1
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Zhang Y, Xu C, Lan Z. Automated Exploration of Reaction Networks and Mechanisms Based on Metadynamics Nanoreactor Simulations. J Chem Theory Comput 2023. [PMID: 38031422 DOI: 10.1021/acs.jctc.3c00752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2023]
Abstract
We developed an automated approach to construct a complex reaction network and explore the reaction mechanisms for numerous reactant molecules by integrating several theoretical approaches. Nanoreactor-type molecular dynamics was used to generate possible chemical reactions, in which the metadynamics was used to overcome the reaction barriers, and the semiempirical GFN2-xTB method was used to reduce the computational cost. Reaction events were identified from trajectories using the hidden Markov model based on the evolution of the molecular connectivity. This provided the starting points for further transition-state searches at the electronic structure levels of density functional theory to obtain the reaction mechanism. Finally, the entire reaction network containing multiple pathways was built. The feasibility and efficiency of the automated construction of the reaction network were investigated using the HCHO and NH3 biomolecular reaction and the reaction network for a multispecies system comprising dozens of HCN and H2O molecules. The results indicated that the proposed approach provides a valuable and effective tool for the automated exploration of the reaction networks.
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Affiliation(s)
- Yutai Zhang
- Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety and MOE Key Laboratory of Environmental Theoretical Chemistry, SCNU Environmental Research Institute, School of Environment, South China Normal University, Guangzhou 510006, P. R. China
| | - Chao Xu
- Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety and MOE Key Laboratory of Environmental Theoretical Chemistry, SCNU Environmental Research Institute, School of Environment, South China Normal University, Guangzhou 510006, P. R. China
| | - Zhenggang Lan
- Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety and MOE Key Laboratory of Environmental Theoretical Chemistry, SCNU Environmental Research Institute, School of Environment, South China Normal University, Guangzhou 510006, P. R. China
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2
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Kang PL, Yang ZX, Shang C, Liu ZP. Global Neural Network Potential with Explicit Many-Body Functions for Improved Descriptions of Complex Potential Energy Surface. J Chem Theory Comput 2023; 19:7972-7981. [PMID: 37856312 DOI: 10.1021/acs.jctc.3c00873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2023]
Abstract
The high dimensional machine learning potential (MLP) that has developed rapidly in the past decade represents a giant step forward in large-scale atomic simulation for complex systems. The long-range interaction and the poor description of chemical reactions are typical problems of high dimensional MLP, which are mainly caused by the poor structure discrimination of the atom-centered ML model. Herein, we propose a low-cost neural-network-based MLP architecture for fitting global potential energy surface data, namely, G-MBNN, that can offer improved energy and force resolution on a complex potential energy surface. In G-MBNN, a set of many-body energy terms based on the local atomic environment are explicitly included in computing the total energy─the total energy of the system is written as the sum of atomic energy and many-body energy contributions. These extra many-body energy terms are computationally low-cost and, importantly, can provide easy access to delicate energy terms in complex systems such as very short repulsion, long-range attractions, and sensitive angular-dependent covalent interactions. We implement G-MBNN in the LASP code and demonstrate the improved accuracy of the new framework in representative systems, including ternary-element energy materials LiCoOx, TiO2 with defects, and a series of organic reactions.
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Affiliation(s)
- Pei-Lin Kang
- Collaborative Innovation Center of Chemistry for Energy Material (iChem), Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University, Shanghai 200433, China
| | - Zheng-Xin Yang
- Collaborative Innovation Center of Chemistry for Energy Material (iChem), Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University, Shanghai 200433, China
| | - Cheng Shang
- Collaborative Innovation Center of Chemistry for Energy Material (iChem), Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University, Shanghai 200433, China
| | - Zhi-Pan Liu
- Collaborative Innovation Center of Chemistry for Energy Material (iChem), Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University, Shanghai 200433, China
- Key Laboratory of Synthetic and Self-Assembly Chemistry for Organic Functional Molecules, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 200032, China
- Shanghai Qi Zhi Institution, Shanghai 200030, China
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3
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Li G, Li Z, Gao L, Chen S, Wang G, Li S. Combined molecular dynamics and coordinate driving method for automatically searching complicated reaction pathways. Phys Chem Chem Phys 2023; 25:23696-23707. [PMID: 37610711 DOI: 10.1039/d3cp02443a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
The combined molecular dynamics and coordinate driving (MD/CD) method is updated and generalized in this work to broaden its applications in automatically searching reaction pathways for complicated reactions. In this updated version, MD simulations are performed with the GFN's family of methods to systematically sample conformers for almost any systems with atomic numbers Z ≤ 86. The improved CD procedure is greatly accelerated by applying a pre-screening stage at the semiempirical GFN2-xTB level. An automatic module based on the Marcus theory and its improved version (the Wolynes theory) is designed to include single electron transfer (SET) processes into reaction pathways. The capabilities of this method are demonstrated by exploring the most possible reaction pathways of three experimentally reported reactions: the organophosphine-catalyzed trans phosphinoboration, the Fe(II) complex-mediated C(sp2)-H borylation reaction, and the SET-triggered deaminative radical cross-coupling reaction. Comprehensive reaction networks are obtained for all three reactions with reasonable computational costs. Detailed mechanisms for these reactions can account for the reported experimental facts.
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Affiliation(s)
- Guoao Li
- Key Laboratory of Mesoscopic Chemistry of Ministry of Education, New Cornerstone Science Laboratory, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, People's Republic of China.
| | - Zhenxing Li
- Key Laboratory of Mesoscopic Chemistry of Ministry of Education, New Cornerstone Science Laboratory, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, People's Republic of China.
| | - Liuzhou Gao
- Key Laboratory of Mesoscopic Chemistry of Ministry of Education, New Cornerstone Science Laboratory, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, People's Republic of China.
| | - Shengda Chen
- Key Laboratory of Mesoscopic Chemistry of Ministry of Education, New Cornerstone Science Laboratory, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, People's Republic of China.
| | - Guoqiang Wang
- Key Laboratory of Mesoscopic Chemistry of Ministry of Education, New Cornerstone Science Laboratory, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, People's Republic of China.
| | - Shuhua Li
- Key Laboratory of Mesoscopic Chemistry of Ministry of Education, New Cornerstone Science Laboratory, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, People's Republic of China.
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4
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Bridging the complexity gap in computational heterogeneous catalysis with machine learning. Nat Catal 2023. [DOI: 10.1038/s41929-023-00911-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/03/2023]
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5
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Mita T, Takano H, Hayashi H, Kanna W, Harabuchi Y, Houk KN, Maeda S. Prediction of High-Yielding Single-Step or Cascade Pericyclic Reactions for the Synthesis of Complex Synthetic Targets. J Am Chem Soc 2022; 144:22985-23000. [PMID: 36451276 DOI: 10.1021/jacs.2c09830] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
Pericyclic reactions, which involve cyclic concerted transition states without ionic or radical intermediates, have been extensively studied since their definition in the 1960s, and the famous Woodward-Hoffmann rules predict their stereoselectivity and chemoselectivity. Here, we describe the application of a fully automated reaction-path search method, that is, the artificial force induced reaction (AFIR), to trace an input compound back to reasonable starting materials through thermally allowed pericyclic reactions via product-based quantum-chemistry-aided retrosynthetic analysis (QCaRA) without using any a priori experimental knowledge. All categories of pericyclic reactions, including cycloadditions, ene reactions, group-transfer, cheletropic, electrocyclic, and sigmatropic reactions, were successfully traced back via concerted reaction pathways, and starting materials were computationally obtained with the correct stereochemistry. Furthermore, AFIR was used to predict whether the identified reaction pathway can be expected to occur in good yield relative to other possible reactions of the identified starting material. In order to showcase its practical utility, this state-of-the-art technology was also applied to the retrosynthetic analysis of a natural product with a relatively high number of atoms (52 atoms: endiandric acid C methyl ester), which was first synthesized by Nicolaou in 1982 and provided the corresponding starting polyenes with the correct stereospecificity via three pericyclic reaction cascades (one Diels-Alder reaction as well as 6π and 8π electrocyclic reactions). Moreover, not only systems that obey the Woodward-Hoffmann rules but also systems that violate these rules, such as those recently calculated by Houk, can be retrosynthesized accurately.
