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Li L, Liu H, Qin Y, Wang H, Han J, Zhu X, Ge Q. Coupled oxygen desorption and structural reconstruction accompanying reduction of copper oxide. J Chem Phys 2023; 158:054702. [PMID: 36754813 DOI: 10.1063/5.0136537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
Abstract
Understanding structural transformation and phase transition accompanying reactions in a solid as a catalyst or oxygen carrier is important to the design and optimization of many catalytic or chemical looping reaction processes. Herein, we combined density functional theory calculation with the stochastic surface walking global optimization approach to track the structural transformation accompanying the reduction of CuO upon releasing oxygen. We then used machine learning (ML) methods to correlate the structural properties of CuOx with varying x. By decomposing a reduction step into oxygen detachment and structural reconstruction, we identified two types of pathways: (1) uniform reduction with minimal structural changes; (2) segregated reduction with significant reconstruction. The results of ML analysis showed that the most important feature is the radial distribution functions of Cu-O at a percentage of oxygen vacancy [C(OV)] < 50% and Cu-Cu at C(OV) > 50% for CuOx formation. These features reflect the underlying physicochemical origin, i.e., Cu-O breaking and Cu-Cu formation in the respective stage of reduction. Phase diagram analysis indicates that CuO will be reduced to Cu2O under a typical oxygen uncoupling condition. This work demonstrates the complexity of solid structural transformation and the potential of ML methods in studying solid state materials involved in many chemical processes.
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Affiliation(s)
- Liwen Li
- Collaborative Innovation Center of Chemical Science and Engineering, Key Laboratory for Green Chemical Technology, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China
| | - Huixian Liu
- Collaborative Innovation Center of Chemical Science and Engineering, Key Laboratory for Green Chemical Technology, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China
| | - Yuyao Qin
- Collaborative Innovation Center of Chemical Science and Engineering, Key Laboratory for Green Chemical Technology, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China
| | - Hua Wang
- Collaborative Innovation Center of Chemical Science and Engineering, Key Laboratory for Green Chemical Technology, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China
| | - Jinyu Han
- Collaborative Innovation Center of Chemical Science and Engineering, Key Laboratory for Green Chemical Technology, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China
| | - Xinli Zhu
- Collaborative Innovation Center of Chemical Science and Engineering, Key Laboratory for Green Chemical Technology, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China
| | - Qingfeng Ge
- Department of Chemistry and Biochemistry, Southern Illinois University, Carbondale, Illinois 62901, USA
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Santos-Florez PA, Yanxon H, Kang B, Yao Y, Zhu Q. Size-Dependent Nucleation in Crystal Phase Transition from Machine Learning Metadynamics. PHYSICAL REVIEW LETTERS 2022; 129:185701. [PMID: 36374681 DOI: 10.1103/physrevlett.129.185701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 08/05/2022] [Accepted: 09/11/2022] [Indexed: 06/16/2023]
Abstract
In this Letter, we present a framework that combines machine learning potential (MLP) and metadynamics to investigate solid-solid phase transition. Based on the spectral descriptors and neural networks regression, we develop a scalable MLP model to warrant an accurate interpolation of the energy surface where two phases coexist. Applying it to the simulation of B4-B1 phase transition of GaN under 50 GPa with different model sizes, we observe sequential change of the phase transition mechanism from collective modes to nucleation and growths. When the size is at or below 128 000 atoms, the nucleation and growth appear to follow a preferred direction. At larger sizes, the nuclei occur at multiple sites simultaneously and grow to microstructures by passing the critical size. The observed change of the atomistic mechanism manifests the importance of statistical sampling with large system size in phase transition modeling.
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Affiliation(s)
- Pedro A Santos-Florez
- Department of Physics and Astronomy, University of Nevada, Las Vegas, Nevada 89154, USA
| | - Howard Yanxon
- X-Ray Science Division, Argonne National Laboratory, Lemont, Illinois 60439, USA
| | - Byungkyun Kang
- Department of Physics and Astronomy, University of Nevada, Las Vegas, Nevada 89154, USA
| | - Yansun Yao
- Department of Physics and Engineering Physics, University of Saskatchewan, Saskatoon, Saskatchewan, S7N 5E2, Canada
| | - Qiang Zhu
- Department of Physics and Astronomy, University of Nevada, Las Vegas, Nevada 89154, USA
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Deb S, Sidheekh S, Clements CF, Krishnan NC, Dutta PS. Machine learning methods trained on simple models can predict critical transitions in complex natural systems. ROYAL SOCIETY OPEN SCIENCE 2022; 9:211475. [PMID: 35223058 PMCID: PMC8847887 DOI: 10.1098/rsos.211475] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 01/18/2022] [Indexed: 05/03/2023]
Abstract
Forecasting sudden changes in complex systems is a critical but challenging task, with previously developed methods varying widely in their reliability. Here we develop a novel detection method, using simple theoretical models to train a deep neural network to detect critical transitions-the Early Warning Signal Network (EWSNet). We then demonstrate that this network, trained on simulated data, can reliably predict observed real-world transitions in systems ranging from rapid climatic change to the collapse of ecological populations. Importantly, our model appears to capture latent properties in time series missed by previous warning signals approaches, allowing us to not only detect if a transition is approaching, but critically whether the collapse will be catastrophic or non-catastrophic. These novel properties mean EWSNet has the potential to serve as an indicator of transitions across a broad spectrum of complex systems, without requiring information on the structure of the system being monitored. Our work highlights the practicality of deep learning for addressing further questions pertaining to ecosystem collapse and has much broader management implications.
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Affiliation(s)
- Smita Deb
- Department of Mathematics, Indian Institute of Technology Ropar, Rupnagar, Punjab 140001, India
| | - Sahil Sidheekh
- Department of Computer Science and Engineering, Indian Institute of Technology Ropar, Rupnagar, Punjab 140001, India
| | | | - Narayanan C. Krishnan
- Department of Computer Science and Engineering, Indian Institute of Technology Ropar, Rupnagar, Punjab 140001, India
| | - Partha S. Dutta
- Department of Mathematics, Indian Institute of Technology Ropar, Rupnagar, Punjab 140001, India
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