Machine Learning for the Prediction of Thermodynamic Properties in Amorphous Silicon
This study integrated molecular dynamics (MD) simulations with machine learning techniques, specifically Linear, Ridge, and Support Vector Regression, to predict the thermodynamic properties of amorphous silicon (a-Si) under varying conditions. The MD simulations provided a detailed dataset that cap...
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MDPI AG
2025-05-01
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| author | Nicolás Amigo |
| author_facet | Nicolás Amigo |
| author_sort | Nicolás Amigo |
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| description | This study integrated molecular dynamics (MD) simulations with machine learning techniques, specifically Linear, Ridge, and Support Vector Regression, to predict the thermodynamic properties of amorphous silicon (a-Si) under varying conditions. The MD simulations provided a detailed dataset that captured the atomic-level behavior of the a-Si, which enabled exploration of how thermodynamic factors, such as the cooling rate, temperature, and pressure, affect the material’s density, internal energy, and enthalpy. Machine learning models were trained on this dataset and demonstrated exceptional predictive accuracy with <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> values that exceeded 0.95 and minimal root-mean-square errors. The results reveal that the temperature and pressure significantly influenced the thermodynamic properties of the a-Si, while the cooling rate had a minor effect. The models generated isobaric and isothermal curves, which offered deeper insights into the thermodynamic behavior of the a-Si and complemented traditional MD simulations by providing a more efficient means to explore thermodynamic states. This work highlights the potential of machine learning to accelerate the study of materials by enabling faster exploration of thermodynamic behavior and the generation of additional data. This approach enhances the understanding of the equation of state of a-Si and opens new avenues for applying this hybrid modeling technique to other materials. |
| format | Article |
| id | doaj-art-a3900ac2069c4e6c9596f269e6560c8e |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-a3900ac2069c4e6c9596f269e6560c8e2025-08-20T02:33:43ZengMDPI AGApplied Sciences2076-34172025-05-011510557410.3390/app15105574Machine Learning for the Prediction of Thermodynamic Properties in Amorphous SiliconNicolás Amigo0Departamento de Física, Facultad de Ciencias Naturales, Matemática y del Medio Ambiente, Universidad Tecnológica Metropolitana, Las Palmeras 3360, Ñuñoa, Santiago 7800003, ChileThis study integrated molecular dynamics (MD) simulations with machine learning techniques, specifically Linear, Ridge, and Support Vector Regression, to predict the thermodynamic properties of amorphous silicon (a-Si) under varying conditions. The MD simulations provided a detailed dataset that captured the atomic-level behavior of the a-Si, which enabled exploration of how thermodynamic factors, such as the cooling rate, temperature, and pressure, affect the material’s density, internal energy, and enthalpy. Machine learning models were trained on this dataset and demonstrated exceptional predictive accuracy with <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> values that exceeded 0.95 and minimal root-mean-square errors. The results reveal that the temperature and pressure significantly influenced the thermodynamic properties of the a-Si, while the cooling rate had a minor effect. The models generated isobaric and isothermal curves, which offered deeper insights into the thermodynamic behavior of the a-Si and complemented traditional MD simulations by providing a more efficient means to explore thermodynamic states. This work highlights the potential of machine learning to accelerate the study of materials by enabling faster exploration of thermodynamic behavior and the generation of additional data. This approach enhances the understanding of the equation of state of a-Si and opens new avenues for applying this hybrid modeling technique to other materials.https://www.mdpi.com/2076-3417/15/10/5574thermodynamic propertiesmachine learningstatisticsmolecular dynamics |
| spellingShingle | Nicolás Amigo Machine Learning for the Prediction of Thermodynamic Properties in Amorphous Silicon Applied Sciences thermodynamic properties machine learning statistics molecular dynamics |
| title | Machine Learning for the Prediction of Thermodynamic Properties in Amorphous Silicon |
| title_full | Machine Learning for the Prediction of Thermodynamic Properties in Amorphous Silicon |
| title_fullStr | Machine Learning for the Prediction of Thermodynamic Properties in Amorphous Silicon |
| title_full_unstemmed | Machine Learning for the Prediction of Thermodynamic Properties in Amorphous Silicon |
| title_short | Machine Learning for the Prediction of Thermodynamic Properties in Amorphous Silicon |
| title_sort | machine learning for the prediction of thermodynamic properties in amorphous silicon |
| topic | thermodynamic properties machine learning statistics molecular dynamics |
| url | https://www.mdpi.com/2076-3417/15/10/5574 |
| work_keys_str_mv | AT nicolasamigo machinelearningforthepredictionofthermodynamicpropertiesinamorphoussilicon |