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Affiliation(s)
- Tsuyoshi Mita
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Kita 21, Nishi 10, Kita-ku, Sapporo, Hokkaido 001-0021, Japan.,JST, ERATO Maeda Artificial Intelligence in Chemical Reaction Design and Discovery Project, Kita 10, Nishi 8, Kita-ku, Sapporo, Hokkaido 060-0810, Japan
| | - Hideaki Takano
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Kita 21, Nishi 10, Kita-ku, Sapporo, Hokkaido 001-0021, Japan.,JST, ERATO Maeda Artificial Intelligence in Chemical Reaction Design and Discovery Project, Kita 10, Nishi 8, Kita-ku, Sapporo, Hokkaido 060-0810, Japan
| | - Hiroki Hayashi
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Kita 21, Nishi 10, Kita-ku, Sapporo, Hokkaido 001-0021, Japan.,JST, ERATO Maeda Artificial Intelligence in Chemical Reaction Design and Discovery Project, Kita 10, Nishi 8, Kita-ku, Sapporo, Hokkaido 060-0810, Japan
| | - Wataru Kanna
- Department of Chemistry, Faculty of Science, Hokkaido University, Kita 10, Nishi 8, Kita-ku, Sapporo, Hokkaido 060-0810, Japan
| | - Yu Harabuchi
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Kita 21, Nishi 10, Kita-ku, Sapporo, Hokkaido 001-0021, Japan.,JST, ERATO Maeda Artificial Intelligence in Chemical Reaction Design and Discovery Project, Kita 10, Nishi 8, Kita-ku, Sapporo, Hokkaido 060-0810, Japan.,Department of Chemistry, Faculty of Science, Hokkaido University, Kita 10, Nishi 8, Kita-ku, Sapporo, Hokkaido 060-0810, Japan
| | - K N Houk
- Department of Chemical and Biomolecular Engineering and Department of Chemistry and Biochemistry, University of California Los Angeles, Los Angeles, California 90095, United States
| | - Satoshi Maeda
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Kita 21, Nishi 10, Kita-ku, Sapporo, Hokkaido 001-0021, Japan.,JST, ERATO Maeda Artificial Intelligence in Chemical Reaction Design and Discovery Project, Kita 10, Nishi 8, Kita-ku, Sapporo, Hokkaido 060-0810, Japan.,Department of Chemistry, Faculty of Science, Hokkaido University, Kita 10, Nishi 8, Kita-ku, Sapporo, Hokkaido 060-0810, Japan.,Research and Services Division of Materials Data and Integrated System (MaDIS), National Institute for Materials Science (NIMS), Tsukuba, Ibaraki 305-0044, Japan
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7
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Kang PL, Shi YF, Shang C, Liu ZP. Artificial intelligence pathway search to resolve catalytic glycerol hydrogenolysis selectivity. Chem Sci 2022; 13:8148-8160. [PMID: 35919423 PMCID: PMC9278456 DOI: 10.1039/d2sc02107b] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 06/20/2022] [Indexed: 11/29/2022] Open
Abstract
The complex interaction between molecules and catalyst surfaces leads to great difficulties in understanding and predicting the activity and selectivity in heterogeneous catalysis. Here we develop an end-to-end artificial intelligence framework for the activity prediction of heterogeneous catalytic systems (AI-Cat method), which takes simple inputs from names of molecules and metal catalysts and outputs the reaction energy profile from the input molecule to low energy pathway products. The AI-Cat method combines two neural network models, one for predicting reaction patterns and the other for providing the reaction barrier and energy, with a Monte Carlo tree search to resolve the low energy pathways in a reaction network. We then apply AI-Cat to resolve the reaction network of glycerol hydrogenolysis on Cu surfaces, which is a typical selective C-O bond activation system and of key significance for biomass-derived polyol utilization. We show that glycerol hydrogenolysis features a huge reaction network of relevant candidates, containing 420 reaction intermediates and 2467 elementary reactions. Among them, the surface-mediated enol-keto tautomeric resonance is a key step to facilitate the primary C-OH bond breaking and thus selects 1,2-propanediol as the major product on Cu catalysts. 1,3-Propanediol can only be produced under strong acidic conditions and high surface H coverage by following a hydrogenation-dehydration pathway. AI-Cat further discovers six low-energy reaction patterns for C-O bond activation on metals that is of general significance to polyol catalysis. Our results demonstrate that the reaction prediction for complex heterogeneous catalysis is now feasible with AI-based atomic simulation and a Monte Carlo tree search.
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Affiliation(s)
- Pei-Lin Kang
- Collaborative Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University Shanghai 200433 China
| | - Yun-Fei Shi
- Collaborative Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University Shanghai 200433 China
| | - Cheng Shang
- Collaborative Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University Shanghai 200433 China
- Shanghai Qi Zhi Institution Shanghai 200030 China
| | - Zhi-Pan Liu
- Collaborative Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University Shanghai 200433 China
- Shanghai Qi Zhi Institution Shanghai 200030 China
- Key Laboratory of Synthetic and Self-Assembly Chemistry for Organic Functional Molecules, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences Shanghai 200032 China
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8
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Zeolite-confined subnanometric PtSn mimicking mortise-and-tenon joinery for catalytic propane dehydrogenation. Nat Commun 2022; 13:2716. [PMID: 35581210 PMCID: PMC9114386 DOI: 10.1038/s41467-022-30522-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 04/28/2022] [Indexed: 11/08/2022] Open
Abstract
Heterogeneous catalysts are often composite materials synthesized via several steps of chemical transformation, and thus the atomic structure in composite is a black-box. Herein with machine-learning-based atomic simulation we explore millions of structures for MFI zeolite encapsulated PtSn catalyst, demonstrating that the machine-learning enhanced large-scale potential energy surface scan offers a unique route to connect the thermodynamics and kinetics within catalysts' preparation procedure. The functionalities of the two stages in catalyst preparation are now clarified, namely, the oxidative clustering and the reductive transformation, which form separated Sn4O4 and PtSn alloy clusters in MFI. These confined clusters have high thermal stability at the intersection voids of MFI because of the formation of "Mortise-and-tenon Joinery". Among, the PtSn clusters with high Pt:Sn ratios (>1:1) are active for propane dehydrogenation to propene, ∼103 in turnover-of-frequency greater than conventional Pt3Sn metal. Key recipes to optimize zeolite-confined metal catalysts are predicted.
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9
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Garay-Ruiz D, Álvarez-Moreno M, Bo C, Martínez-Núñez E. New Tools for Taming Complex Reaction Networks: The Unimolecular Decomposition of Indole Revisited. ACS PHYSICAL CHEMISTRY AU 2022; 2:225-236. [PMID: 36855573 PMCID: PMC9718323 DOI: 10.1021/acsphyschemau.1c00051] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The level of detail attained in the computational description of reaction mechanisms can be vastly improved through tools for automated chemical space exploration, particularly for systems of small to medium size. Under this approach, the unimolecular decomposition landscape for indole was explored through the automated reaction mechanism discovery program AutoMeKin. Nevertheless, the sheer complexity of the obtained mechanisms might be a hindrance regarding their chemical interpretation. In this spirit, the new Python library amk-tools has been designed to read and manipulate complex reaction networks, greatly simplifying their overall analysis. The package provides interactive dashboards featuring visualizations of the network, the three-dimensional (3D) molecular structures and vibrational normal modes of all chemical species, and the corresponding energy profiles for selected pathways. The combination of the joined mechanism generation and postprocessing workflow with the rich chemistry of indole decomposition enabled us to find new details of the reaction (obtained at the CCSD(T)/aug-cc-pVTZ//M06-2X/MG3S level of theory) that were not reported before: (i) 16 pathways leading to the formation of HCN and NH3 (via amino radical); (ii) a barrierless reaction between methylene radical and phenyl isocyanide, which might be an operative mechanism under the conditions of the interstellar medium; and (iii) reaction channels leading to both hydrogen cyanide and hydrogen isocyanide, of potential astrochemical interest as the computed HNC/HCN ratios greatly exceed the calculated equilibrium value at very low temperatures. The reported reaction networks can be very valuable to supplement databases of kinetic data, which is of remarkable interest for pyrolysis and astrochemical studies.
