemistry, University of New Mexico, Albuquerque, New Mexico 87131, United States
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4
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Ge F, Wang R, Qu C, Zheng P, Nandi A, Conte R, Houston PL, Bowman JM, Dral PO. Tell Machine Learning Potentials What They Are Needed For: Simulation-Oriented Training Exemplified for Glycine. J Phys Chem Lett 2024; 15:4451-4460. [PMID: 38626460 DOI: 10.1021/acs.jpclett.4c00746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/18/2024]
Abstract
Machine learning potentials (MLPs) are widely applied as an efficient alternative way to represent potential energy surfaces (PESs) in many chemical simulations. The MLPs are often evaluated with the root-mean-square errors on the test set drawn from the same distribution as the training data. Here, we systematically investigate the relationship between such test errors and the simulation accuracy with MLPs on an example of a full-dimensional, global PES for the glycine amino acid. Our results show that the errors in the test set do not unambiguously reflect the MLP performance in different simulation tasks, such as relative conformer energies, barriers, vibrational levels, and zero-point vibrational energies. We also offer an easily accessible solution for improving the MLP quality in a simulation-oriented manner, yielding the most precise relative conformer energies and barriers. This solution also passed the stringent test by diffusion Monte Carlo simulations.
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Affiliation(s)
- Fuchun Ge
- State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China
| | - Ran Wang
- State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China
| | - Chen Qu
- Independent Researcher, Toronto, Ontario M9B0E3, Canada
| | - Peikun Zheng
- State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China
| | - Apurba Nandi
- Department of Chemistry and Cherry L. Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, United States
- Department of Physics and Materials Science, University of Luxembourg, Luxembourg City L-1511, Luxembourg
| | - Riccardo Conte
- Dipartimento di Chimica, Università degli Studi di Milano, via Golgi 19, 20133 Milano, Italy
| | - Paul L Houston
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United States
| | - Joel M Bowman
- Department of Chemistry and Cherry L. Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, United States
| | - Pavlo O Dral
- State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China
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5
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Houston PL, Qu C, Yu Q, Pandey P, Conte R, Nandi A, Bowman JM. No Headache for PIPs: A PIP Potential for Aspirin Runs Much Faster and with Similar Precision Than Other Machine-Learned Potentials. J Chem Theory Comput 2024; 20:3008-3018. [PMID: 38593438 DOI: 10.1021/acs.jctc.4c00054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/11/2024]
Abstract
Assessments of machine-learning (ML) potentials are an important aspect of the rapid development of this field. We recently reported an assessment of the linear-regression permutationally invariant polynomial (PIP) method for ethanol, using the widely used (revised) rMD17 data set. We demonstrated that the PIP approach outperformed numerous other methods, e.g., ANI, PhysNet, sGDML, and p-KRR, with respect to precision and notably with respect to speed [Houston et al., J. Chem. Phys. 2022, 156, 044120]. Here, we extend this assessment to the 21-atom aspirin molecule, using the rMD17 data set, with a focus on the speed of evaluation. Both energies and forces are used for training, and the precision of several PIPs is examined for both. Normal mode frequencies, the methyl torsional potential, and 1d vibrational energies for an OH stretch are presented. We show that the PIP approach achieves the level of precision obtained from other ML methods, e.g., atom-centered neural network methods, linear regression ACE, and kernel methods, as reported by Kovács et al. in J. Chem. Theory Comput. 2021, 17, 7696-7711. More significantly, we show that the PIP PESs run much faster than all other ML methods, whose timings were evaluated in that paper. We also show that the PIP PES extrapolates well enough to describe several internal motions of aspirin, including an OH stretch.
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Affiliation(s)
- Paul L Houston
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United States
- Department of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Chen Qu
- Independent Researcher, Toronto, Ontario M9B0E3, Canada
| | - Qi Yu
- Department of Chemistry, Fudan University, Shanghai 200438, P. R. China
| | - Priyanka Pandey
- Department of Chemistry, Cherry L. Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, United States
| | - Riccardo Conte
- Dipartimento di Chimica, Università degli Studi di Milano, via Golgi 19, 20133 Milano, Italy
| | - Apurba Nandi
- Department of Chemistry, Cherry L. Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, United States
- Department of Physics and Materials Science, University of Luxembourg, Luxembourg City L-1511, Luxembourg
| | - Joel M Bowman
- Department of Chemistry, Cherry L. Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, United States
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6
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Zhai Y, Rashmi R, Palos E, Paesani F. Many-body interactions and deep neural network potentials for water. J Chem Phys 2024; 160:144501. [PMID: 38587225 DOI: 10.1063/5.0203682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Accepted: 03/23/2024] [Indexed: 04/09/2024] Open
Abstract
We present a detailed assessment of deep neural network potentials developed within the Deep Potential Molecular Dynamics (DeePMD) framework and trained on the MB-pol data-driven many-body potential energy function. Specific focus is directed at the ability of DeePMD-based potentials to correctly reproduce the accuracy of MB-pol across various water systems. Analyses of bulk and interfacial properties as well as many-body interactions characteristic of water elucidate inherent limitations in the transferability and predictive accuracy of DeePMD-based potentials. These limitations can be traced back to an incomplete implementation of the "nearsightedness of electronic matter" principle, which may be common throughout machine learning potentials that do not include a proper representation of self-consistently determined long-range electric fields. These findings provide further support for the "short-blanket dilemma" faced by DeePMD-based potentials, highlighting the challenges in achieving a balance between computational efficiency and a rigorous, physics-based representation of the properties of water. Finally, we believe that our study contributes to the ongoing discourse on the development and application of machine learning models in simulating water systems, offering insights that could guide future improvements in the field.
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Affiliation(s)
- Yaoguang Zhai
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, USA
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, California 92093, USA
| | - Richa Rashmi
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, USA
| | - Etienne Palos
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, USA
| | - Francesco Paesani
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, USA
- Materials Science and Engineering, University of California San Diego, La Jolla, California 92093, USA
- Halicioğlu Data Science Institute, University of California San Diego, La Jolla, California 92093, USA
- San Diego Supercomputer Center, University of California San Diego, La Jolla, California 92093, USA
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7
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Iyengar SS, Ricard TC, Zhu X. Reformulation of All ONIOM-Type Molecular Fragmentation Approaches and Many-Body Theories Using Graph-Theory-Based Projection Operators: Applications to Dynamics, Molecular Potential Surfaces, Machine Learning, and Quantum Computing. J Phys Chem A 2024; 128:466-478. [PMID: 38180503 DOI: 10.1021/acs.jpca.3c05630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2024]
Abstract
We present a graph-theory-based reformulation of all ONIOM-based molecular fragmentation methods. We discuss applications to (a) accurate post-Hartree-Fock AIMD that can be conducted at DFT cost for medium-sized systems, (b) hybrid DFT condensed-phase studies at the cost of pure density functionals, (c) reduced cost on-the-fly large basis gas-phase AIMD and condensed-phase studies, (d) post-Hartree-Fock-level potential surfaces at DFT cost to obtain quantum nuclear effects, and (e) novel transfer machine learning protocols derived from these measures. Additionally, in previous work, the unifying strategy discussed here has been used to construct new quantum computing algorithms. Thus, we conclude that this reformulation is robust and accurate.
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Affiliation(s)
- Srinivasan S Iyengar
- Department of Chemistry, Department of Physics, and the Indiana University Quantum Science and Engineering Center (IU-QSEC), Indiana University, 800 E. Kirkwood Avenue, Bloomington, Indiana 47405, United States
| | - Timothy C Ricard
- Department of Chemistry, Department of Physics, and the Indiana University Quantum Science and Engineering Center (IU-QSEC), Indiana University, 800 E. Kirkwood Avenue, Bloomington, Indiana 47405, United States
| | - Xiao Zhu
- Department of Chemistry, Department of Physics, and the Indiana University Quantum Science and Engineering Center (IU-QSEC), Indiana University, 800 E. Kirkwood Avenue, Bloomington, Indiana 47405, United States
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8
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Manzhos S, Ihara M. Degeneration of kernel regression with Matern kernels into low-order polynomial regression in high dimension. J Chem Phys 2024; 160:021101. [PMID: 38189605 DOI: 10.1063/5.0187867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 12/17/2023] [Indexed: 01/09/2024] Open
Abstract
Kernel methods such as kernel ridge regression and Gaussian process regression with Matern-type kernels have been increasingly used, in particular, to fit potential energy surfaces (PES) and density functionals, and for materials informatics. When the dimensionality of the feature space is high, these methods are used with necessarily sparse data. In this regime, the optimal length parameter of a Matern-type kernel may become so large that the method effectively degenerates into a low-order polynomial regression and, therefore, loses any advantage over such regression. This is demonstrated theoretically as well as numerically in the examples of six- and fifteen-dimensional molecular PES using squared exponential and simple exponential kernels. The results shed additional light on the success of polynomial approximations such as PIP for medium-size molecules and on the importance of orders-of-coupling-based models for preserving the advantages of kernel methods with Matern-type kernels of on the use of physically motivated (reproducing) kernels.
