Scholarlink

<p>In this paper, we propose a robust deep-learning model based on a Quantitative Structure − Property Relationship (QSPR) approach for estimating the critical temperature (TC), critical pressure (PC), acentric factor (ACEN) and normal boiling point (NBP) of any C, H, O, N, S, P, F, Cl, Br, I molecule. The Mordred calculator was used to determine 247 descriptors to characterize the molecules considered in this work. For each evaluated property, multiple neural networks were trained within a <i>bagging</i> framework. The predictions from the final ensemble were successfully tested against a large set of experimental data comprising more than 1700 molecules and compared with those from different recent learning models found in the literature. Comprehensive comparisons and extensive testing highlight the robustness and predictive power of the newly proposed multimodal learning model. The developed prediction tool is available on a website at <a href="https://lrgp-thermoppt.streamlit.app/">https://lrgp-thermoppt.streamlit.app/</a>. Furthermore, a source code for implementing the trained models in Python is available via github <a href="https://github.com/bounac80/AI-ThermPpt">https://github.com/bounac80/AI-ThermPpt</a>.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

AI-powered prediction of critical properties and boiling points: a hybrid ensemble learning and QSPR approach

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月29日

查看全文
  • Roda Bounaceur,
  • Francisco Paes,
  • Romain Privat,
  • Jean-Noël Jaubert

摘要

In this paper, we propose a robust deep-learning model based on a Quantitative Structure − Property Relationship (QSPR) approach for estimating the critical temperature (TC), critical pressure (PC), acentric factor (ACEN) and normal boiling point (NBP) of any C, H, O, N, S, P, F, Cl, Br, I molecule. The Mordred calculator was used to determine 247 descriptors to characterize the molecules considered in this work. For each evaluated property, multiple neural networks were trained within a bagging framework. The predictions from the final ensemble were successfully tested against a large set of experimental data comprising more than 1700 molecules and compared with those from different recent learning models found in the literature. Comprehensive comparisons and extensive testing highlight the robustness and predictive power of the newly proposed multimodal learning model. The developed prediction tool is available on a website at https://lrgp-thermoppt.streamlit.app/. Furthermore, a source code for implementing the trained models in Python is available via github https://github.com/bounac80/AI-ThermPpt.