Constructing machine learning potential for metal nanoparticles of varying sizes via basin-hoping Monte Carlo and active learning
Nanoparticles, distinguished by their unique chemical and physical properties, have emerged as focal points within the realm of materials science. Traditional theoretical approaches for atomic simulations mainly include empirical force field and ab initio simulations, with the former offering effici...
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Science Press
2024-03-01
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| Series: | National Science Open |
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| Online Access: | https://www.sciengine.com/doi/10.1360/nso/20230088 |
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| author | Gong Fu-Qiang Xiong Ke Cheng Jun |
| author_facet | Gong Fu-Qiang Xiong Ke Cheng Jun |
| author_sort | Gong Fu-Qiang |
| collection | DOAJ |
| description | Nanoparticles, distinguished by their unique chemical and physical properties, have emerged as focal points within the realm of materials science. Traditional theoretical approaches for atomic simulations mainly include empirical force field and ab initio simulations, with the former offering efficiency but limited reliability, and the latter providing accuracy but restricted to systems of relatively small sizes. Herein, we propose a systematic strategy and automated workflow designed for collecting a diverse types of atomic local environments within a training dataset. This includes small nanoclusters, nanoparticles, as well as surface and bulk systems with periodic boundary conditions. The objective is to construct a machine learning potential tailored for pure metal nanoparticle simulations of varying sizes. Through rigorous validation, we have shown that our trained machine learning potential is capable of effectively driving molecular dynamics simulations of nanoparticles across a wide temperature range, especially within the nanoscale regime. Remarkably, this is achieved while preserving the accuracy typically associated with ab initio methods. |
| format | Article |
| id | doaj-art-dba259b7efc24ed8b417413e6fbf6009 |
| institution | OA Journals |
| issn | 2097-1168 |
| language | English |
| publishDate | 2024-03-01 |
| publisher | Science Press |
| record_format | Article |
| series | National Science Open |
| spelling | doaj-art-dba259b7efc24ed8b417413e6fbf60092025-08-20T02:02:04ZengScience PressNational Science Open2097-11682024-03-01310.1360/nso/20230088eb33e642Constructing machine learning potential for metal nanoparticles of varying sizes via basin-hoping Monte Carlo and active learningGong Fu-Qiang0Xiong Ke1Cheng Jun2["State Key Laboratory of Physical Chemistry of Solid Surface, iChEM, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China"]["State Key Laboratory of Physical Chemistry of Solid Surface, iChEM, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China"]["State Key Laboratory of Physical Chemistry of Solid Surface, iChEM, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China","Laboratory of AI for Electrochemistry (AI4EC), Tan Kah Kee Innovation Laboratory (IKKEM), Xiamen 361005, China","Institute of Artificial Intelligence, Xiamen University, Xiamen 361005, China"]Nanoparticles, distinguished by their unique chemical and physical properties, have emerged as focal points within the realm of materials science. Traditional theoretical approaches for atomic simulations mainly include empirical force field and ab initio simulations, with the former offering efficiency but limited reliability, and the latter providing accuracy but restricted to systems of relatively small sizes. Herein, we propose a systematic strategy and automated workflow designed for collecting a diverse types of atomic local environments within a training dataset. This includes small nanoclusters, nanoparticles, as well as surface and bulk systems with periodic boundary conditions. The objective is to construct a machine learning potential tailored for pure metal nanoparticle simulations of varying sizes. Through rigorous validation, we have shown that our trained machine learning potential is capable of effectively driving molecular dynamics simulations of nanoparticles across a wide temperature range, especially within the nanoscale regime. Remarkably, this is achieved while preserving the accuracy typically associated with ab initio methods.https://www.sciengine.com/doi/10.1360/nso/20230088condensed matter physicsnanoparticlesmachine learning potentialworkflow |
| spellingShingle | Gong Fu-Qiang Xiong Ke Cheng Jun Constructing machine learning potential for metal nanoparticles of varying sizes via basin-hoping Monte Carlo and active learning National Science Open condensed matter physics nanoparticles machine learning potential workflow |
| title | Constructing machine learning potential for metal nanoparticles of varying sizes via basin-hoping Monte Carlo and active learning |
| title_full | Constructing machine learning potential for metal nanoparticles of varying sizes via basin-hoping Monte Carlo and active learning |
| title_fullStr | Constructing machine learning potential for metal nanoparticles of varying sizes via basin-hoping Monte Carlo and active learning |
| title_full_unstemmed | Constructing machine learning potential for metal nanoparticles of varying sizes via basin-hoping Monte Carlo and active learning |
| title_short | Constructing machine learning potential for metal nanoparticles of varying sizes via basin-hoping Monte Carlo and active learning |
| title_sort | constructing machine learning potential for metal nanoparticles of varying sizes via basin hoping monte carlo and active learning |
| topic | condensed matter physics nanoparticles machine learning potential workflow |
| url | https://www.sciengine.com/doi/10.1360/nso/20230088 |
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