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Affiliation(s)
- Sergei Manzhos
- School of Materials and Chemical Technology, Tokyo Institute of Technology, Ookayama 2-12-1, Meguro-ku, Tokyo 152-8552, Japan
| | - Manabu Ihara
- School of Materials and Chemical Technology, Tokyo Institute of Technology, Ookayama 2-12-1, Meguro-ku, Tokyo 152-8552, Japan
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9
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Fu B, Zhang DH. Accurate fundamental invariant-neural network representation of ab initio potential energy surfaces. Natl Sci Rev 2023; 10:nwad321. [PMID: 38274241 PMCID: PMC10808953 DOI: 10.1093/nsr/nwad321] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Revised: 11/01/2023] [Accepted: 11/02/2023] [Indexed: 01/27/2024] Open
Abstract
Highly accurate potential energy surfaces are critically important for chemical reaction dynamics. The large number of degrees of freedom and the intricate symmetry adaption pose a big challenge to accurately representing potential energy surfaces (PESs) for polyatomic reactions. Recently, our group has made substantial progress in this direction by developing the fundamental invariant-neural network (FI-NN) approach. Here, we review these advances, demonstrating that the FI-NN approach can represent highly accurate, global, full-dimensional PESs for reactive systems with even more than 10 atoms. These multi-channel reactions typically involve many intermediates, transition states, and products. The complexity and ruggedness of this potential energy landscape present even greater challenges for full-dimensional PES representation. These PESs exhibit a high level of complexity, molecular size, and accuracy of fit. Dynamics simulations based on these PESs have unveiled intriguing and novel reaction mechanisms, providing deep insights into the intricate dynamics involved in combustion, atmospheric, and organic chemistry.
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Affiliation(s)
- Bina Fu
- State Key Laboratory of Molecular Reaction Dynamics and Center for Theoretical and Computational Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
- Hefei National Laboratory, Hefei 230088, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Dong H Zhang
- State Key Laboratory of Molecular Reaction Dynamics and Center for Theoretical and Computational Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
- Hefei National Laboratory, Hefei 230088, China
- University of Chinese Academy of Sciences, Beijing 100049, China
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10
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Xia J, Zhang Y, Jiang B. Accuracy Assessment of Atomistic Neural Network Potentials: The Impact of Cutoff Radius and Message Passing. J Phys Chem A 2023; 127:9874-9883. [PMID: 37943102 DOI: 10.1021/acs.jpca.3c06024] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2023]
Abstract
Atomistic neural network potentials have achieved great success in accelerating atomistic simulations in complicated systems in recent years. They are typically based on the atomic decomposition of total properties, truncating the interatomic correlations to a local environment within a given cutoff radius. A more recently developed message passing (MP) neural network framework can, in principle, incorporate nonlocal effects through iteratively correlating some atoms outside the cutoff sphere with atoms inside, a process referred to as MP. However, how the model accuracy depends on the cutoff radius and the MP process has rarely been discussed. In this work, we investigate this dependence using a recursively embedded atom neural network method that possesses both local and MP features, in two representative systems: liquid H2O and solid Al2O3. We focus on how these settings influence predictions for structural and vibrational properties, namely, radial distribution functions (RDFs) and vibrational density of states (VDOSs). We find that while MP lowers test errors of energy and forces in general, it may not improve the prediction for RDFs and/or VDOSs if direct interatomic correlations in the local environment are insufficiently described. A cutoff radius exceeding the first neighbor shell is necessary, beyond which involving MP quickly enhances the model accuracy until convergence. This is a potentially more efficient way to increase the model accuracy than directly increasing the cutoff radius, especially with more memory savings in the GPU implementation. Our findings also suggest that using the mean test error as the measure of the model accuracy alone is inadequate.
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Affiliation(s)
- Junfan Xia
- Key Laboratory of Precision and Intelligent Chemistry, Department of Chemical Physics, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Yaolong Zhang
- École Polytechnique FFlytech de Lausanne, 1015 Lausanne, Switzerland
| | - Bin Jiang
- Key Laboratory of Precision and Intelligent Chemistry, Department of Chemical Physics, University of Science and Technology of China, Hefei, Anhui 230026, China
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11
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Liu Y, Guo H. A Gaussian Process Based Δ-Machine Learning Approach to Reactive Potential Energy Surfaces. J Phys Chem A 2023; 127:8765-8772. [PMID: 37815868 DOI: 10.1021/acs.jpca.3c05318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/12/2023]
Abstract
The Gaussian process (GP) is an efficient nonparametric machine learning (ML) method. A distinct advantage of the GP is its ability to provide an estimate of statistical uncertainties. This is particularly useful in constructing high-dimensional potential energy surfaces (PESs) from ab initio data as it offers an optimal way to add new geometries to reduce the overall error. In this work, GP is employed in the context of Δ-machine learning (Δ-ML), in which a correction PES to an inaccurate low-level PES is constructed using a small number of high-level ab initio calculations. This new method is tested in three prototypical reactive systems, namely, the H + H2 → H + H2, OH + H2 → H2O + H, and H + CH4 → H2 + CH3 reactions. The results show that the GP-based Δ-ML approach is more efficient than its direct application in constructing high-level PESs. We also compare the new method to a previously proposed neural-network-based Δ-ML approach [Liu and Li J. Phys. Chem. Lett. 2022, 13, 4729-4738]. The results indicate that the two Δ-ML methods have comparable efficiencies in constructing accurate PESs.
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Affiliation(s)
- Yang Liu
- Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131, United States
| | - Hua Guo
- Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131, United States
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12
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Song H, Guo H. Theoretical Insights into the Dynamics of Gas-Phase Bimolecular Reactions with Submerged Barriers. ACS PHYSICAL CHEMISTRY AU 2023; 3:406-418. [PMID: 37780541 PMCID: PMC10540288 DOI: 10.1021/acsphyschemau.3c00009] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 06/02/2023] [Accepted: 06/06/2023] [Indexed: 10/03/2023]
Abstract
Much attention has been paid to the dynamics of both activated gas-phase bimolecular reactions, which feature monotonically increasing integral cross sections and Arrhenius kinetics, and their barrierless capture counterparts, which manifest monotonically decreasing integral cross sections and negative temperature dependence of the rate coefficients. In this Perspective, we focus on the dynamics of gas-phase bimolecular reactions with submerged barriers, which often involve radicals or ions and are prevalent in combustion, atmospheric chemistry, astrochemistry, and plasma chemistry. The temperature dependence of the rate coefficients for such reactions is often non-Arrhenius and complex, and the corresponding dynamics may also be quite different from those with significant barriers or those completely dominated by capture. Recent experimental and theoretical studies of such reactions, particularly at relatively low temperatures or collision energies, have revealed interesting dynamical behaviors, which are discussed here. The new knowledge enriches our understanding of the dynamics of these unusual reactions.
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Affiliation(s)
- Hongwei Song
- State
Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Innovation Academy for Precision Measurement Science
and Technology, Chinese Academy of Sciences, Wuhan 430071, China
| | - Hua Guo
- Department
of Chemistry and Chemical Biology, University
of New Mexico, Albuquerque, New Mexico 87131, United States
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13
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Manzhos S, Ihara M. Neural Network with Optimal Neuron Activation Functions Based on Additive Gaussian Process Regression. J Phys Chem A 2023; 127:7823-7835. [PMID: 37698519 DOI: 10.1021/acs.jpca.3c02949] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/13/2023]
Abstract
Feed-forward neural networks (NNs) are a staple machine learning method widely used in many areas of science and technology, including physical chemistry, computational chemistry, and materials informatics. While even a single-hidden-layer NN is a universal approximator, its expressive power is limited by the use of simple neuron activation functions (such as sigmoid functions) that are typically the same for all neurons. More flexible neuron activation functions would allow the use of fewer neurons and layers and thereby save computational cost and improve expressive power. We show that additive Gaussian process regression (GPR) can be used to construct optimal neuron activation functions that are individual to each neuron. An approach is also introduced that avoids nonlinear fitting of neural network parameters by defining them with rules. The resulting method combines the advantage of robustness of a linear regression with the higher expressive power of an NN. We demonstrate the approach by fitting the potential energy surfaces of the water molecule and formaldehyde. Without requiring any nonlinear optimization, the additive-GPR-based approach outperforms a conventional NN in the high-accuracy regime, where a conventional NN suffers more from overfitting.
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Affiliation(s)
- Sergei Manzhos
- School of Materials and Chemical Technology, Tokyo Institute of Technology, Ookayama 2-12-1, Meguro-ku, Tokyo 152-8552, Japan
| | - Manabu Ihara
- School of Materials and Chemical Technology, Tokyo Institute of Technology, Ookayama 2-12-1, Meguro-ku, Tokyo 152-8552, Japan
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14
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Hashem Y, Foust K, Kaledin M, Kaledin AL. Fitting Potential Energy Surfaces by Learning the Charge Density Matrix with Permutationally Invariant Polynomials. J Chem Theory Comput 2023; 19:5690-5700. [PMID: 37561135 PMCID: PMC10501011 DOI: 10.1021/acs.jctc.3c00586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Indexed: 08/11/2023]
Abstract
The electronic energy in the Hartree-Fock (HF) theory is the trace of the product of the charge density matrix (CDM) with the one-electron and two-electron matrices represented in an atomic orbital basis, where the two-electron matrix is also a function of the same CDM. In this work, we examine a formalism of analytic representation of a generic molecular potential energy surface (PES) as a sum of a linearly parameterized HF and a correction term, the latter formally representing the electron correlation energy, also linearly parameterized, by expressing the elements of CDM using permutationally invariant polynomials (PIPs). We show on a variety of numerical examples, ranging from exemplary two-electron systems HeH+ and H3+ to the more challenging cases of methanium (CH5+) fragmentation and high-energy tautomerization of formamide to formimidic acid that such a formulation requires significantly fewer, 10-20% of PIPs, to accomplish the same accuracy of the fit as the conventional representation at practically the same computational cost.