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Affiliation(s)
- Diego Garay-Ruiz
- Institute
of Chemical Research of Catalonia (ICIQ), Barcelona Institute of Science & Technology (BIST), Avinguda Països Catalans,
16, 43007 Tarragona, Spain,Departament
de Química Física i Inorgànica, Universitat Rovira i Virgili (URV), Marcel·lí Domingo s/n, 43007 Tarragona, Spain
| | - Moises Álvarez-Moreno
- Institute
of Chemical Research of Catalonia (ICIQ), Barcelona Institute of Science & Technology (BIST), Avinguda Països Catalans,
16, 43007 Tarragona, Spain
| | - Carles Bo
- Institute
of Chemical Research of Catalonia (ICIQ), Barcelona Institute of Science & Technology (BIST), Avinguda Països Catalans,
16, 43007 Tarragona, Spain,Departament
de Química Física i Inorgànica, Universitat Rovira i Virgili (URV), Marcel·lí Domingo s/n, 43007 Tarragona, Spain,
| | - Emilio Martínez-Núñez
- Departmento
de Química Física, Facultade de Química, Universidade de Santiago de Compostela, 15782 Santiago
de Compostela, Spain,
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10
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Cui Q, Peng J, Xu C, Lan Z. Automatic Approach to Explore the Multireaction Mechanism for Medium-Sized Bimolecular Reactions via Collision Dynamics Simulations and Transition State Searches. J Chem Theory Comput 2022; 18:910-924. [DOI: 10.1021/acs.jctc.1c00795] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Qinghai Cui
- Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety and MOE Key Laboratory of Environmental Theoretical Chemistry, SCNU Environmental Research Institute, School of Environment, South China Normal University, Guangzhou 510006, P. R. China
| | - Jiawei Peng
- Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety and MOE Key Laboratory of Environmental Theoretical Chemistry, SCNU Environmental Research Institute, School of Environment, South China Normal University, Guangzhou 510006, P. R. China
| | - Chao Xu
- Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety and MOE Key Laboratory of Environmental Theoretical Chemistry, SCNU Environmental Research Institute, School of Environment, South China Normal University, Guangzhou 510006, P. R. China
- Key Laboratory of Theoretical Chemistry of Environment, Ministry of Education; School of Chemistry, South China Normal University, Guangzhou 510006, P. R. China
| | - Zhenggang Lan
- Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety and MOE Key Laboratory of Environmental Theoretical Chemistry, SCNU Environmental Research Institute, School of Environment, South China Normal University, Guangzhou 510006, P. R. China
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11
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Jiao F, Huang X, Zhang C, Xie W. High-pressure phases of a Mn-N system. Phys Chem Chem Phys 2022; 24:1830-1839. [PMID: 34986210 DOI: 10.1039/d1cp04386b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Highly compressed extended states of light elemental solids have emerged recently as a novel group of energetic materials. The application of these materials is seriously limited by the energy-safety contradiction, because the material with high energy density is highly metastable and can hardly be recovered under ambient conditions. Recently, it has been found that high-energy density transition metal polynitrides could be synthesized at ∼100 GPa and recovered at ∼20 GPa. Inspired by these findings, we have studied a high-pressure Mn-N system from the aspects of structure, stability, phase transition, energy density and electronic structure theoretically for the first time. The results reveal that MnN4_P1̄ consisting of [N4]∞2- is thermodynamically stable at 36.9-100 GPa, dynamically stable at 0 GPa and has a noticeably high volumetric energy density of 15.71 kJ cm-3. Upon decompression, this structure will transform to MnN4_C2/m with the transition barrier declining sharply at 5-10 GPa due to the switching of transition pathways. Hence, we propose MnN4_P1̄ as a potential energetic material that is synthesizable above 40 GPa and recoverable until 10 GPa.
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Affiliation(s)
- Fangbao Jiao
- Institute of Chemical Materials, China Academy of Engineering Physics, P. O. Box 919-311, Mianyang, Sichuan, 621999, China.
| | - Xin Huang
- Institute of Chemical Materials, China Academy of Engineering Physics, P. O. Box 919-311, Mianyang, Sichuan, 621999, China. .,School of Physics and Optoelectronics, Xiangtan University, Xiangtan, Hunan, 411105, China
| | - Chaoyang Zhang
- Institute of Chemical Materials, China Academy of Engineering Physics, P. O. Box 919-311, Mianyang, Sichuan, 621999, China.
| | - Weiyu Xie
- Institute of Chemical Materials, China Academy of Engineering Physics, P. O. Box 919-311, Mianyang, Sichuan, 621999, China.
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12
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Rice PS, Liu ZP, Hu P. Hydrogen Coupling on Platinum Using Artificial Neural Network Potentials and DFT. J Phys Chem Lett 2021; 12:10637-10645. [PMID: 34704763 DOI: 10.1021/acs.jpclett.1c02998] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
To date, the understanding of reactions at solid-liquid interfaces has proven challenging, mainly because of the inaccessible nature of such systems to current experimental techniques with atomic resolution. This has meant that many important features, including free energy barriers and the atomistic structure of intermediates, remain unknown. To tackle these issues, we construct and utilize a high-dimensional neural network (HDNN) potential for the simulation of hydrogen evolution at the HCl(aq)/Pt(111) interface, taking into consideration the influence of adsorbate-adsorbate, adsorbate-solvent interactions, and ion solvation explicitly. Long time scale MD simulations reveal coadsorbed Had/H2Oad on the surface. The free energy profiles for the Tafel and Heyrovsky type hydrogen coupling are extracted using umbrella sampling. It is found that the preferential mechanism can change depending on the surface coverage, highlighting the dual mechanistic nature for HER on Pt(111). Our work demonstrates the importance of controlling the solvent-substrate interactions in developing catalysts beyond Pt.
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Affiliation(s)
- Peter S Rice
- School of Chemistry and Chemical Engineering, The Queen's University of Belfast, Belfast BT9 5AG, Northern Ireland
| | - Zhi-Pan Liu
- Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Department of Chemistry, Key Laboratory of Computational Physical Science (Ministry of Education), Fudan University, Shanghai 200433, China
| | - P Hu
- School of Chemistry and Chemical Engineering, The Queen's University of Belfast, Belfast BT9 5AG, Northern Ireland
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13
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Martínez-Núñez E, Barnes GL, Glowacki DR, Kopec S, Peláez D, Rodríguez A, Rodríguez-Fernández R, Shannon RJ, Stewart JJP, Tahoces PG, Vazquez SA. AutoMeKin2021: An open-source program for automated reaction discovery. J Comput Chem 2021; 42:2036-2048. [PMID: 34387374 DOI: 10.1002/jcc.26734] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 07/16/2021] [Accepted: 07/27/2021] [Indexed: 01/10/2023]
Abstract
AutoMeKin2021 is an updated version of tsscds2018, a program for the automated discovery of reaction mechanisms (J. Comput. Chem. 2018, 39, 1922). This release features a number of new capabilities: rare-event molecular dynamics simulations to enhance reaction discovery, extension of the original search algorithm to study van der Waals complexes, use of chemical knowledge, a new search algorithm based on bond-order time series analysis, statistics of the chemical reaction networks, a web application to submit jobs, and other features. The source code, manual, installation instructions and the website link are available at: https://rxnkin.usc.es/index.php/AutoMeKin.
