Yen-Hsiang, Lin, Yi-Pei, Li, Hsin-Hao, Liang, Shiang-Tai, Lin
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引用次数: 0

摘要

背景:蒸气压是化学和环境工程中的一项关键特性。准确预测各种温度范围内的蒸气压对各种应用都至关重要,但传统方法依赖于临界性质测量或量子力学计算,这可能具有局限性,尤其是对于新化学品或特征描述不足的化学品。重要发现:与传统的 PR + COSMOSAC 方法相比,D-MPNN 模型实现了更高的准确度,使用 19,079 个分子的数据集,平均绝对相对偏差(AARD)为 0.617,低于传统方法的 1.36。机器学习方法提供了一种无需额外关键属性数据或量子力学计算的稳健替代方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Advancing Vapor Pressure Prediction: A Machine Learning Approach with Directed Message Passing Neural Networks
Background: Vapor pressure is a critical property in chemical and environmental engineering. Accurately predicting vapor pressure across a range of temperatures is vital for various applications, but traditional methods rely on critical property measurements or quantum mechanical calculations, which can be limiting, especially for new or under-characterized chemicals. Methods: This study employs a machine learning model based on the directed message passing neural network (D-MPNN) architecture to predict the vapor pressure of organic molecules. Various strategies to incorporate temperature effects into the model are explored to improve prediction accuracy. Significant findings: The D-MPNN model achieves significantly better accuracy than the traditional PR + COSMOSAC method, with a lower average absolute relative deviation (AARD) of 0.617 compared to 1.36 for the traditional method, using a dataset of 19,079 molecules. The machine learning approach offers a robust alternative that does not require additional critical property data or quantum mechanical calculations.
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