Machine learning driven prediction of lattice constants in transition metal dichalcogenides
Machine learning represents an emerging branch of artificial intelligence, centering on the enhancement of algorithms in computer programs through the utilization of data and the accumulation of research-driven knowledge. The requirement for artificial intelligence in materials science is essential...
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| Format: | Article |
| Language: | English |
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Department of Physics, Mahendra Morang Adarsh Multiple Campus, Tribhuvan University
2023-11-01
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| Series: | Bibechana |
| Subjects: | |
| Online Access: | https://www.nepjol.info/index.php/BIBECHANA/article/view/57732 |
| _version_ | 1797194046181998592 |
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| author | Bhupendra Sharma Laxman Chaudhary Rajendra Adhikari Madhav Prasad Ghimire |
| author_facet | Bhupendra Sharma Laxman Chaudhary Rajendra Adhikari Madhav Prasad Ghimire |
| author_sort | Bhupendra Sharma |
| collection | DOAJ |
| description |
Machine learning represents an emerging branch of artificial intelligence, centering on the enhancement of algorithms in computer programs through the utilization of data and the accumulation of research-driven knowledge. The requirement for artificial intelligence in materials science is essential due to the significant need for innovative high-performance materials on a large scale. In this report, the gradient boosting regression tree model of machine learning was applied to predict the lattice constants of cubic and trigonal MX2 systems (M=transition metal and X=chalcogen atoms). The theoretical/experimental values of the materials were compared to the predicted values to calculate the standard errors such as RMSE (root mean square error) and MAE (mean absolute error). The features used to predict lattice constants were ionic radius, lattice angles, bandgap, formation energy, total magnetic moment, density and oxidation states. The features versus contribution barplot has been drawn to reveal the contribution level of each parameter in the degree of [0,1] to obtain the predictions. This report provides a precise account of the prediction methodology for lattice parameters of the transition metal dichalcogenides family, a process that was previously not reported.
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| first_indexed | 2024-03-09T01:31:11Z |
| format | Article |
| id | doaj.art-5985ce9f2f87478f8fb4c5c13e4d65b4 |
| institution | Directory Open Access Journal |
| issn | 2091-0762 2382-5340 |
| language | English |
| last_indexed | 2024-04-24T05:50:03Z |
| publishDate | 2023-11-01 |
| publisher | Department of Physics, Mahendra Morang Adarsh Multiple Campus, Tribhuvan University |
| record_format | Article |
| series | Bibechana |
| spelling | doaj.art-5985ce9f2f87478f8fb4c5c13e4d65b42024-04-23T13:04:48ZengDepartment of Physics, Mahendra Morang Adarsh Multiple Campus, Tribhuvan UniversityBibechana2091-07622382-53402023-11-0120310.3126/bibechana.v20i3.5773284833Machine learning driven prediction of lattice constants in transition metal dichalcogenidesBhupendra Sharma0Laxman Chaudhary1Rajendra Adhikari2Madhav Prasad Ghimire3Central Department of Physics, Tribhuvan University, NepalCentral Department of Physics, Tribhuvan University, Kirtipur, 44613, Kathmandu, NepalDepartment of Physics, Kathmandu University, Dhulikhel, Kavre, NepalCentral Department of Physics, Tribhuvan University, Nepal Machine learning represents an emerging branch of artificial intelligence, centering on the enhancement of algorithms in computer programs through the utilization of data and the accumulation of research-driven knowledge. The requirement for artificial intelligence in materials science is essential due to the significant need for innovative high-performance materials on a large scale. In this report, the gradient boosting regression tree model of machine learning was applied to predict the lattice constants of cubic and trigonal MX2 systems (M=transition metal and X=chalcogen atoms). The theoretical/experimental values of the materials were compared to the predicted values to calculate the standard errors such as RMSE (root mean square error) and MAE (mean absolute error). The features used to predict lattice constants were ionic radius, lattice angles, bandgap, formation energy, total magnetic moment, density and oxidation states. The features versus contribution barplot has been drawn to reveal the contribution level of each parameter in the degree of [0,1] to obtain the predictions. This report provides a precise account of the prediction methodology for lattice parameters of the transition metal dichalcogenides family, a process that was previously not reported. https://www.nepjol.info/index.php/BIBECHANA/article/view/57732Machine learningArtificial intelligenceGradient Boosting RegressionGradient DescentRMSEMAE |
| spellingShingle | Bhupendra Sharma Laxman Chaudhary Rajendra Adhikari Madhav Prasad Ghimire Machine learning driven prediction of lattice constants in transition metal dichalcogenides Bibechana Machine learning Artificial intelligence Gradient Boosting Regression Gradient Descent RMSE MAE |
| title | Machine learning driven prediction of lattice constants in transition metal dichalcogenides |
| title_full | Machine learning driven prediction of lattice constants in transition metal dichalcogenides |
| title_fullStr | Machine learning driven prediction of lattice constants in transition metal dichalcogenides |
| title_full_unstemmed | Machine learning driven prediction of lattice constants in transition metal dichalcogenides |
| title_short | Machine learning driven prediction of lattice constants in transition metal dichalcogenides |
| title_sort | machine learning driven prediction of lattice constants in transition metal dichalcogenides |
| topic | Machine learning Artificial intelligence Gradient Boosting Regression Gradient Descent RMSE MAE |
| url | https://www.nepjol.info/index.php/BIBECHANA/article/view/57732 |
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