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Affiliation(s)
- Younos Hashem
- Department
of Chemistry & Biochemistry, Kennesaw
State University, 370 Paulding Ave NW, Box # 1203, Kennesaw 30144, Georgia
| | - Katheryn Foust
- Department
of Chemistry & Biochemistry, Kennesaw
State University, 370 Paulding Ave NW, Box # 1203, Kennesaw 30144, Georgia
| | - Martina Kaledin
- Department
of Chemistry & Biochemistry, Kennesaw
State University, 370 Paulding Ave NW, Box # 1203, Kennesaw 30144, Georgia
| | - Alexey L. Kaledin
- Cherry
L. Emerson Center for Scientific Computation and Department of Chemistry, Emory University, 1515 Dickey Drive, Atlanta 30322, Georgia
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15
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Li J, Tu Z, Xiang H, Li Y, Song H. Theoretical studies on the kinetics and dynamics of the BeH + + H 2O reaction: comparison with the experiment. Phys Chem Chem Phys 2023; 25:20997-21005. [PMID: 37503894 DOI: 10.1039/d3cp02322b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
The reaction of BeH+ with background gaseous H2O may play a role in qubit loss for quantum information processing with Be+ as trapped ions, and yet its reaction mechanism has not been well understood until now. In this work, a globally accurate, full-dimensional ground-state potential energy surface (PES) for the BeH+ + H2O reaction was constructed by fitting a total of 170 438 ab initio energy points at the level of RCCSD(T)-F12/aug-cc-pVTZ using the fundamental invariant-neural network method. The total root-mean-square error of the final PES was 0.178 kcal mol-1. For comparison, quasi-classical trajectory calculations were carried out on the PES at an experimental temperature of 150 K. The obtained thermal rate constant and product branching ratio of the BeD+ + H2O reaction agreed quite well with experimental results. In addition, the vibrational state distributions and energy disposals of the products were calculated and rationalized using the sudden vector projection model.
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Affiliation(s)
- Jiaqi Li
- College of Physical Science and Technology, Huazhong Normal University, Wuhan 430079, China.
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430071, China.
| | - Zhao Tu
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430071, China.
- School of Chemical and Environmental Engineering, Hubei Minzu University, Enshi 445000, China
| | - Haipan Xiang
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430071, China.
- School of Physics and Electronics, Hunan University, Changsha 410082, China
| | - Yong Li
- College of Physical Science and Technology, Huazhong Normal University, Wuhan 430079, China.
| | - Hongwei Song
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430071, China.
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16
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Riera M, Knight C, Bull-Vulpe EF, Zhu X, Agnew H, Smith DGA, Simmonett AC, Paesani F. MBX: A many-body energy and force calculator for data-driven many-body simulations. J Chem Phys 2023; 159:054802. [PMID: 37526156 PMCID: PMC10550339 DOI: 10.1063/5.0156036] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 07/11/2023] [Indexed: 08/02/2023] Open
Abstract
Many-Body eXpansion (MBX) is a C++ library that implements many-body potential energy functions (PEFs) within the "many-body energy" (MB-nrg) formalism. MB-nrg PEFs integrate an underlying polarizable model with explicit machine-learned representations of many-body interactions to achieve chemical accuracy from the gas to the condensed phases. MBX can be employed either as a stand-alone package or as an energy/force engine that can be integrated with generic software for molecular dynamics and Monte Carlo simulations. MBX is parallelized internally using Open Multi-Processing and can utilize Message Passing Interface when available in interfaced molecular simulation software. MBX enables classical and quantum molecular simulations with MB-nrg PEFs, as well as hybrid simulations that combine conventional force fields and MB-nrg PEFs, for diverse systems ranging from small gas-phase clusters to aqueous solutions and molecular fluids to biomolecular systems and metal-organic frameworks.
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Affiliation(s)
- Marc Riera
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, USA
| | - Christopher Knight
- Argonne National Laboratory, Computational Science Division, Lemont, Illinois 60439, USA
| | - Ethan F. Bull-Vulpe
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, USA
| | - Xuanyu Zhu
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, USA
| | - Henry Agnew
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, USA
| | | | - Andrew C. Simmonett
- Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
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17
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Tang Z, Bromley ST, Hammer B. A machine learning potential for simulating infrared spectra of nanosilicate clusters. J Chem Phys 2023; 158:2895243. [PMID: 37290080 DOI: 10.1063/5.0150379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 05/23/2023] [Indexed: 06/10/2023] Open
Abstract
The use of machine learning (ML) in chemical physics has enabled the construction of interatomic potentials having the accuracy of ab initio methods and a computational cost comparable to that of classical force fields. Training an ML model requires an efficient method for the generation of training data. Here, we apply an accurate and efficient protocol to collect training data for constructing a neural network-based ML interatomic potential for nanosilicate clusters. Initial training data are taken from normal modes and farthest point sampling. Later on, the set of training data is extended via an active learning strategy in which new data are identified by the disagreement between an ensemble of ML models. The whole process is further accelerated by parallel sampling over structures. We use the ML model to run molecular dynamics simulations of nanosilicate clusters with various sizes, from which infrared spectra with anharmonicity included can be extracted. Such spectroscopic data are needed for understanding the properties of silicate dust grains in the interstellar medium and in circumstellar environments.
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Affiliation(s)
- Zeyuan Tang
- Center for Interstellar Catalysis, Department of Physics and Astronomy, Aarhus University, Ny Munkegade 120, Aarhus C 8000, Denmark
| | - Stefan T Bromley
- Departament de Ciència de Materials i Química Física and Institut de Química Teòrica i Computatcional (IQTCUB), Universitat de Barcelona, c/Martí i Franquès 1-11, 08028 Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluis Companys 23, 08010 Barcelona, Spain
| | - Bjørk Hammer
- Center for Interstellar Catalysis, Department of Physics and Astronomy, Aarhus University, Ny Munkegade 120, Aarhus C 8000, Denmark
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18
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Akher FB, Shu Y, Varga Z, Bhaumik S, Truhlar DG. Parametrically Managed Activation Function for Fitting a Neural Network Potential with Physical Behavior Enforced by a Low-Dimensional Potential. J Phys Chem A 2023. [PMID: 37307218 DOI: 10.1021/acs.jpca.3c02627] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Machine-learned representations of potential energy surfaces generated in the output layer of a feedforward neural network are becoming increasingly popular. One difficulty with neural network output is that it is often unreliable in regions where training data is missing or sparse. Human-designed potentials often build in proper extrapolation behavior by choice of functional form. Because machine learning is very efficient, it is desirable to learn how to add human intelligence to machine-learned potentials in a convenient way. One example is the well-understood feature of interaction potentials that they vanish when subsystems are too far separated to interact. In this article, we present a way to add a new kind of activation function to a neural network to enforce low-dimensional constraints. In particular, the activation function depends parametrically on all of the input variables. We illustrate the use of this step by showing how it can force an interaction potential to go to zero at large subsystem separations without either inputting a specific functional form for the potential or adding data to the training set in the asymptotic region of geometries where the subsystems are separated. In the process of illustrating this, we present an improved set of potential energy surfaces for the 14 lowest 3A' states of O3. The method is more general than this example, and it may be used to add other low-dimensional knowledge or lower-level knowledge to machine-learned potentials. In addition to the O3 example, we present a greater-generality method called parametrically managed diabatization by deep neural network (PM-DDNN) that is an improvement on our previously presented permutationally restrained diabatization by deep neural network (PR-DDNN).
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Affiliation(s)
- Farideh Badichi Akher
- Department of Chemistry and Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455-0431, United States
| | - Yinan Shu
- Department of Chemistry and Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455-0431, United States
| | - Zoltan Varga
- Department of Chemistry and Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455-0431, United States
| | - Suman Bhaumik
- Department of Chemistry and Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455-0431, United States
| | - Donald G Truhlar
- Department of Chemistry and Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455-0431, United States
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19
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Heindel JP, Herman KM, Xantheas SS. Many-Body Effects in Aqueous Systems: Synergies Between Interaction Analysis Techniques and Force Field Development. Annu Rev Phys Chem 2023; 74:337-360. [PMID: 37093659 DOI: 10.1146/annurev-physchem-062422-023532] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
Interaction analysis techniques, including the many-body expansion (MBE), symmetry-adapted perturbation theory, and energy decomposition analysis, allow for an intuitive understanding of complex molecular interactions. We review these methods by first providing a historical context for the study of many-body interactions and discussing how nonadditivities emerge from Hamiltonians containing strictly pairwise-additive interactions. We then elaborate on the synergy between these interaction analysis techniques and the development of advanced force fields aimed at accurately reproducing the Born-Oppenheimer potential energy surface. In particular, we focus on ab initio-based force fields that aim to explicitly reproduce many-body terms and are fitted to high-level electronic structure results. These force fields generally incorporate many-body effects through (a) parameterization of distributed multipoles, (b) explicit fitting of the MBE, (c) inclusion of many-atom features in a neural network, and (d) coarse-graining of many-body terms into an effective two-body term. We also discuss the emerging use of the MBE to improve the accuracy and speed of ab initio molecular dynamics.