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Affiliation(s)
- Emilio Martínez-Núñez
- Department of Physical Chemistry, University of Santiago de Compostela, Santiago de Compostela, Spain
| | - George L Barnes
- Department of Chemistry and Biochemistry, Siena College, Loudonville, New York, USA
| | - David R Glowacki
- Centre for Computational Chemistry, School of Chemistry, University of Bristol, Bristol, UK
| | - Sabine Kopec
- Institut de Sciences Moléculaires d'Orsay, UMR 8214, Université Paris-Sud - Université Paris-Saclay, Orsay, France
| | - Daniel Peláez
- Institut de Sciences Moléculaires d'Orsay, UMR 8214, Université Paris-Sud - Université Paris-Saclay, Orsay, France
| | - Aurelio Rodríguez
- Galicia Supercomputing Center (CESGA), Santiago de Compostela, Spain
| | | | - Robin J Shannon
- Centre for Computational Chemistry, School of Chemistry, University of Bristol, Bristol, UK
| | | | - Pablo G Tahoces
- Department of Electronics and Computer Science, University of Santiago de Compostela, Santiago de Compostela, Spain
| | - Saulo A Vazquez
- Department of Physical Chemistry, University of Santiago de Compostela, Santiago de Compostela, Spain
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14
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Xie X, Clark Spotte-Smith EW, Wen M, Patel HD, Blau SM, Persson KA. Data-Driven Prediction of Formation Mechanisms of Lithium Ethylene Monocarbonate with an Automated Reaction Network. J Am Chem Soc 2021; 143:13245-13258. [PMID: 34379977 DOI: 10.1021/jacs.1c05807] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Interfacial reactions are notoriously difficult to characterize, and robust prediction of the chemical evolution and associated functionality of the resulting surface film is one of the grand challenges of materials chemistry. The solid-electrolyte interphase (SEI), critical to Li-ion batteries (LIBs), exemplifies such a surface film, and despite decades of work, considerable controversy remains regarding the major components of the SEI as well as their formation mechanisms. Here we use a reaction network to investigate whether lithium ethylene monocarbonate (LEMC) or lithium ethylene dicarbonate (LEDC) is the major organic component of the LIB SEI. Our data-driven, automated methodology is based on a systematic generation of relevant species using a general fragmentation/recombination procedure which provides the basis for a vast thermodynamic reaction landscape, calculated with density functional theory. The shortest pathfinding algorithms are employed to explore the reaction landscape and obtain previously proposed formation mechanisms of LEMC as well as several new reaction pathways and intermediates. For example, we identify two novel LEMC formation mechanisms: one which involves LiH generation and another that involves breaking the (CH2)O-C(═O)OLi bond in LEDC. Most importantly, we find that all identified paths, which are also kinetically favorable under the explored conditions, require water as a reactant. This condition severely limits the amount of LEMC that can form, as compared with LEDC, a conclusion that has direct impact on the SEI formation in Li-ion energy storage systems. Finally, the data-driven framework presented here is generally applicable to any electrochemical system and expected to improve our understanding of surface passivation.
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Affiliation(s)
- Xiaowei Xie
- Department of Chemistry, University of California, Berkeley, California 94720, United States.,Materials Science Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Evan Walter Clark Spotte-Smith
- Materials Science Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States.,Department of Materials Science and Engineering, University of California, Berkeley, California 94720, United States
| | - Mingjian Wen
- Energy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Hetal D Patel
- Materials Science Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States.,Department of Materials Science and Engineering, University of California, Berkeley, California 94720, United States
| | - Samuel M Blau
- Energy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Kristin A Persson
- Department of Materials Science and Engineering, University of California, Berkeley, California 94720, United States.,Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
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15
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Maeda S, Harabuchi Y. Exploring paths of chemical transformations in molecular and periodic systems: An approach utilizing force. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2021. [DOI: 10.1002/wcms.1538] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Affiliation(s)
- Satoshi Maeda
- Institute for Chemical Reaction Design and Discovery (WPI‐ICReDD), Hokkaido University Sapporo Hokkaido Japan
- Department of Chemistry, Faculty of Science Hokkaido University Sapporo Hokkaido Japan
- JST, ERATO Maeda Artificial Intelligence for Chemical Reaction Design and Discovery Project Sapporo Hokkaido Japan
- National Institute for Materials Science (NIMS) Research and Services Division of Materials Data and Integrated System (MaDIS) Tsukuba Ibaraki Japan
| | - Yu Harabuchi
- Institute for Chemical Reaction Design and Discovery (WPI‐ICReDD), Hokkaido University Sapporo Hokkaido Japan
- Department of Chemistry, Faculty of Science Hokkaido University Sapporo Hokkaido Japan
- JST, ERATO Maeda Artificial Intelligence for Chemical Reaction Design and Discovery Project Sapporo Hokkaido Japan
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16
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Kirkok SK, Kibet JK, Kinyanjui T, Okanga FI, Nyamori VO. Dioxin and dibenzofuran like molecular analogues from the pyrolysis of biomass materials-the emerging challenge in bio-oil production. BMC Chem 2021; 15:3. [PMID: 33451336 PMCID: PMC7809783 DOI: 10.1186/s13065-020-00732-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Accepted: 12/15/2020] [Indexed: 11/12/2022] Open
Abstract
Introduction The aggressive search for renewable energy resources and essential pyrosynthetic compounds has marked an exponential rise in the thermal degradation of biomass materials. Consequently, clean and sustainable transport fuels are increasingly desirable in a highly industrialized economy, for energy security and environmental protection. For this reason, biomass materials have been identified as promising alternatives to fossil fuels despite the challenges resulting from the possible formation of toxic nitrogen-based molecules during biomass degradation. In order to understand the free radical characteristic challenges facing the use of bio-oil, a brief review of the effects of free radicals in bio-oil is presented. Methodology Pyrolysis was conducted in a tubular flow quartz reactor at a residence time of 2 s at 1 atm. pressure, for a total pyrolysis time of 5 min. The thermal degradation of biomass components was investigated over the temperature range of 200 to 700 °C typically in 50 °C increments under two reaction conditions; pyrolysis in N2 and oxidative pyrolysis in 5% O2 in N2. The pyrolysate effluent was analysed using a Gas chromatograph hyphenated to a mass selective detector (MSD). Results The yield of levoglucosan in the pyrolysis of cellulose in the entire pyrolysis temperature range was 68.2 wt % under inert conditions and 28.8 wt % under oxidative conditions. On the other hand, formaldehyde from pyrolysis of cellulose yielded 4 wt % while that from oxidative pyrolysis was 7 wt % translating to ⁓ 1.8 times higher than the yield from pyrolysis. Accordingly, we present for the first time dioxin-like and dibenzofuran-like nitrogenated analogues from an equimassic pyrolysis of cellulose and tyrosine. Levoglucosan and formaldehyde were completely inhibited during the equimassic pyrolysis of cellulose and tyrosine. Conclusion Clearly, any small amounts of N-biomass components such as amino acids in cellulosic biomass materials can inhibit the formation of levoglucosan–a major constituent of bio-oil. Overall, a judicious balance between the production of bio-oil and side products resulting from amino acids present in plant matter should be taken into account to minimize economic losses and mitigate against negative public health concerns.![]()
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Affiliation(s)
- Samuel K Kirkok
- Department of Chemistry, Egerton University, P.O Box 536, Egerton, 20115, Kenya
| | - Joshua K Kibet
- Department of Chemistry, Egerton University, P.O Box 536, Egerton, 20115, Kenya.
| | - Thomas Kinyanjui
- Department of Chemistry, Egerton University, P.O Box 536, Egerton, 20115, Kenya
| | - Francis I Okanga
- Department of Chemistry, Egerton University, P.O Box 536, Egerton, 20115, Kenya
| | - Vincent O Nyamori
- School of Chemistry and Physics, University of KwaZulu-Natal, Westville Campus, Private Bag X54001, Durban, 4000, South Africa
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17
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Abstract
It is an ultimate goal in chemistry to predict reaction without recourse to experiment. Reaction prediction is not just the reaction rate determination of known reactions but, more broadly, the reaction exploration to identify new reaction routes. This review briefly overviews the theory on chemical reaction and the current methods for computing/estimating reaction rate and exploring reaction space. We particularly focus on the atomistic simulation methods for reaction exploration, which are benefited significantly by recently emerged machine learning potentials. We elaborate the stochastic surface walking global pathway sampling based on the global neural network (SSW-NN) potential, developed in our group since 2013, which can explore complex reactions systems unbiasedly and automatedly. Two examples, molecular reaction and heterogeneous catalytic reactions, are presented to illustrate the current status for reaction prediction using SSW-NN.
