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Barrios Herrera L, Lourenço MP, Hostaš J, Calaminici P, Köster AM, Tchagang A, Salahub DR. Active-learning for global optimization of Ni-Ceria nanoparticles: The case of Ce 4-xNi xO 8- x (x = 1, 2, 3). J Comput Chem 2024; 45:1643-1656. [PMID: 38551129 DOI: 10.1002/jcc.27346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 02/15/2024] [Accepted: 03/05/2024] [Indexed: 06/04/2024]
Abstract
Ni-CeO2 nanoparticles (NPs) are promising nanocatalysts for water splitting and water gas shift reactions due to the ability of ceria to temporarily donate oxygen to the catalytic reaction and accept oxygen after the reaction is completed. Therefore, elucidating how different properties of the Ni-Ceria NPs relate to the activity and selectivity of the catalytic reaction, is of crucial importance for the development of novel catalysts. In this work the active learning (AL) method based on machine learning regression and its uncertainty is used for the global optimization of Ce(4-x)NixO(8-x) (x = 1, 2, 3) nanoparticles, employing density functional theory calculations. Additionally, further investigation of the NPs by mass-scaled parallel-tempering Born-Oppenheimer molecular dynamics resulted in the same putative global minimum structures found by AL, demonstrating the robustness of our AL search to learn from small datasets and assist in the global optimization of complex electronic structure systems.
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Affiliation(s)
- Lizandra Barrios Herrera
- Department of Chemistry, Department of Physics and Astronomy, CMS Centre for Molecular Simulation, IQST Institute for Quantum Science and Technology, Quantum Alberta, University of Calgary, Calgary, Canada
| | - Maicon Pierre Lourenço
- Departamento de Química e Física, Centro de Ciências Exatas, Naturais e da Saúde (CCENS), Universidade Federal do Espírito Santo, Espírito Santo, Brasil
| | - Jiří Hostaš
- Department of Chemistry, Department of Physics and Astronomy, CMS Centre for Molecular Simulation, IQST Institute for Quantum Science and Technology, Quantum Alberta, University of Calgary, Calgary, Canada
- Digital Technologies Research Centre, National Research Council of Canada, Ottawa, Canada
| | | | | | - Alain Tchagang
- Digital Technologies Research Centre, National Research Council of Canada, Ottawa, Canada
| | - Dennis R Salahub
- Department of Chemistry, Department of Physics and Astronomy, CMS Centre for Molecular Simulation, IQST Institute for Quantum Science and Technology, Quantum Alberta, University of Calgary, Calgary, Canada
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Luna-Valenzuela A, Pedroza-Montero JN, Köster AM, Calaminici P, Gálvez-González LE, Posada-Amarillas A. Pd 8 Cluster: Too Small to Melt? A BOMD Study. J Phys Chem A 2024; 128:572-580. [PMID: 38207112 DOI: 10.1021/acs.jpca.3c06173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2024]
Abstract
The question of whether a solid-liquid phase transition occurs in small clusters poses a fundamental challenge. In this study, we attempt to elucidate this phenomenon through a thorough examination of the thermal behavior and structural stability of Pd8 clusters employing ab initio simulations. Initially, a systematic global search is carried out to identify the various isomers of the Pd8 cluster. This is accomplished by employing an ab initio basin-hopping algorithm and using the PBE/SDD scheme integrated in the Gaussian code. The resulting isomers are further refined through reoptimization using the deMon2k package. To ensure the structural firmness of the lowest-energy isomer, we calculated normal modes. The structural stability as a function of temperature is analyzed through the Born-Oppenheimer molecular dynamics (BOMD) approach. Multiple BOMD trajectories at distinct simulated temperatures are examined with data clustering analysis to determine cluster isomers. This analysis establishes a connection between the potential energy landscape and the simulated temperature. To address the question of cluster melting, canonical parallel-tempering BOMD runs are performed and analyzed with the multiple-histogram method. A broad maximum in the heat capacity curve indicates a melting transition between 500 and 600 K. To further examine this transition, the mean-squared displacement and the pair-distance distribution function are calculated. The results of these calculations confirm the existence of a solid-liquid phase transition, as indicated by the heat capacity curve.
