基于机器学习的硅基纳米复合材料介电常数建模

2020 International Conference on Actual Problems of Electron Devices Engineering (APEDE) Pub Date : 2020-09-24 DOI:10.1109/APEDE48864.2020.9255560

S. Korchagin, Y. Klinaev, D. Terin, S. Romanchuk

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引用次数: 1

摘要

在这项工作中，我们使用机器学习方法解决了硅基纳米复合材料介电常数的预测问题。数学模型和程序已经发展到预测新材料的电物理性质，以及选择最佳的组成。所获得的结果将有助于更快、更便宜地创建新的功能复合材料。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Modeling the Dielectric Constant of Silicon-Based Nanocomposites Using Machine Learning

In this work, we solve the problem of predicting the dielectric constant of silicon-based nanocomposites using machine learning methods. Mathematical models and programs have been developed to predict the electrophysical properties of new materials, as well as to select the optimal composition. The results obtained will be useful for faster and cheaper creation of new functional composites.

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2020 International Conference on Actual Problems of Electron Devices Engineering (APEDE)

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