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Affiliation(s)
- Pei-Lin Kang
- Collaborative Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University, Shanghai 200433, China
| | - Zhi-Pan Liu
- Collaborative Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University, Shanghai 200433, China
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18
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Sumiya Y, Maeda S. Rate Constant Matrix Contraction Method for Systematic Analysis of Reaction Path Networks. CHEM LETT 2020. [DOI: 10.1246/cl.200092] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- Yosuke Sumiya
- Department of Chemistry, Faculty of Science, Hokkaido University, Kita 10 Nishi 8, Kita-ku, Sapporo, Hokkaido 060-0810, Japan
| | - Satoshi Maeda
- Department of Chemistry, Faculty of Science, Hokkaido University, Kita 10 Nishi 8, Kita-ku, Sapporo, Hokkaido 060-0810, Japan
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Kita 21 Nishi 10, Kita-ku, Sapporo, Hokkaido 001-0021, Japan
- National Institute for Materials Science (NIMS), Research and Services Division of Materials Data and Integrated System (MaDIS), Tsukuba, Ibaraki 305-0044, Japan
- JST, ERATO Maeda Artificial Intelligence for Chemical Reaction Design and Discovery Project, Kita 10 Nishi 8, Kita-ku, Sapporo, Hokkaido 060-0810, Japan
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19
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Artificial Force-Induced Reaction Method for Systematic Elucidation of Mechanism and Selectivity in Organometallic Reactions. TOP ORGANOMETAL CHEM 2020. [DOI: 10.1007/3418_2020_51] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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20
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Kang PL, Shang C, Liu ZP. Glucose to 5-Hydroxymethylfurfural: Origin of Site-Selectivity Resolved by Machine Learning Based Reaction Sampling. J Am Chem Soc 2019; 141:20525-20536. [DOI: 10.1021/jacs.9b11535] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Pei-Lin Kang
- Collaborative Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University, Shanghai 200433, P. R. China
| | - Cheng Shang
- Collaborative Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University, Shanghai 200433, P. R. China
| | - Zhi-Pan Liu
- Collaborative Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University, Shanghai 200433, P. R. China
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21
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Jara‐Toro RA, Pino GA, Glowacki DR, Shannon RJ, Martínez‐Núñez E. Enhancing Automated Reaction Discovery with Boxed Molecular Dynamics in Energy Space. CHEMSYSTEMSCHEM 2019. [DOI: 10.1002/syst.201900024] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Affiliation(s)
- Rafael A. Jara‐Toro
- INIFIQC (CONICET-UNC) Dpto. De Fisicoquímica-Facultad de Ciencias Químicas-Centro Láser de Ciencias MolecularesUniversidad de Córdoba Ciudad Universitaria X50000HUA Córdoba Argentina
| | - Gustavo A. Pino
- INIFIQC (CONICET-UNC) Dpto. De Fisicoquímica-Facultad de Ciencias Químicas-Centro Láser de Ciencias MolecularesUniversidad de Córdoba Ciudad Universitaria X50000HUA Córdoba Argentina
| | - David R. Glowacki
- Centre for Computational Chemistry School of ChemistryUniversity of Bristol Cantock's Close Bristol BS8 1TS UK
| | - Robin J. Shannon
- Centre for Computational Chemistry School of ChemistryUniversity of Bristol Cantock's Close Bristol BS8 1TS UK
| | - Emilio Martínez‐Núñez
- Departmento de Química Física, Facultade de QuímicaUniversidade de Santiago de Compostela 15782 Santiago de Compostela Spain
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22
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Zhu L, Cohen RE, Strobel TA. Phase Transition Pathway Sampling via Swarm Intelligence and Graph Theory. J Phys Chem Lett 2019; 10:5019-5026. [PMID: 31342739 DOI: 10.1021/acs.jpclett.9b01715] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
The prediction of reaction pathways for solid-solid transformations remains a key challenge. Here, we develop a pathway sampling method via swarm intelligence and graph theory and demonstrate that our pallas method is an effective tool to help understand phase transformations in solid-state systems. The method is capable of finding low-energy transition pathways between two minima without having to specify any details of the transition mechanism a priori. We benchmarked our pallas method against known phase transitions in cadmium selenide (CdSe) and silicon (Si). pallas readily identifies previously reported, low-energy phase transition pathways for the wurtzite to rock-salt transition in CdSe and reveals a novel lower-energy pathway that has not yet been observed. In addition, pallas provides detailed information that explains the complex phase transition sequence observed during the decompression of Si from high pressure. Given the efficiency to identify low-barrier-energy reaction pathways, the pallas methodology represents a promising tool for materials by design with valuable insights for novel synthesis.
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Affiliation(s)
- Li Zhu
- Geophysical Laboratory, Carnegie Institution for Science, 5251 Broad Branch Road, Northwest, Washington, DC 20015, United States
| | - R E Cohen
- Geophysical Laboratory, Carnegie Institution for Science, 5251 Broad Branch Road, Northwest, Washington, DC 20015, United States
- Department of Earth and Environmental Sciences, Ludwig Maximilians Universität, Munich 80333, Germany
| | - Timothy A Strobel
- Geophysical Laboratory, Carnegie Institution for Science, 5251 Broad Branch Road, Northwest, Washington, DC 20015, United States
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23
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Maeda S, Harabuchi Y. On Benchmarking of Automated Methods for Performing Exhaustive Reaction Path Search. J Chem Theory Comput 2019; 15:2111-2115. [DOI: 10.1021/acs.jctc.8b01182] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Satoshi Maeda
- Department of Chemistry, Faculty of Science, Hokkaido University, Kita 10, Nishi 8, Kita-ku, Sapporo 060-0810, Japan
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Sapporo 001-0021, Japan
- Research and Services Division of Materials Data and Integrated System (MaDIS), National Institute for Materials Science (NIMS), Tsukuba 305-0044, Japan
| | - Yu Harabuchi
- Department of Chemistry, Faculty of Science, Hokkaido University, Kita 10, Nishi 8, Kita-ku, Sapporo 060-0810, Japan
- JST, PRESTO, 4-1-8 Honcho, Kawaguchi, Saitama 332-0012, Japan
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24
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Huang S, Shang C, Kang P, Zhang X, Liu Z. LASP: Fast global potential energy surface exploration. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2019. [DOI: 10.1002/wcms.1415] [Citation(s) in RCA: 59] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Si‐Da Huang
- Collaborative Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry Fudan University Shanghai China
| | - Cheng Shang
- Collaborative Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry Fudan University Shanghai China
| | - Pei‐Lin Kang
- Collaborative Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry Fudan University Shanghai China
| | - Xiao‐Jie Zhang
- Collaborative Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry Fudan University Shanghai China
| | - Zhi‐Pan Liu
- Collaborative Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry Fudan University Shanghai China
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25
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Affiliation(s)
- Yosuke Sumiya
- Department of Chemistry, Faculty of Science, Hokkaido University, Sapporo, Hokkaido 060-0810, Japan
| | - Satoshi Maeda
- Department of Chemistry, Faculty of Science, Hokkaido University, Sapporo, Hokkaido 060-0810, Japan
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26
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Sugiyama K, Sumiya Y, Takagi M, Saita K, Maeda S. Understanding CO oxidation on the Pt(111) surface based on a reaction route network. Phys Chem Chem Phys 2019; 21:14366-14375. [DOI: 10.1039/c8cp06856a] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Kinetic analysis by the rate constant matrix contraction on the reaction route network of CO oxidation on the Pt(111) surface obtained by the artificial force induced reaction reveals the impact of entropic contributions arising from a variety of local minima and transition states.