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Affiliation(s)
- Joseph P Heindel
- Department of Chemistry, University of Washington, Seattle, Washington, USA
| | - Kristina M Herman
- Department of Chemistry, University of Washington, Seattle, Washington, USA
| | - Sotiris S Xantheas
- Department of Chemistry, University of Washington, Seattle, Washington, USA
- Advanced Computing, Mathematics and Data Division, Pacific Northwest National Laboratory, Richland, Washington, USA; ,
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20
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Yang Z, Chen H, Buren B, Chen M. Globally Accurate Gaussian Process Potential Energy Surface and Quantum Dynamics Studies on the Li( 2S) + Na 2 → LiNa + Na Reaction at Low Collision Energies. Molecules 2023; 28:2938. [PMID: 37049701 PMCID: PMC10096016 DOI: 10.3390/molecules28072938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 03/22/2023] [Accepted: 03/23/2023] [Indexed: 03/29/2023] Open
Abstract
The LiNa2 reactive system has recently received great attention in the experimental study of ultracold chemical reactions, but the corresponding theoretical calculations have not been carried out. Here, we report the first globally accurate ground-state LiNa2 potential energy surface (PES) using a Gaussian process model based on only 1776 actively selected high-level ab initio training points. The constructed PES had high precision and strong generalization capability. On the new PES, the quantum dynamics calculations on the Li(2S) + Na2(v = 0, j = 0) → LiNa + Na reaction were carried out in the 0.001-0.01 eV collision energy range using an improved time-dependent wave packet method. The calculated results indicate that this reaction is dominated by a complex-forming mechanism at low collision energies. The presented dynamics data provide guidance for experimental research, and the newly constructed PES could be further used for ultracold reaction dynamics calculations on this reactive system.
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Affiliation(s)
- Zijiang Yang
- Key Laboratory of Materials Modification by Laser, Electron, and Ion Beams (Ministry of Education), School of Physics, Dalian University of Technology, Dalian 116024, China
| | - Hanghang Chen
- Key Laboratory of Materials Modification by Laser, Electron, and Ion Beams (Ministry of Education), School of Physics, Dalian University of Technology, Dalian 116024, China
| | - Bayaer Buren
- School of Science, Shenyang University of Technology, Shenyang 110870, China
| | - Maodu Chen
- Key Laboratory of Materials Modification by Laser, Electron, and Ion Beams (Ministry of Education), School of Physics, Dalian University of Technology, Dalian 116024, China
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21
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Wang HD, Fu YL, Fu B, Fang W, Zhang DH. A highly accurate full-dimensional ab initio potential surface for the rearrangement of methylhydroxycarbene (H 3C-C-OH). Phys Chem Chem Phys 2023; 25:8117-8127. [PMID: 36876923 DOI: 10.1039/d3cp00312d] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Abstract
We report here a full-dimensional machine learning global potential surface (PES) for the rearrangement of methylhydroxycarbene (H3C-C-OH, 1t). The PES is trained with the fundamental invariant neural network (FI-NN) method on 91 564 ab initio energies calculated at the UCCSD(T)-F12a/cc-pVTZ level of theory, covering three possible product channels. FI-NN PES has the correct symmetry properties with respect to permutation of four identical hydrogen atoms and is suitable for dynamics studies of the 1t rearrangement. The averaged root mean square error (RMSE) is 11.4 meV. Six important reaction pathways, as well as the energies and vibrational frequencies at the stationary geometries on these pathways are accurately preproduced by our FI-NN PES. To demonstrate the capacity of the PES, we calculated the rate coefficient of hydrogen migration in -CH3 (path A) and hydrogen migration of -OH (path B) with instanton theory on this PES. Our calculations predicted the half-life of 1t to be 95 min, which is excellent in agreement with experimental observations.
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Affiliation(s)
- Heng-Ding Wang
- State Key Laboratory of Molecular Reaction Dynamics and Center for Theoretical and Computational Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China.
| | - Yan-Lin Fu
- State Key Laboratory of Molecular Reaction Dynamics and Center for Theoretical and Computational Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China.
| | - Bina Fu
- State Key Laboratory of Molecular Reaction Dynamics and Center for Theoretical and Computational Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China.
| | - Wei Fang
- Fudan University, Shanghai, 200032, China.
| | - Dong H Zhang
- State Key Laboratory of Molecular Reaction Dynamics and Center for Theoretical and Computational Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China.
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22
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Käser S, Vazquez-Salazar LI, Meuwly M, Töpfer K. Neural network potentials for chemistry: concepts, applications and prospects. DIGITAL DISCOVERY 2023; 2:28-58. [PMID: 36798879 PMCID: PMC9923808 DOI: 10.1039/d2dd00102k] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 12/20/2022] [Indexed: 12/24/2022]
Abstract
Artificial Neural Networks (NN) are already heavily involved in methods and applications for frequent tasks in the field of computational chemistry such as representation of potential energy surfaces (PES) and spectroscopic predictions. This perspective provides an overview of the foundations of neural network-based full-dimensional potential energy surfaces, their architectures, underlying concepts, their representation and applications to chemical systems. Methods for data generation and training procedures for PES construction are discussed and means for error assessment and refinement through transfer learning are presented. A selection of recent results illustrates the latest improvements regarding accuracy of PES representations and system size limitations in dynamics simulations, but also NN application enabling direct prediction of physical results without dynamics simulations. The aim is to provide an overview for the current state-of-the-art NN approaches in computational chemistry and also to point out the current challenges in enhancing reliability and applicability of NN methods on a larger scale.
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Affiliation(s)
- Silvan Käser
- Department of Chemistry, University of Basel Klingelbergstrasse 80 CH-4056 Basel Switzerland
| | | | - Markus Meuwly
- Department of Chemistry, University of Basel Klingelbergstrasse 80 CH-4056 Basel Switzerland
| | - Kai Töpfer
- Department of Chemistry, University of Basel Klingelbergstrasse 80 CH-4056 Basel Switzerland
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23
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Yang D, Guo H, Xie D. Recent advances in quantum theory on ro-vibrationally inelastic scattering. Phys Chem Chem Phys 2023; 25:3577-3594. [PMID: 36602236 DOI: 10.1039/d2cp05069b] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Molecular collisions are of fundamental importance in understanding intermolecular interaction and dynamics. Its importance is accentuated in cold and ultra-cold collisions because of the dominant quantum mechanical nature of the scattering. We review recent advances in the time-independent approach to quantum mechanical characterization of non-reactive scattering in tetratomic systems, which is ideally suited for large collisional de Broglie wavelengths characteristic in cold and ultracold conditions. We discuss quantum scattering algorithms between two diatoms and between a triatom and an atom and their implementation, as well as various approximate schemes. They not only enable the characterization of collision dynamics in realistic systems but also serve as benchmarks for developing more approximate methods.
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Affiliation(s)
- Dongzheng Yang
- Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131, USA.
| | - Hua Guo
- Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131, USA.
| | - Daiqian Xie
- Institute of Theoretical and Computational Chemistry, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China. .,Hefei National Laboratory, Hefei 230088, China
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24
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Houston PL, Qu C, Yu Q, Conte R, Nandi A, Li JK, Bowman JM. PESPIP: Software to fit complex molecular and many-body potential energy surfaces with permutationally invariant polynomials. J Chem Phys 2023; 158:044109. [PMID: 36725524 DOI: 10.1063/5.0134442] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
We wish to describe a potential energy surface by using a basis of permutationally invariant polynomials whose coefficients will be determined by numerical regression so as to smoothly fit a dataset of electronic energies as well as, perhaps, gradients. The polynomials will be powers of transformed internuclear distances, usually either Morse variables, exp(-ri,j/λ), where λ is a constant range hyperparameter, or reciprocals of the distances, 1/ri,j. The question we address is how to create the most efficient basis, including (a) which polynomials to keep or discard, (b) how many polynomials will be needed, (c) how to make sure the polynomials correctly reproduce the zero interaction at a large distance, (d) how to ensure special symmetries, and (e) how to calculate gradients efficiently. This article discusses how these questions can be answered by using a set of programs to choose and manipulate the polynomials as well as to write efficient Fortran programs for the calculation of energies and gradients. A user-friendly interface for access to monomial symmetrization approach results is also described. The software for these programs is now publicly available.
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Affiliation(s)
- Paul L Houston
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, USA and Department of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, USA
| | - Chen Qu
- Independent Researcher, Toronto, Ontario M9B0E3, Canada
| | - Qi Yu
- Department of Chemistry, Yale University, New Haven, Connecticut 06520, USA
| | - Riccardo Conte
- Dipartimento di Chimica, Università Degli Studi di Milano, Via Golgi 19, 20133 Milano, Italy
| | - Apurba Nandi
- Department of Chemistry and Cherry L. Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, USA
| | - Jeffrey K Li
- Department of Chemistry and Cherry L. Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, USA
| | - Joel M Bowman
- Department of Chemistry and Cherry L. Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, USA
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25
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Bowman JM, Qu C, Conte R, Nandi A, Houston PL, Yu Q. Δ-Machine Learned Potential Energy Surfaces and Force Fields. J Chem Theory Comput 2023; 19:1-17. [PMID: 36527383 DOI: 10.1021/acs.jctc.2c01034] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
There has been great progress in developing machine-learned potential energy surfaces (PESs) for molecules and clusters with more than 10 atoms. Unfortunately, this number of atoms generally limits the level of electronic structure theory to less than the "gold standard" CCSD(T) level. Indeed, for the well-known MD17 dataset for molecules with 9-20 atoms, all of the energies and forces were obtained with DFT calculations (PBE). This Perspective is focused on a Δ-machine learning method that we recently proposed and applied to bring DFT-based PESs to close to CCSD(T) accuracy. This is demonstrated for hydronium, N-methylacetamide, acetyl acetone, and ethanol. For 15-atom tropolone, it appears that special approaches (e.g., molecular tailoring, local CCSD(T)) are needed to obtain the CCSD(T) energies. A new aspect of this approach is the extension of Δ-machine learning to force fields. The approach is based on many-body corrections to polarizable force field potentials. This is examined in detail using the TTM2.1 water potential. The corrections make use of our recent CCSD(T) datasets for 2-b, 3-b, and 4-b interactions for water. These datasets were used to develop a new fully ab initio potential for water, termed q-AQUA.
