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使用人工神经网络与小组贡献方法相结合,预测深层环氧溶剂的声速

Ayat Hussein Adhab¹, Morug Salih Mahdi², Hardik Doshi³

¹Department of Pharmacy, Al-Zahrawi University College, Karbala, Iraq.

Scientific reports

|August 10, 2025

概括

预测在深层水溶剂 (DESs) 中的声音速度至关重要. 结合集团贡献 (GC) 和人工神经网络 (ANN) 的新模型准确地估计了这种特性,有助于设计新的溶剂.

科学领域:

物理化学物理化学
热力学是一种热力学.
计算化学计算化学

背景情况:

准确地预测深度性溶剂 (DES) 的生理化学性质,对于新型溶剂的开发至关重要.
声速是一个关键的热力学属性,但对DESs的实验数据很少.
现有的模型在准确估计理想气体热容量方面面临挑战,这是一个关键参数.

研究的目的:

开发一个可靠的热力学模型来估计各种温度和压力的DES中的声音速度.
通过使用机器学习方法来解决实验数据的局限性.
准确预测DES的理想气体热容量,这通常是不可用的.

主要方法:

使用集团贡献 (GC) 方法来估计DESs的临界温度,体积和离心系数.
开发了一个人工神经网络 (ANN) 模型,结合GC估计的参数,以预测声音的速度.
采用CatBoost机器学习算法与GC方法一起作为一种替代的预测方法.
使用累积频率图和杆方法验证模型性能.

主要成果:

该ANN+GC模型在预测声音速度方面取得了高准确性,绝对相对偏差 (ARD%) 为0.032%,R2为0.998.
在ANN+GC和ML+GC两种方法中,与传统的基于关联的模型相比,ARD%显著降低.
杆分析证实了拟议模型和输入数据的可靠性和准确性.

结论:

结合GC和机器学习 (ANN或CatBoost) 的方法提供了一个非常准确和可靠的方法来预测DES的声音速度.
这些模型有效地根据分子结构估计声音的速度,为设计新的DES提供了有价值的工具.
开发的方法在DES中声音速度的实验数据有限或不可用时特别有用.

关键词:

人工智能的人工智能是人工智能.在 CatBoost 中使用 CatBoost.深深的欧特克斯的欧特克斯的集团的贡献集团的贡献机器学习机器学习神经网络的神经网络的神经网络声音的速度是声音的速度.

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