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Affiliation(s)
- Kanami Sugiyama
- Graduate School of Chemical Sciences and Engineering
- Hokkaido University
- Sapporo
- Japan
| | - Yosuke Sumiya
- Department of Chemistry
- Faculty of Science
- Hokkaido University
- Sapporo
- Japan
| | - Makito Takagi
- Graduate School of Chemical Sciences and Engineering
- Hokkaido University
- Sapporo
- Japan
| | - Kenichiro Saita
- Department of Chemistry
- Faculty of Science
- Hokkaido University
- Sapporo
- Japan
| | - Satoshi Maeda
- Department of Chemistry
- Faculty of Science
- Hokkaido University
- Sapporo
- Japan
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27
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Chen X, Zhao YF, Zhang YY, Li J. TGMin: An efficient global minimum searching program for free and surface-supported clusters. J Comput Chem 2018; 40:1105-1112. [PMID: 30549064 DOI: 10.1002/jcc.25649] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2018] [Revised: 09/10/2018] [Accepted: 09/19/2018] [Indexed: 12/24/2022]
Abstract
In this article, we introduce an efficient global-minimum structural search program named Tsinghua Global Minimum 2 (TGMin-2), which is the successor of the original TGMin algorithm that was developed in our group in 2011. We have introduced a number of new features and improvements into TGMin-2, including a symmetric structure generation algorithm that can produce good initial seeds for small- and medium-size clusters, the duplicated structure identification algorithm, and the improved structure adaption algorithm that was implemented in the original TGMin code. To predict the simulated photoelectron spectrum (PE spectrum) automatically, we also implemented a standalone program named AutoPES (Auto Photoelectron Spectroscopy), which can be used to simulate PE spectra and compare them with experimental results automatically. We have demonstrated that TGMin-2 and AutoPES are powerful tools for studying free and surface-supported molecules, clusters, and nanoclusters. © 2018 Wiley Periodicals, Inc.
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Affiliation(s)
- Xin Chen
- Department of Chemistry and Laboratory of Organic Optoelectronics & Molecular Engineering of the Ministry of Education, Tsinghua University, Beijing 100084, China
| | - Ya-Fan Zhao
- Department of Chemistry and Laboratory of Organic Optoelectronics & Molecular Engineering of the Ministry of Education, Tsinghua University, Beijing 100084, China.,Institute of Applied Physics and Computational Mathematics and CAEP Software Center for High Performance Numerical Simulation, Beijing 100088, China
| | - Yang-Yang Zhang
- Department of Chemistry and Laboratory of Organic Optoelectronics & Molecular Engineering of the Ministry of Education, Tsinghua University, Beijing 100084, China
| | - Jun Li
- Department of Chemistry and Laboratory of Organic Optoelectronics & Molecular Engineering of the Ministry of Education, Tsinghua University, Beijing 100084, China
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28
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A Trajectory-Based Method to Explore Reaction Mechanisms. Molecules 2018; 23:molecules23123156. [PMID: 30513663 PMCID: PMC6321347 DOI: 10.3390/molecules23123156] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2018] [Revised: 11/23/2018] [Accepted: 11/29/2018] [Indexed: 12/02/2022] Open
Abstract
The tsscds method, recently developed in our group, discovers chemical reaction mechanisms with minimal human intervention. It employs accelerated molecular dynamics, spectral graph theory, statistical rate theory and stochastic simulations to uncover chemical reaction paths and to solve the kinetics at the experimental conditions. In the present review, its application to solve mechanistic/kinetics problems in different research areas will be presented. Examples will be given of reactions involved in photodissociation dynamics, mass spectrometry, combustion chemistry and organometallic catalysis. Some planned improvements will also be described.
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29
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Simm GN, Vaucher AC, Reiher M. Exploration of Reaction Pathways and Chemical Transformation Networks. J Phys Chem A 2018; 123:385-399. [DOI: 10.1021/acs.jpca.8b10007] [Citation(s) in RCA: 103] [Impact Index Per Article: 17.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Gregor N. Simm
- Laboratory of Physical Chemistry, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| | - Alain C. Vaucher
- Laboratory of Physical Chemistry, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| | - Markus Reiher
- Laboratory of Physical Chemistry, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
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30
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Yang M, Yang L, Wang G, Zhou Y, Xie D, Li S. Combined Molecular Dynamics and Coordinate Driving Method for Automatic Reaction Pathway Search of Reactions in Solution. J Chem Theory Comput 2018; 14:5787-5796. [DOI: 10.1021/acs.jctc.8b00799] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Manyi Yang
- School of Chemistry and Chemical Engineering, Key Laboratory of Mesoscopic Chemistry of Ministry of Education, Institute of Theoretical and Computational Chemistry, Nanjing University, Nanjing 210023, People’s Republic of China
| | - Lijiang Yang
- Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, People’s Republic of China
| | - Guoqiang Wang
- School of Chemistry and Chemical Engineering, Key Laboratory of Mesoscopic Chemistry of Ministry of Education, Institute of Theoretical and Computational Chemistry, Nanjing University, Nanjing 210023, People’s Republic of China
| | - Yanzi Zhou
- School of Chemistry and Chemical Engineering, Key Laboratory of Mesoscopic Chemistry of Ministry of Education, Institute of Theoretical and Computational Chemistry, Nanjing University, Nanjing 210023, People’s Republic of China
| | - Daiqian Xie
- School of Chemistry and Chemical Engineering, Key Laboratory of Mesoscopic Chemistry of Ministry of Education, Institute of Theoretical and Computational Chemistry, Nanjing University, Nanjing 210023, People’s Republic of China
| | - Shuhua Li
- School of Chemistry and Chemical Engineering, Key Laboratory of Mesoscopic Chemistry of Ministry of Education, Institute of Theoretical and Computational Chemistry, Nanjing University, Nanjing 210023, People’s Republic of China
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31
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Rodríguez A, Rodríguez‐Fernández R, A. Vázquez S, L. Barnes G, J. P. Stewart J, Martínez‐Núñez E. tsscds2018: A code for automated discovery of chemical reaction mechanisms and solving the kinetics. J Comput Chem 2018; 39:1922-1930. [DOI: 10.1002/jcc.25370] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2018] [Revised: 05/03/2018] [Accepted: 05/11/2018] [Indexed: 01/13/2023]
Affiliation(s)
| | - Roberto Rodríguez‐Fernández
- Departamento de Química Física, Facultade de QuímicaCampus Vida, Universidade de Santiago de Compostela Santiago de Compostela 15782 Spain
| | - Saulo A. Vázquez
- Departamento de Química Física, Facultade de QuímicaCampus Vida, Universidade de Santiago de Compostela Santiago de Compostela 15782 Spain
| | - George L. Barnes
- Department of Chemistry and BiochemistrySiena College 515 Loudon Road, Loudonville New York
| | - James J. P. Stewart
- Stewart Computational Chemistry 15210 Paddington Circle, Colorado Springs Colorado 80921
| | - Emilio Martínez‐Núñez
- Departamento de Química Física, Facultade de QuímicaCampus Vida, Universidade de Santiago de Compostela Santiago de Compostela 15782 Spain
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32
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Proppe J, Husch T, Simm GN, Reiher M. Uncertainty quantification for quantum chemical models of complex reaction networks. Faraday Discuss 2018; 195:497-520. [PMID: 27730243 DOI: 10.1039/c6fd00144k] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
For the quantitative understanding of complex chemical reaction mechanisms, it is, in general, necessary to accurately determine the corresponding free energy surface and to solve the resulting continuous-time reaction rate equations for a continuous state space. For a general (complex) reaction network, it is computationally hard to fulfill these two requirements. However, it is possible to approximately address these challenges in a physically consistent way. On the one hand, it may be sufficient to consider approximate free energies if a reliable uncertainty measure can be provided. On the other hand, a highly resolved time evolution may not be necessary to still determine quantitative fluxes in a reaction network if one is interested in specific time scales. In this paper, we present discrete-time kinetic simulations in discrete state space taking free energy uncertainties into account. The method builds upon thermo-chemical data obtained from electronic structure calculations in a condensed-phase model. Our kinetic approach supports the analysis of general reaction networks spanning multiple time scales, which is here demonstrated for the example of the formose reaction. An important application of our approach is the detection of regions in a reaction network which require further investigation, given the uncertainties introduced by both approximate electronic structure methods and kinetic models. Such cases can then be studied in greater detail with more sophisticated first-principles calculations and kinetic simulations.
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Affiliation(s)
- Jonny Proppe
- Laboratory of Physical Chemistry, ETH Zürich, Zürich, Switzerland.
| | - Tamara Husch
- Laboratory of Physical Chemistry, ETH Zürich, Zürich, Switzerland.
| | - Gregor N Simm
- Laboratory of Physical Chemistry, ETH Zürich, Zürich, Switzerland.
| | - Markus Reiher
- Laboratory of Physical Chemistry, ETH Zürich, Zürich, Switzerland.