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Affiliation(s)
- Joel M Bowman
- Department of Chemistry and Cherry L. Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, United States
| | - Chen Qu
- Independent Researcher, Toronto, Canada 66777
| | - Riccardo Conte
- Dipartimento di Chimica, Università Degli Studi di Milano, via Golgi 19, 20133 Milano, Italy
| | - Apurba Nandi
- Department of Chemistry and Cherry L. Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, United States
| | - Paul L Houston
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United States.,Department of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Qi Yu
- Department of Chemistry, Yale University, New Haven, Connecticut 06520, United States
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26
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Richardson JO. Machine learning of double-valued nonadiabatic coupling vectors around conical intersections. J Chem Phys 2023; 158:011102. [PMID: 36610946 DOI: 10.1063/5.0133191] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
In recent years, machine learning has had an enormous success in fitting ab initio potential-energy surfaces to enable efficient simulations of molecules in their ground electronic state. In order to extend this approach to excited-state dynamics, one must not only learn the potentials but also nonadiabatic coupling vectors (NACs). There is a particular difficulty in learning NACs in systems that exhibit conical intersections, as due to the geometric-phase effect, the NACs may be double-valued and are, thus, not suitable as training data for standard machine-learning techniques. In this work, we introduce a set of auxiliary single-valued functions from which the NACs can be reconstructed, thus enabling a reliable machine-learning approach.
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27
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Zuo J, Zhang D, Truhlar DG, Guo H. Global Potential Energy Surfaces by Compressed-State Multistate Pair-Density Functional Theory: The Lowest Doublet States Responsible for the N( 4S u) + C 2( a 3Π u) → CN( X 2Σ +) + C( 3P g) Reaction. J Chem Theory Comput 2022; 18:7121-7131. [PMID: 36383357 DOI: 10.1021/acs.jctc.2c00936] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Global potential energy surfaces (PESs) for the 1 2A' and 1 2A″ states of the C2N system responsible for the N(4Su) + C2(a 3Πu) → CN(X 2Σ+) + C(3Pg) reaction are mapped using compressed-state multistate pair-density functional theory (CMS-PDFT), which is a multi-state version of multiconfiguration pair-density functional theory (MC-PDFT). Calculations are also performed at selected geometries by explicitly correlated multireference configuration interaction with quadruple corrections, MRCI-F12+Q, and the comparison of the two sets of calculations shows that CMS-PDFT describes the globally reactive PESs well, including the bond-breaking asymptotes. We conclude that CMS-PDFT is an efficient method for constructing PESs for strongly correlated reactive systems. The PESs for producing CN + C are found to be barrierless and proceed through intermediate complexes. The CMS-PDFT PESs were fitted with a neural network method, and quasiclassical trajectories were computed on the resulting analytic PESs. These trajectories predict that the reaction produces vibrationally excited CN.
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Affiliation(s)
- Junxiang Zuo
- Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131, United States
| | - Dayou Zhang
- Department of Chemistry, Chemical Theory Center, and Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455-0431, United States
| | - Donald G Truhlar
- Department of Chemistry, Chemical Theory Center, and Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455-0431, United States
| | - Hua Guo
- Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131, United States
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28
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Zhang Y, Lin Q, Jiang B. Atomistic neural network representations for chemical dynamics simulations of molecular, condensed phase, and interfacial systems: Efficiency, representability, and generalization. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2022. [DOI: 10.1002/wcms.1645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Affiliation(s)
- Yaolong Zhang
- Department of Chemical Physics, School of Chemistry and Materials Science, Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes University of Science and Technology of China Hefei Anhui China
| | - Qidong Lin
- Department of Chemical Physics, School of Chemistry and Materials Science, Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes University of Science and Technology of China Hefei Anhui China
| | - Bin Jiang
- Department of Chemical Physics, School of Chemistry and Materials Science, Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes University of Science and Technology of China Hefei Anhui China
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29
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Conte R, Nandi A, Qu C, Yu Q, Houston PL, Bowman JM. Semiclassical and VSCF/VCI Calculations of the Vibrational Energies of trans- and gauche-Ethanol Using a CCSD(T) Potential Energy Surface. J Phys Chem A 2022; 126:7709-7718. [PMID: 36240438 DOI: 10.1021/acs.jpca.2c06322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
A recent full-dimensional Δ-Machine learning potential energy surface (PES) for ethanol is employed in semiclassical and vibrational self-consistent field (VSCF) and virtual-state configuration interaction (VCI) calculations, using MULTIMODE, to determine the anharmonic vibrational frequencies of vibration for both the trans and gauche conformers of ethanol. Both semiclassical and VSCF/VCI energies agree well with the experimental data. We find significant mixing between the VSCF basis states due to Fermi resonances between bending and stretching modes. The same effects are also accurately described by the full-dimensional semiclassical calculations. These are the first high-level anharmonic calculations using a PES, in particular a "gold-standard" CCSD(T) one.
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Affiliation(s)
- Riccardo Conte
- Dipartimento di Chimica, Università degli Studi di Milano, via Golgi 19, 20133 Milano, Italy
| | - Apurba Nandi
- Department of Chemistry and Cherry L. Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, United States
| | - Chen Qu
- Independent Researcher, Toronto, Ontario M9B0E3, Canada
| | - Qi Yu
- Department of Chemistry Yale University, New Haven, Connecticut 06520, United States
| | - Paul L Houston
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United States.,Department of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Joel M Bowman
- Department of Chemistry and Cherry L. Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, United States
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30
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Kuntz D, Wilson AK. Machine learning, artificial intelligence, and chemistry: how smart algorithms are reshaping simulation and the laboratory. PURE APPL CHEM 2022. [DOI: 10.1515/pac-2022-0202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Abstract
Machine learning and artificial intelligence are increasingly gaining in prominence through image analysis, language processing, and automation, to name a few applications. Machine learning is also making profound changes in chemistry. From revisiting decades-old analytical techniques for the purpose of creating better calibration curves, to assisting and accelerating traditional in silico simulations, to automating entire scientific workflows, to being used as an approach to deduce underlying physics of unexplained chemical phenomena, machine learning and artificial intelligence are reshaping chemistry, accelerating scientific discovery, and yielding new insights. This review provides an overview of machine learning and artificial intelligence from a chemist’s perspective and focuses on a number of examples of the use of these approaches in computational chemistry and in the laboratory.
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Affiliation(s)
- David Kuntz
- Department of Chemistry , University of North Texas , Denton , TX 76201 , USA
| | - Angela K. Wilson
- Department of Chemistry , Michigan State University , East Lansing , MI 48824 , USA
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31
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Xu X, Li J. Deciphering Dynamics of the Cl + SiH 4 → H + SiH 3Cl Reaction on a Machine Learning Made Globally Accurate Full-Dimensional Potential Energy Surface. J Phys Chem A 2022; 126:6456-6466. [PMID: 36084298 DOI: 10.1021/acs.jpca.2c05417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Chemical reaction dynamics needs the joint effort from both experiment and theory, and theory is useful to rationalize the experimental results by offering intimate details of chemical reaction dynamics and to explore new reaction pathways. With the aid of machine learning, we develop here an accurate full-dimensional potential energy surface (PES) for the reaction between Cl + SiH4. This PES can describe well the hydrogen abstraction channel to HCl + SiH3. It can also give a good description for the hydrogen substitution channel to H + SiH3Cl, which is the focus of the current study and has never been reported by theory. The dynamics of this substitution channel is revealed in detail by calculating ample quasi-classical trajectories (QCTs) on the new PES. The computed product angular distributions are in good agreement with the only crossed molecular beam experiment. Both theory and experiment suggest that this channel takes place mainly via the typical SN2 inversion mechanism. Theory reveals that there also exists a novel torsion mechanism for the substitution channel. Two dynamic mechanisms are analyzed in detail. The present detailed theoretical dynamics study sheds insightful and novel understanding for this fundamentally important chemical reaction.
