Thermal conductivity of nanoporous silicon: Molecular dynamics simulations and machine learning prediction
DOI (Low Temperature Physics):
https://doi.org/10.1063/10.0042162Ключові слова:
nanoporous silicon, thermal conductivity, molecular dynamics, symbolic regression, machine learningАнотація
Досліджено теплопровідність нанопористого кремнію (p-Si) із застосуванням методів рівноважної молекулярної динаміки та машинного навчання. Проаналізовано ефективність використання різних міжатомних потенціалів у молекулярно-динамічних розрахунках і обґрунтовано вибір потенціалу Tersoff завдяки його стабільності та точності. Значення теплопровідності p-Si обчислено в широкому діапазоні температур і пористостей. За допомогою алгоритму символьної регресії отримано аналітичний вираз, який описує залежність теплопровідності від температури та пористості. Показано, що моделі випадкового лісу та градієнтного бустингу, натреновані для реконструкції автокореляційних функцій теплового потоку та прогнозування теплопровідності, забезпечують вищу точність порівняно з моделями, побудованими на основі методу опорних векторів. Запропонований комбінований підхід забезпечує точне й ефективне прогнозування теплового транспорту в пористих наноструктурах і сприяє розробленню матеріалів для систем теплового менеджементу та енергетичних застосувань.
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