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33
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Maeda S, Sugiyama K, Sumiya Y, Takagi M, Saita K. Global Reaction Route Mapping for Surface Adsorbed Molecules: A Case Study for H2O on Cu(111) Surface. CHEM LETT 2018. [DOI: 10.1246/cl.171194] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Satoshi Maeda
- Department of Chemistry, Faculty of Science, Hokkaido University, Sapporo, Hokkaido 060-0810, Japan
- Research and Services Division of Materials Data and Integrated System (MaDIS), National Institute for Materials Science (NIMS), Tsukuba, Ibaraki 305-0044, Japan
| | - Kanami Sugiyama
- Graduate School of Chemical Sciences and Engineering, Hokkaido University, Sapporo, Hokkaido 060-8628, Japan
| | - Yosuke Sumiya
- Graduate School of Chemical Sciences and Engineering, Hokkaido University, Sapporo, Hokkaido 060-8628, Japan
| | - Makito Takagi
- Graduate School of Chemical Sciences and Engineering, Hokkaido University, Sapporo, Hokkaido 060-8628, Japan
| | - Kenichiro Saita
- Department of Chemistry, Faculty of Science, Hokkaido University, Sapporo, Hokkaido 060-0810, Japan
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34
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Tavadze P, Avendaño Franco G, Ren P, Wen X, Li Y, Lewis JP. A Machine-Driven Hunt for Global Reaction Coordinates of Azobenzene Photoisomerization. J Am Chem Soc 2017; 140:285-290. [PMID: 29235856 DOI: 10.1021/jacs.7b10030] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Pedram Tavadze
- Department
of Physics and Astronomy, West Virginia University, Morgantown, West Virginia 26506-6315, United States
| | - Guillermo Avendaño Franco
- Department
of Physics and Astronomy, West Virginia University, Morgantown, West Virginia 26506-6315, United States
| | - Pengju Ren
- State
Key Laboratory of Coal Conversion, Institute of Coal Chemistry, Chinese Academy of Sciences, Taiyuan, Shanxi 030001, China
- Synfuels China Co.
Ltd., Huairou, Beijing 101407, China
| | - Xiaodong Wen
- State
Key Laboratory of Coal Conversion, Institute of Coal Chemistry, Chinese Academy of Sciences, Taiyuan, Shanxi 030001, China
- Synfuels China Co.
Ltd., Huairou, Beijing 101407, China
| | - Yongwang Li
- State
Key Laboratory of Coal Conversion, Institute of Coal Chemistry, Chinese Academy of Sciences, Taiyuan, Shanxi 030001, China
- Synfuels China Co.
Ltd., Huairou, Beijing 101407, China
| | - James P. Lewis
- Department
of Physics and Astronomy, West Virginia University, Morgantown, West Virginia 26506-6315, United States
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35
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Maeda S, Harabuchi Y, Takagi M, Saita K, Suzuki K, Ichino T, Sumiya Y, Sugiyama K, Ono Y. Implementation and performance of the artificial force induced reaction method in the GRRM17 program. J Comput Chem 2017; 39:233-251. [PMID: 29135034 PMCID: PMC5765425 DOI: 10.1002/jcc.25106] [Citation(s) in RCA: 123] [Impact Index Per Article: 17.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2017] [Revised: 10/16/2017] [Accepted: 10/18/2017] [Indexed: 12/20/2022]
Abstract
This article reports implementation and performance of the artificial force induced reaction (AFIR) method in the upcoming 2017 version of GRRM program (GRRM17). The AFIR method, which is one of automated reaction path search methods, induces geometrical deformations in a system by pushing or pulling fragments defined in the system by an artificial force. In GRRM17, three different algorithms, that is, multicomponent algorithm (MC-AFIR), single-component algorithm (SC-AFIR), and double-sphere algorithm (DS-AFIR), are available, where the MC-AFIR was the only algorithm which has been available in the previous 2014 version. The MC-AFIR does automated sampling of reaction pathways between two or more reactant molecules. The SC-AFIR performs automated generation of global or semiglobal reaction path network. The DS-AFIR finds a single path between given two structures. Exploration of minimum energy structures within the hypersurface in which two different electronic states degenerate, and an interface with the quantum mechanics/molecular mechanics method, are also described. A code termed SAFIRE will also be available, as a visualization software for complicated reaction path networks. © 2017 The Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc.
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Affiliation(s)
- Satoshi Maeda
- Department of Chemistry, Faculty of Science, Hokkaido University, Sapporo, 060-0810, Japan
| | - Yu Harabuchi
- Department of Chemistry, Faculty of Science, Hokkaido University, Sapporo, 060-0810, Japan.,Precursory Research for Embryonic Science and Technology (PRESTO), Japan Science and Technology Agency (JST), Saitama, 332-0012, Japan
| | - Makito Takagi
- Graduate School of Chemical Sciences and Engineering, Hokkaido University, Sapporo, 060-8628, Japan
| | - Kenichiro Saita
- Department of Chemistry, Faculty of Science, Hokkaido University, Sapporo, 060-0810, Japan
| | - Kimichi Suzuki
- Department of Chemistry, Faculty of Science, Hokkaido University, Sapporo, 060-0810, Japan.,Fukui Institute for Fundamental Chemistry, Kyoto University, Kyoto, 606-8103, Japan
| | - Tomoya Ichino
- Department of Chemistry, Faculty of Science, Hokkaido University, Sapporo, 060-0810, Japan
| | - Yosuke Sumiya
- Graduate School of Chemical Sciences and Engineering, Hokkaido University, Sapporo, 060-8628, Japan
| | - Kanami Sugiyama
- Graduate School of Chemical Sciences and Engineering, Hokkaido University, Sapporo, 060-8628, Japan
| | - Yuriko Ono
- Department of Chemistry, Faculty of Science, Hokkaido University, Sapporo, 060-0810, Japan
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36
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Zhang XJ, Shang C, Liu ZP. Stochastic surface walking reaction sampling for resolving heterogeneous catalytic reaction network: A revisit to the mechanism of water-gas shift reaction on Cu. J Chem Phys 2017; 147:152706. [DOI: 10.1063/1.4989540] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
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37
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Zhu SC, Hu Q, Mao WL, Mao HK, Sheng H. Hydrogen-Bond Symmetrization Breakdown and Dehydrogenation Mechanism of FeO2H at High Pressure. J Am Chem Soc 2017; 139:12129-12132. [PMID: 28829596 DOI: 10.1021/jacs.7b06528] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Sheng-cai Zhu
- Center for High Pressure Science and Technology Advanced Research (HPSTAR), Shanghai 201203, People’s Republic of China
| | - Qingyang Hu
- Center for High Pressure Science and Technology Advanced Research (HPSTAR), Shanghai 201203, People’s Republic of China
- Department
of Geological Sciences, Stanford University, Stanford, California 94305, United States
| | - Wendy L. Mao
- Department
of Geological Sciences, Stanford University, Stanford, California 94305, United States
| | - Ho-kwang Mao
- Center for High Pressure Science and Technology Advanced Research (HPSTAR), Shanghai 201203, People’s Republic of China
- Geophysical
Laboratory, Carnegie Institution of Washington, Washington, D.C. 20015, United States
| | - Hongwei Sheng
- Center for High Pressure Science and Technology Advanced Research (HPSTAR), Shanghai 201203, People’s Republic of China
- Department
of Physics and Astronomy, George Mason University, Fairfax, Virginia 22030, United States
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38
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Nandi S, McAnanama-Brereton SR, Waller MP, Anoop A. A tabu-search based strategy for modeling molecular aggregates and binary reactions. COMPUT THEOR CHEM 2017. [DOI: 10.1016/j.comptc.2017.03.040] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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39
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Yang M, Zou J, Wang G, Li S. Automatic Reaction Pathway Search via Combined Molecular Dynamics and Coordinate Driving Method. J Phys Chem A 2017; 121:1351-1361. [DOI: 10.1021/acs.jpca.6b12195] [Citation(s) in RCA: 47] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Manyi Yang
- School of Chemistry and Chemical
Engineering, Key Laboratory of Mesoscopic Chemistry of Ministry of
Education, Institute of Theoretical and Computational Chemistry, Nanjing University, Nanjing 210023, People’s Republic of China
| | - Jingxiang Zou
- School of Chemistry and Chemical
Engineering, Key Laboratory of Mesoscopic Chemistry of Ministry of
Education, Institute of Theoretical and Computational Chemistry, Nanjing University, Nanjing 210023, People’s Republic of China
| | - Guoqiang Wang
- School of Chemistry and Chemical
Engineering, Key Laboratory of Mesoscopic Chemistry of Ministry of
Education, Institute of Theoretical and Computational Chemistry, Nanjing University, Nanjing 210023, People’s Republic of China
| | - Shuhua Li
- School of Chemistry and Chemical
Engineering, Key Laboratory of Mesoscopic Chemistry of Ministry of
Education, Institute of Theoretical and Computational Chemistry, Nanjing University, Nanjing 210023, People’s Republic of China
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40
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Zhang XJ, Shang C, Liu ZP. Pressure-induced silica quartz amorphization studied by iterative stochastic surface walking reaction sampling. Phys Chem Chem Phys 2017; 19:4725-4733. [DOI: 10.1039/c6cp06895b] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The origin of the pressure-induced amorphization of SiO2 is resolved from theory based on pathways on the global potential energy surface.