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Affiliation(s)
- Xiaohu Xu
- School of Chemistry and Chemical Engineering & Chongqing Key Laboratory of Theoretical and Computational Chemistry, Chongqing University, Chongqing 401331, China
| | - Jun Li
- School of Chemistry and Chemical Engineering & Chongqing Key Laboratory of Theoretical and Computational Chemistry, Chongqing University, Chongqing 401331, China
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32
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Espinosa‐Garcia J. Kinetics study of the OH + SiH
4
hydrogen abstraction reaction: A theoretical analysis. INT J CHEM KINET 2022. [DOI: 10.1002/kin.21593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
- Joaquín Espinosa‐Garcia
- Departamento de Química Física and Instituto de Computación Científica Avanzada Universidad de Extremadura Badajoz Spain
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33
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Qin J, Liu Y, Li J. Quantitative Dynamics of Paradigmatic SN2 reaction OH− + CH3F on Accurate Full-Dimensional Potential Energy Surface. J Chem Phys 2022; 157:124301. [DOI: 10.1063/5.0112228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
The bimolecular reaction between OH− and CH3F is not just a prototypical SN2 process but also has three other product channels. Here, we develop an accurate full-dimensional potential energy surface (PES) based on 191 193 points calculated at the level CCSD(T)-F12a/aug-cc-pVTZ. A detailed dynamics and mechanism analysis were carried out on this PES by using the quasi-classical trajectory approach. It is verified that the trajectories do not follow the minimum energy path (MEP) but directly dissociate to F− and CH3OH. In addition, a new transition state for proton exchange and a new product complex CH2F−‧‧‧H2O for proton abstraction were discovered. The trajectories avoid the transition state or this complex, instead dissociate to H2O and CH2F− directly through the ridge regions of the MEP before the transition state. These non-MEP dynamics become more pronounced at high collision energies. Detailed dynamics simulations provide new insights into the atomic-level mechanisms of the title reaction thanks to the new chemically accurate PES with the aid of the machine learning.
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Affiliation(s)
- Jie Qin
- Chemistry and Chemical Engineering, Chongqing University Department of Chemical Engineering, China
| | | | - Jun Li
- School of Chemistry and Chemical Engineering, Chongqing University, China
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34
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Nandi A, Conte R, Qu C, Houston PL, Yu Q, Bowman JM. Quantum Calculations on a New CCSD(T) Machine-Learned Potential Energy Surface Reveal the Leaky Nature of Gas-Phase Trans and Gauche Ethanol Conformers. J Chem Theory Comput 2022; 18:5527-5538. [PMID: 35951990 PMCID: PMC9476654 DOI: 10.1021/acs.jctc.2c00760] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
![]()
Ethanol is a molecule of fundamental interest in combustion,
astrochemistry,
and condensed phase as a solvent. It is characterized by two methyl
rotors and trans (anti) and gauche conformers, which are known to be very close in energy.
Here we show that based on rigorous quantum calculations of the vibrational
zero-point state, using a new ab initio potential
energy surface (PES), the ground state resembles the trans conformer, but substantial delocalization to the gauche conformer is present. This explains experimental issues about identification
and isolation of the two conformers. This “leak” effect
is partially quenched when deuterating the OH group, which further
demonstrates the need for a quantum mechanical approach. Diffusion
Monte Carlo and full-dimensional semiclassical dynamics calculations
are employed. The new PES is obtained by means of a Δ-machine
learning approach starting from a pre-existing low level density functional
theory surface. This surface is brought to the CCSD(T) level of theory
using a relatively small number of ab initio CCSD(T)
energies. Agreement between the corrected PES and direct ab
initio results for standard tests is excellent. One- and
two-dimensional discrete variable representation calculations focusing
on the trans–gauche torsional
motion are also reported, in reasonable agreement with experiment.
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Affiliation(s)
- Apurba Nandi
- Department of Chemistry and Cherry L. Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, United States
| | - Riccardo Conte
- Dipartimento di Chimica, Università Degli Studi di Milano, via Golgi 19, 20133 Milano, Italy
| | - Chen Qu
- Independent Researcher, Toronto 66777, Canada
| | - Paul L Houston
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United States.,Department of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Qi Yu
- Department of Chemistry, Yale University, New Haven, Connecticut 06520, United States
| | - Joel M Bowman
- Department of Chemistry and Cherry L. Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, United States
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35
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Han S, Schröder M, Gatti F, Meyer HD, Lauvergnat D, Yarkony DR, Guo H. Representation of Diabatic Potential Energy Matrices for Multiconfiguration Time-Dependent Hartree Treatments of High-Dimensional Nonadiabatic Photodissociation Dynamics. J Chem Theory Comput 2022; 18:4627-4638. [PMID: 35839299 DOI: 10.1021/acs.jctc.2c00370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Conventional quantum mechanical characterization of photodissociation dynamics is restricted by steep scaling laws with respect to the dimensionality of the system. In this work, we examine the applicability of the multi-configurational time-dependent Hartree (MCTDH) method in treating nonadiabatic photodissociation dynamics in two prototypical systems, taking advantage of its favorable scaling laws. To conform to the sum-of-product form, elements of the ab initio diabatic potential energy matrix (DPEM) are re-expressed using the recently proposed Monte Carlo canonical polyadic decomposition method, with enforcement of proper symmetry. The MCTDH absorption spectra and product branching ratios are shown to compare well with those calculated using conventional grid-based methods, demonstrating its promise for treating high-dimensional nonadiabatic photodissociation problems.
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Affiliation(s)
- Shanyu Han
- Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131, United States
| | - Markus Schröder
- Theoretische Chemie, Physikalisch Chemisches Institut, Ruprecht-Karls Universität Heidelberg, D-69120 Heidelberg, Germany
| | - Fabien Gatti
- ISMO, Institut des Sciences Moléculaires d'Orsay─UMR 8214 CNRS/Université Paris-Saclay, F-91405 Orsay, France
| | - Hans-Dieter Meyer
- Theoretische Chemie, Physikalisch Chemisches Institut, Ruprecht-Karls Universität Heidelberg, D-69120 Heidelberg, Germany
| | - David Lauvergnat
- Université Paris-Saclay, CNRS, Institut de Chimie Physique UMR8000, Orsay 91405, France
| | - David R Yarkony
- Department of Chemistry, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Hua Guo
- Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131, United States
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36
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Li J, Lopez SA. A Look Inside the Black Box of Machine Learning Photodynamics Simulations. Acc Chem Res 2022; 55:1972-1984. [PMID: 35796602 DOI: 10.1021/acs.accounts.2c00288] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
ConspectusPhotochemical reactions are of great importance in chemistry, biology, and materials science because they take advantage of a renewable energy source, mild reaction conditions, and high atom economy. Light absorption can excite molecules to a higher energy electronic state of the same spin multiplicity. The following nonadiabatic processes induce molecular transformations that afford exotic molecular architectures and high-energy-isomers that are inaccessible by thermal means. Computational simulations now complement time-resolved instrumentation to reveal ultrafast excited-state mechanistic information for photochemical reactions that is essential in disentangling elusive spectroscopic features, excited-state lifetimes, and excited-state mechanistic critical points. Nonadiabatic molecular dynamics (NAMD), powered by surface hopping techniques, is among the most widely applied techniques to model the photochemical reactions of medium-sized molecules. However, the computational efficiency is limited because of the requisite thousands of multiconfigurational quantum-chemical calculations multiplied by hundreds of trajectories. Machine learning (ML) has emerged as a revolutionary force in computational chemistry to predict the outcome of the resource-intensive multiconfigurational calculations on the fly. An ML potential trained with a substantial set of quantum-chemical calculations can predict the energies and forces with errors under chemical accuracy at a negligible cost. The integration of ML potentials in NAMD dramatically extends the maximum simulation time scale by ∼10 000-fold to the nanosecond regime.In this Account, we present a comprehensive demonstration of ML photodynamics simulations and summarize our most recent applications in resolving complex photochemical reactions. First, we address three fundamental components of ML techniques for photodynamics simulations: the quantum-chemical data set, the ML potential, and NAMD. Second, we describe best practices in building training data and our procedure toward training the ML photodynamics model with our recent literature contributions. We introduce a convenient training data generation scheme combining Wigner sampling and geometrical interpolation. It trains reliable and effective ML potentials suitable for subsequent active learning to detect undersampled data. We demonstrate how active learning automatically discovers new mechanistic pathways and reproduces experimental results. We point out that atomic permutation is an essential data augmentation approach to improve the learnability of distance-based molecular descriptors for highly symmetric molecules. Third, we demonstrate the utility of ML-photodynamics by showing the results of ML photodynamics simulations of (1) photo-torquoselective 4π disrotatory electrocyclic ring closing of norbornyl cyclohexadiene, which reveals a thermal conversion from experimentally unobserved intermediates to the reactant in 1 ns; (2) [2 + 2] photocycloaddition of substituted [3]-syn-ladderdienes in competition with 4π and 6π electrocyclic ring-opening reactions, uncovering substituent effects to explain the reported increased quantum yield of substituted cubane precursors; and (3) photochemical 4π disrotatory electrocyclic reactions of fluorobenzenes in nanoseconds with XMS-CASPT2-level training data. We expect this Account to broaden understanding of ML photodynamics and inspire future developments and applications to increasingly large molecules within complex environments on long time scales.