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Affiliation(s)
- Analila Luna-Valenzuela
- Departamento de Ciencias de la Salud, Unidad Regional Los Mochis, Universidad Autónoma de Occidente, Blvd. Macario Gaxiola S/N, 81217 Los Mochis, Sinaloa, México
| | - Jesús N Pedroza-Montero
- Departamento de Química, CINVESTAV, Av. Instituto Politécnico Nacional, 2508 Ciudad de Mexico, México
| | - Andreas M Köster
- Departamento de Química, CINVESTAV, Av. Instituto Politécnico Nacional, 2508 Ciudad de Mexico, México
| | - Patrizia Calaminici
- Departamento de Química, CINVESTAV, Av. Instituto Politécnico Nacional, 2508 Ciudad de Mexico, México
| | - Luis E Gálvez-González
- Departamento de Investigación en Física, Universidad de Sonora, Blvd. Luis Encinas & Rosales, 83000 Hermosillo, Sonora, México
| | - Alvaro Posada-Amarillas
- Departamento de Investigación en Física, Universidad de Sonora, Blvd. Luis Encinas & Rosales, 83000 Hermosillo, Sonora, México
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Lourenço MP, Hostaš J, Herrera LB, Calaminici P, Köster AM, Tchagang A, Salahub DR. GAMaterial-A genetic-algorithm software for material design and discovery. J Comput Chem 2023; 44:814-823. [PMID: 36444916 DOI: 10.1002/jcc.27043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 09/26/2022] [Accepted: 11/06/2022] [Indexed: 11/30/2022]
Abstract
Genetic algorithms (GAs) are stochastic global search methods inspired by biological evolution. They have been used extensively in chemistry and materials science coupled with theoretical methods, ranging from force-fields to high-throughput first-principles methods. The methodology allows an accurate and automated structural determination for molecules, atomic clusters, nanoparticles, and solid surfaces, fundamental to understanding chemical processes in catalysis and environmental sciences, for instance. In this work, we propose a new genetic algorithm software, GAMaterial, implemented in Python3.x, that performs global searches to elucidate the structures of atomic clusters, doped clusters or materials and atomic clusters on surfaces. For all these applications, it is possible to accelerate the GA search by using machine learning (ML), the ML@GA method, to build subsequent populations. Results for ML@GA applied for the dopant distributions in atomic clusters are presented. The GAMaterial software was applied for the automatic structural search for the Ti6 O12 cluster, doping Al in Si11 (4Al@Si11 ) and Na10 supported on graphene (Na10 @graphene), where DFTB calculations were used to sample the complex search surfaces with reasonably low computational cost. Finally, the global search by GA of the Mo8 C4 cluster was considered, where DFT calculations were made with the deMon2k code, which is interfaced with GAMaterial.
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Affiliation(s)
- Maicon Pierre Lourenço
- Departamento de Química e Física - Centro de Ciências Exatas, Naturais e da Saúde - CCENS - Universidade Federal do Espírito Santo, Espírito Santo, Brazil
| | - Jiří Hostaš
- Department of Chemistry, Department of Physics and Astronomy, CMS Centre for Molecular Simulation, IQST Institute for Quantum Science and Technology, Quantum Alberta, University of Calgary, Calgary, Alberta, Canada
| | - Lizandra Barrios Herrera
- Department of Chemistry, Department of Physics and Astronomy, CMS Centre for Molecular Simulation, IQST Institute for Quantum Science and Technology, Quantum Alberta, University of Calgary, Calgary, Alberta, Canada
| | | | | | - Alain Tchagang
- Digital Technologies Research Centre, National Research Council of Canada, Ottawa, Ontario, Canada
| | - Dennis R Salahub
- Department of Chemistry, Department of Physics and Astronomy, CMS Centre for Molecular Simulation, IQST Institute for Quantum Science and Technology, Quantum Alberta, University of Calgary, Calgary, Alberta, Canada
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