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Affiliation(s)
- Xiao-Jie Zhang
- Collaborative Innovation Center of Chemistry for Energy Material
- Key Laboratory of Computational Physical Science (Ministry of Education)
- Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials
- Department of Chemistry
- Fudan University
| | - Cheng Shang
- Collaborative Innovation Center of Chemistry for Energy Material
- Key Laboratory of Computational Physical Science (Ministry of Education)
- Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials
- Department of Chemistry
- Fudan University
| | - Zhi-Pan Liu
- Collaborative Innovation Center of Chemistry for Energy Material
- Key Laboratory of Computational Physical Science (Ministry of Education)
- Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials
- Department of Chemistry
- Fudan University
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41
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Shang C, Zhang XJ, Liu ZP. Crystal phase transition of urea: what governs the reaction kinetics in molecular crystal phase transitions. Phys Chem Chem Phys 2017; 19:32125-32131. [DOI: 10.1039/c7cp07060h] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The predominant type-I hydrogen-bonding network in urea crystals facilitates the solid-to-solid transformation between major crystal forms.
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Affiliation(s)
- Cheng Shang
- Collaborative Innovation Center of Chemistry for Energy Material
- Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials
- Key Laboratory of Computational Physical Science (Ministry of Education)
- Department of Chemistry
- Fudan University
| | - Xiao-Jie Zhang
- Collaborative Innovation Center of Chemistry for Energy Material
- Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials
- Key Laboratory of Computational Physical Science (Ministry of Education)
- Department of Chemistry
- Fudan University
| | - Zhi-Pan Liu
- Collaborative Innovation Center of Chemistry for Energy Material
- Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials
- Key Laboratory of Computational Physical Science (Ministry of Education)
- Department of Chemistry
- Fudan University
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42
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Wei GF, Liu ZP. Subnano Pt Particles from a First-Principles Stochastic Surface Walking Global Search. J Chem Theory Comput 2016; 12:4698-706. [DOI: 10.1021/acs.jctc.6b00556] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Guang-Feng Wei
- Shanghai
Key Laboratory of Chemical Assessment and Sustainability, Department
of Chemistry, Tongji University, Shanghai 200092, China
- Collaborative
Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory
of Molecular Catalysis and Innovative Materials, Key Laboratory of
Computational Physical Science (Ministry of Education), Department
of Chemistry, Fudan University, Shanghai 200433, China
| | - Zhi-Pan Liu
- Collaborative
Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory
of Molecular Catalysis and Innovative Materials, Key Laboratory of
Computational Physical Science (Ministry of Education), Department
of Chemistry, Fudan University, Shanghai 200433, China
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43
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Schaefer B, Goedecker S. Computationally efficient characterization of potential energy surfaces based on fingerprint distances. J Chem Phys 2016; 145:034101. [DOI: 10.1063/1.4956461] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
Affiliation(s)
- Bastian Schaefer
- Department of Physics, University of Basel, Klingelbergstrasse 82, CH-4056 Basel, Switzerland
| | - Stefan Goedecker
- Department of Physics, University of Basel, Klingelbergstrasse 82, CH-4056 Basel, Switzerland
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44
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Zhu SC, Guan SH, Liu ZP. Mechanism and microstructures in Ga2O3 pseudomartensitic solid phase transition. Phys Chem Chem Phys 2016; 18:18563-74. [PMID: 27344965 DOI: 10.1039/c6cp03673b] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Solid-to-solid phase transition, although widely exploited in making new materials, challenges persistently our current theory for predicting its complex kinetics and rich microstructures in transition. The Ga2O3α-β phase transformation represents such a common but complex reaction with marked change in cation coordination and crystal density, which was known to yield either amorphous or crystalline products under different synthetic conditions. Here we, via recently developed stochastic surface walking (SSW) method, resolve for the first time the atomistic mechanism of Ga2O3α-β phase transformation, the pathway of which turns out to be the first reaction pathway ever determined for a new type of diffusionless solid phase transition, namely, pseudomartensitic phase transition. We demonstrate that the sensitivity of product crystallinity is caused by its multi-step, multi-type reaction pathway, which bypasses seven intermediate phases and involves all types of elementary solid phase transition steps, i.e. the shearing of O layers (martensitic type), the local diffusion of Ga atoms (reconstructive type) and the significant lattice dilation (dilation type). While the migration of Ga atoms across the close-packed O layers is the rate-determining step and yields "amorphous-like" high energy intermediates, the shearing of O layers contributes to the formation of coherent biphase junctions and the presence of a crystallographic orientation relation, (001)α//(201[combining macron])β + [120]α//[13[combining macron]2]β. Our experiment using high-resolution transmission electron microscopy further confirms the theoretical predictions on the atomic structure of biphase junction and the formation of (201[combining macron])β twin, and also discovers the late occurrence of lattice expansion in the nascent β phase that grows out from the parent α phase. By distinguishing pseudomartensitic transition from other types of mechanisms, we propose general rules to predict the product crystallinity of solid phase transition. The new knowledge on the kinetics of pseudomartensitic transition complements the theory of diffusionless solid phase transition.
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Affiliation(s)
- Sheng-Cai Zhu
- Collaborative Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science (Ministry of Education), Department of Chemistry, Fudan University, Shanghai 200433, China.
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45
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Maeda S, Harabuchi Y, Takagi M, Taketsugu T, Morokuma K. Artificial Force Induced Reaction (AFIR) Method for Exploring Quantum Chemical Potential Energy Surfaces. CHEM REC 2016; 16:2232-2248. [DOI: 10.1002/tcr.201600043] [Citation(s) in RCA: 94] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2016] [Indexed: 12/25/2022]
Affiliation(s)
- Satoshi Maeda
- Department of Chemistry, Faculty of Science; Hokkaido University; Sapporo 060-0810 Japan
| | - Yu Harabuchi
- Department of Chemistry, Faculty of Science; Hokkaido University; Sapporo 060-0810 Japan
| | - Makito Takagi
- Graduate School of Chemical Sciences and Engineering; Hokkaido University; Sapporo 060-8628 Japan
| | - Tetsuya Taketsugu
- Department of Chemistry, Faculty of Science; Hokkaido University; Sapporo 060-0810 Japan
| | - Keiji Morokuma
- Fukui Institute for Fundamental Chemistry, Kyoto University; Kyoto 606-8103 Japan
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46
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Suleimanov YV, Green WH. Automated Discovery of Elementary Chemical Reaction Steps Using Freezing String and Berny Optimization Methods. J Chem Theory Comput 2015; 11:4248-59. [DOI: 10.1021/acs.jctc.5b00407] [Citation(s) in RCA: 99] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Yury V. Suleimanov
- Computation-based
Science and Technology Research Center, Cyprus Institute, 20
Kavafi Street, Nicosia 2121, Cyprus
- Department
of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - William H. Green
- Department
of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
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