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Affiliation(s)
- Jingbai Li
- Department of Chemistry and Chemical Biology, Northeastern University, Boston, Massachusetts 02115, United States
| | - Steven A Lopez
- Department of Chemistry and Chemical Biology, Northeastern University, Boston, Massachusetts 02115, United States
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37
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Meng Q, Chen J, Ma J, Zhang X, Chen J. Adiabatic models for the quantum dynamics of surface scattering with lattice effects. Phys Chem Chem Phys 2022; 24:16415-16436. [PMID: 35766107 DOI: 10.1039/d2cp01560a] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
In this contribution, we review models for the lattice effects in quantum dynamics calculations on surface scattering, which is important to modeling heterogeneous catalysis for achieving an interpretation of experimental measurements. Unlike dynamics models for reactions in the gas phase, those for heterogeneous reactions have to include the effects of the surface. For manageable computational costs in calculations, the effects of static surface (SS) are firstly modeled as this is simply and easily implemented. Then, the SS model has to be improved to include the effects of the flexible surface, that is the lattice effects. To do this, various surface models have been designed where the coordinates of the surface atoms are introduced in the Hamiltonian operator, especially those of the top surface atom. Based on this model Hamiltonian operator, extensive multi-dimension quantum dynamics calculations can be performed to recover the lattice effects. Here, we first review an overview of the techniques in constructing the Hamiltonian operator, which is a sum of the kinetic energy operator (KEO) and potential energy surface (PES). Since the PES containing the coordinates of the surface atoms in a cell is still expensive, the SS model is often accepted. We consider a mathematical model, called the coupled harmonic oscillator (CHO) model, to introduce the concepts of adiabatic and diabatic representations for separating the molecule and surface. Under the adiabatic model, we further introduce the expansion model where the potential function is Taylor expanded around the optimized geometry of the surface. By an expansion model truncated at the first and second order, various coupling surface models between the molecule and surface are derived. Moreover, by further and deeply understanding the adiabatic representation, an effective Hamiltonian operator is obtained by optimizing the total wave function in factorized form. By this factorized form of wave function and effective Hamiltonian operator, the geometry phase of the surface wave function is theoretically found. This theoretical prediction may be measured by carefully designing experiments. Finally, discussions on the adiabatic representation, the PES construction, and possibility of the classical-dynamics solutions are given. Based on these discussions, a simple outlook on the dynamics of photocatalytics is finally given.
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Affiliation(s)
- Qingyong Meng
- Department of Chemistry, Northwestern Polytechnical University, West Youyi Road 127, 710072 Xi'an, China.
| | - Junbo Chen
- Department of Chemistry, Northwestern Polytechnical University, West Youyi Road 127, 710072 Xi'an, China. .,Xi'an Modern Chemistry Research Institute, China North Industries Group Corp., Ltd., East Zhangba Road 168, 710065 Xi'an, China
| | - Jianxing Ma
- Department of Chemistry, Northwestern Polytechnical University, West Youyi Road 127, 710072 Xi'an, China.
| | - Xingyu Zhang
- Department of Chemistry, Northwestern Polytechnical University, West Youyi Road 127, 710072 Xi'an, China.
| | - Jun Chen
- State Key Laboratory of Structural Chemistry, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Yangqiao Road West 155, 350002 Fuzhou, China.,Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Optoelectronic Industry Base at High-tech Zone, 350108 Fuzhou, China
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38
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Bowman JM, Qu C, Conte R, Nandi A, Houston PL, Yu Q. The MD17 datasets from the perspective of datasets for gas-phase “small” molecule potentials. J Chem Phys 2022; 156:240901. [DOI: 10.1063/5.0089200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
There has been great progress in developing methods for machine-learned potential energy surfaces. There have also been important assessments of these methods by comparing so-called learning curves on datasets of electronic energies and forces, notably the MD17 database. The dataset for each molecule in this database generally consists of tens of thousands of energies and forces obtained from DFT direct dynamics at 500 K. We contrast the datasets from this database for three “small” molecules, ethanol, malonaldehyde, and glycine, with datasets we have generated with specific targets for the potential energy surfaces (PESs) in mind: a rigorous calculation of the zero-point energy and wavefunction, the tunneling splitting in malonaldehyde, and, in the case of glycine, a description of all eight low-lying conformers. We found that the MD17 datasets are too limited for these targets. We also examine recent datasets for several PESs that describe small-molecule but complex chemical reactions. Finally, we introduce a new database, “QM-22,” which contains datasets of molecules ranging from 4 to 15 atoms that extend to high energies and a large span of configurations.
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Affiliation(s)
- Joel M. Bowman
- Department of Chemistry and Cherry L. Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, USA
| | - Chen Qu
- Independent Researcher, Toronto, Canada
| | - Riccardo Conte
- Dipartimento di Chimica, Università Degli Studi di Milano, via Golgi 19, 20133 Milano, Italy
| | - Apurba Nandi
- Department of Chemistry and Cherry L. Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, USA
| | - Paul L. Houston
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, USA
- Department of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, USA
| | - Qi Yu
- Department of Chemistry, Yale University, New Haven, Connecticut 06520, USA
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39
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Omodemi O, Kaledin M, Kaledin AL. Permutationally invariant polynomial representation of polarizability tensor surfaces for linear regression analysis. J Comput Chem 2022; 43:1495-1503. [PMID: 35737590 DOI: 10.1002/jcc.26952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Accepted: 06/09/2022] [Indexed: 11/07/2022]
Abstract
A linearly parameterized functional form for a Cartesian representation of molecular dipole polarizability tensor surfaces (PTS) is described. The proposed expression for the PTS is a linearization of the recently reported power series ansatz of the original Applequist model, which by construction is non-linear in parameter space. This new approach possesses (i) a unique solution to the least-squares fitting problem; (ii) a low level of the computational complexity of the resulting linear regression procedure, comparable to those of the potential energy and dipole moment surfaces; and (iii) a competitive level of accuracy compared to the non-linear PTS model. Calculations of CH4 PTS, with polarizabilities fitted to 9000 training set points with the energies up to 14,000 cm-1 show an impressive level of accuracy of the linear PTS model obtained with ~1600 parameters: ~1% versus 0.3% RMSE for the non-linear vs. linear model on a test set of 1000 configurations.
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Affiliation(s)
- Oluwaseun Omodemi
- Department of Chemistry & Biochemistry, Kennesaw State University, Kennesaw, Georgia, USA
| | - Martina Kaledin
- Department of Chemistry & Biochemistry, Kennesaw State University, Kennesaw, Georgia, USA
| | - Alexey L Kaledin
- Cherry L. Emerson Center for Scientific Computation and Department of Chemistry, Emory University, Atlanta, Georgia, USA
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40
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Espinosa-Garcia J, Rangel C, Corchado JC. Current Status of the X + C 2H 6 [X ≡ H, F( 2P), Cl( 2P), O( 3P), OH] Hydrogen Abstraction Reactions: A Theoretical Review. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27123773. [PMID: 35744901 PMCID: PMC9228020 DOI: 10.3390/molecules27123773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 06/08/2022] [Accepted: 06/08/2022] [Indexed: 12/03/2022]
Abstract
This paper is a detailed review of the chemistry of medium-size reactive systems using the following hydrogen abstraction reactions with ethane, X + C2H6 → HX + C2H5; X ≡ H, F(2P), Cl(2P), O(3P) and OH, and focusing attention mainly on the theoretical developments. These bimolecular reactions range from exothermic to endothermic systems and from barrierless to high classical barriers of activation. Thus, the topography of the reactive systems changes from reaction to reaction with the presence or not of stabilized intermediate complexes in the entrance and exit channels. The review begins with some reflections on the inherent problems in the theory/experiment comparison. When one compares kinetics or dynamics theoretical results with experimental measures, one is testing both the potential energy surface describing the nuclei motion and the kinetics or dynamics method used. Discrepancies in the comparison may be due to inaccuracies of the surface, limitations of the kinetics or dynamics methods, and experimental uncertainties that also cannot be ruled out. The paper continues with a detailed review of some bimolecular reactions with ethane, beginning with the reactions with hydrogen atoms. The reactions with halogens present a challenge owing to the presence of stabilized intermediate complexes in the entrance and exit channels and the influence of the spin-orbit states on reactivity. Reactions with O(3P) atoms lead to three surfaces, which is an additional difficulty in the theoretical study. Finally, the reactions with the hydroxyl radical correspond to a reactive system with ten atoms and twenty-four degrees of freedom. Throughout this review, different strategies in the development of analytical potential energy surfaces describing these bimolecular reactions have been critically analyzed, showing their advantages and limitations. These surfaces are fitted to a large number of ab initio calculations, and we found that a huge number of calculations leads to accurate surfaces, but this information does not guarantee that the kinetics and dynamics results match the experimental measurements.
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41
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Liu Y, Li J. Permutation-Invariant-Polynomial Neural-Network-Based Δ-Machine Learning Approach: A Case for the HO 2 Self-Reaction and Its Dynamics Study. J Phys Chem Lett 2022; 13:4729-4738. [PMID: 35609295 DOI: 10.1021/acs.jpclett.2c01064] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Δ-machine learning, or the hierarchical construction scheme, is a highly cost-effective method, as only a small number of high-level ab initio energies are required to improve a potential energy surface (PES) fit to a large number of low-level points. However, there is no efficient and systematic way to select as few points as possible from the low-level data set. We here propose a permutation-invariant-polynomial neural-network (PIP-NN)-based Δ-machine learning approach to construct full-dimensional accurate PESs of complicated reactions efficiently. Particularly, the high flexibility of the NN is exploited to efficiently sample points from the low-level data set. This approach is applied to the challenging case of a HO2 self-reaction with a large configuration space. Only 14% of the DFT data set is used to successfully bring a newly fitted DFT PES to the UCCSD(T)-F12a/AVTZ quality. Then, the quasiclassical trajectory (QCT) calculations are performed to study its dynamics, particularly the mode specificity.
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Affiliation(s)
- Yang Liu
- School of Chemistry and Chemical Engineering & Chongqing Key Laboratory of Theoretical and Computational Chemistry, Chongqing University, Chongqing 401331, China
| | - Jun Li
- School of Chemistry and Chemical Engineering & Chongqing Key Laboratory of Theoretical and Computational Chemistry, Chongqing University, Chongqing 401331, China
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42
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Töpfer K, Upadhyay M, Meuwly M. Quantitative molecular simulations. Phys Chem Chem Phys 2022; 24:12767-12786. [PMID: 35593769 PMCID: PMC9158373 DOI: 10.1039/d2cp01211a] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Accepted: 04/30/2022] [Indexed: 11/21/2022]
Abstract
All-atom simulations can provide molecular-level insights into the dynamics of gas-phase, condensed-phase and surface processes. One important requirement is a sufficiently realistic and detailed description of the underlying intermolecular interactions. The present perspective provides an overview of the present status of quantitative atomistic simulations from colleagues' and our own efforts for gas- and solution-phase processes and for the dynamics on surfaces. Particular attention is paid to direct comparison with experiment. An outlook discusses present challenges and future extensions to bring such dynamics simulations even closer to reality.
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Affiliation(s)
- Kai Töpfer
- Department of Chemistry, University of Basel, Klingelbergstrasse 80, CH-4056 Basel, Switzerland.
| | - Meenu Upadhyay
- Department of Chemistry, University of Basel, Klingelbergstrasse 80, CH-4056 Basel, Switzerland.
| | - Markus Meuwly
- Department of Chemistry, University of Basel, Klingelbergstrasse 80, CH-4056 Basel, Switzerland.
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43
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DiRisio RJ, Finney JM, McCoy AB. Diffusion Monte Carlo approaches for studying nuclear quantum effects in fluxional molecules. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2022. [DOI: 10.1002/wcms.1615] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Ryan J. DiRisio
- Department of Chemistry University of Washington Seattle Washington USA
| | - Jacob M. Finney
- Department of Chemistry University of Washington Seattle Washington USA
| | - Anne B. McCoy
- Department of Chemistry University of Washington Seattle Washington USA
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Zhang Y, Xia J, Jiang B. REANN: A PyTorch-based end-to-end multi-functional deep neural network package for molecular, reactive, and periodic systems. J Chem Phys 2022; 156:114801. [DOI: 10.1063/5.0080766] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
In this work, we present a general purpose deep neural network package for representing energies, forces, dipole moments, and polarizabilities of atomistic systems. This so-called recursively embedded atom neural network model takes advantages of both the physically inspired atomic descriptor based neural networks and the message-passing based neural networks. Implemented in the PyTorch framework, the training process is parallelized on both the central processing unit and the graphics processing unit with high efficiency and low memory in which all hyperparameters can be optimized automatically. We demonstrate the state-of-the-art accuracy, high efficiency, scalability, and universality of this package by learning not only energies (with or without forces) but also dipole moment vectors and polarizability tensors in various molecular, reactive, and periodic systems. An interface between a trained model and LAMMPs is provided for large scale molecular dynamics simulations. We hope that this open-source toolbox will allow for future method development and applications of machine learned potential energy surfaces and quantum-chemical properties of molecules, reactions, and materials.
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Affiliation(s)
- Yaolong Zhang
- School of Chemistry and Materials Science, Department of Chemical Physics, Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Junfan Xia
- School of Chemistry and Materials Science, Department of Chemical Physics, Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Bin Jiang
- School of Chemistry and Materials Science, Department of Chemical Physics, Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, Anhui 230026, China
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Meuwly M. Atomistic Simulations for Reactions and Vibrational Spectroscopy in the Era of Machine Learning─ Quo Vadis?. J Phys Chem B 2022; 126:2155-2167. [PMID: 35286087 DOI: 10.1021/acs.jpcb.2c00212] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Atomistic simulations using accurate energy functions can provide molecular-level insight into functional motions of molecules in the gas and in the condensed phase. This Perspective delineates the present status of the field from the efforts of others and some of our own work and discusses open questions and future prospects. The combination of physics-based long-range representations using multipolar charge distributions and kernel representations for the bonded interactions is shown to provide realistic models for the exploration of the infrared spectroscopy of molecules in solution. For reactions, empirical models connecting dedicated energy functions for the reactant and product states allow statistically meaningful sampling of conformational space whereas machine-learned energy functions are superior in accuracy. The future combination of physics-based models with machine-learning techniques and integration into all-purpose molecular simulation software provides a unique opportunity to bring such dynamics simulations closer to reality.
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Affiliation(s)
- Markus Meuwly
- Department of Chemistry, University of Basel, Klingelbergstrasse 80, 4056 Basel, Switzerland
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Khire SS, Gurav ND, Nandi A, Gadre SR. Enabling Rapid and Accurate Construction of CCSD(T)-Level Potential Energy Surface of Large Molecules Using Molecular Tailoring Approach. J Phys Chem A 2022; 126:1458-1464. [PMID: 35170973 DOI: 10.1021/acs.jpca.2c00025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The construction of a potential energy surface (PES) of even a medium-sized molecule employing correlated theory, such as CCSD(T), is arduous due to the high computational cost involved. The present study reports the possibility of efficiently constructing such a PES of molecules containing up to 15 atoms and 550 basis functions by employing the fragment-based molecular tailoring approach (MTA) on off-the-shelf hardware. The MTA energies at the CCSD(T)/aug-cc-pVTZ level for several geometries of three test molecules, viz., acetylacetone, N-methylacetamide, and tropolone, are reported. These energies are in excellent agreement with their full calculation counterparts with a time advantage factor of 3-5. The energy barrier from the ground to transition state is also accurately captured. Further, we demonstrate the accuracy and efficiency of MTA for estimating the energy gradients at the CCSD(T) level. As a further application of our MTA methodology, the energies of acetylacetone at ∼430 geometries are computed at the CCSD(T)/aug-cc-pVTZ level and used for generating a Δ-machine learning (Δ-ML) PES. This leads to the H-transfer barrier of 3.02 kcal/mol, well in agreement with the benchmarked barrier of 3.19 kcal/mol. The fidelity of this Δ-ML PES is examined by geometry optimization and normal mode frequency calculations of global minima and saddle point geometries. We trust that the present work is a major development for the rapid and accurate construction of PES at the CCSD(T) level for molecules containing up to 20 atoms and 600 basis functions using off-the-shelf hardware.
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Affiliation(s)
- Subodh S Khire
- Department of Scientific Computing, Modelling and Simulation, Savitribai Phule Pune University, Pune 411 007, India
| | - Nalini D Gurav
- Department of Scientific Computing, Modelling and Simulation, Savitribai Phule Pune University, Pune 411 007, India
| | - Apurba Nandi
- Department of Chemistry and Cherry L. Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, United States
| | - Shridhar R Gadre
- Department of Scientific Computing, Modelling and Simulation, Savitribai Phule Pune University, Pune 411 007, India
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Houston PL, Qu C, Nandi A, Conte R, Yu Q, Bowman JM. Permutationally invariant polynomial regression for energies and gradients, using reverse differentiation, achieves orders of magnitude speed-up with high precision compared to other machine learning methods. J Chem Phys 2022; 156:044120. [DOI: 10.1063/5.0080506] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Affiliation(s)
- Paul L. Houston
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, USA and Department of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, USA
| | - Chen Qu
- Department of Chemistry and Biochemistry, University of Maryland, College Park, Maryland 20742, USA
| | - Apurba Nandi
- Department of Chemistry and Cherry L. Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, USA
| | - Riccardo Conte
- Dipartimento di Chimica, Università Degli Studi di Milano, via Golgi 19, 20133 Milano, Italy
| | - Qi Yu
- Department of Chemistry, Yale University, New Haven, Connecticut 06511, USA
| | - Joel M. Bowman
- Department of Chemistry and Cherry L. Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, USA
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Győri T, Czako G. ManyHF: A pragmatic automated method of finding lower-energy Hartree−Fock solutions for potential energy surface development. J Chem Phys 2022; 156:071101. [DOI: 10.1063/5.0080817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Tibor Győri
- Chemistry, University of Szeged Faculty of Science and Informatics, Hungary
| | - Gabor Czako
- Chemistry, University of Szeged Faculty of Science and Informatics, Hungary
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Li S, Liu Y, Chen D, Jiang Y, Nie Z, Pan F. Encoding the atomic structure for machine learning in materials science. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2022. [DOI: 10.1002/wcms.1558] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Shunning Li
- School of Advanced Materials Peking University, Shenzhen Graduate School Shenzhen China
| | - Yuanji Liu
- School of Advanced Materials Peking University, Shenzhen Graduate School Shenzhen China
| | - Dong Chen
- School of Advanced Materials Peking University, Shenzhen Graduate School Shenzhen China
| | - Yi Jiang
- School of Advanced Materials Peking University, Shenzhen Graduate School Shenzhen China
| | - Zhiwei Nie
- School of Advanced Materials Peking University, Shenzhen Graduate School Shenzhen China
| | - Feng Pan
- School of Advanced Materials Peking University, Shenzhen Graduate School Shenzhen China
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Yang Z, Chen H, Chen M. Representing Globally Accurate Reactive Potential Energy Surfaces with Complex Topography by Combining Gaussian Process Regression and Neural Network. Phys Chem Chem Phys 2022; 24:12827-12836. [DOI: 10.1039/d2cp00719c] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
There has been increasing attention in using machine learning technologies, such as neural network (NN) and Gaussian process regression (GPR), to model multidimensional potential energy surfaces (PESs). NN PES features...
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