Prediction of Flash Point of Materials Using Bayesian Kernel Machine Regression Based on Gaussian Processes With LASSO-Like Spike-and-Slab Hyperprior

IF 2.3 4区 化学 Q1 SOCIAL WORK Journal of Chemometrics Pub Date : 2024-07-30 DOI:10.1002/cem.3586
Keunhong Jeong, Ji Hyun Nam, Seul Lee, Jahyun Koo, Jooyeon Lee, Donghyun Yu, Seongil Jo, Jaeoh Kim
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Abstract

The determination of flash points is a critical aspect of chemical safety, essential for assessing explosion hazards and fire risks associated with flammable solutions. With the advent of new chemical blends and the increasing complexity of chemical waste management, the need for accurate and reliable flash point prediction methods has become more pronounced. This study introduces a novel predictive approach using Bayesian kernel machine regression (BKMR) with Gaussian process priors, designed to meet the growing demand for precise flash point estimation in the context of chemical safety. The BKMR model, underpinned by Bayesian statistics, offers a comprehensive framework that not only quantifies prediction uncertainty but also enhances interpretability amidst experimental data variability. Our comparative analysis reveals that BKMR surpasses traditional predictive models, including support vector machines, kernel ridge regression, and Gaussian process regression, in terms of accuracy and reliability across multiple metrics. By elucidating the intricate interactions between molecular features and flash point properties, the BKMR model provides profound insights into the chemical dynamics that influence flash point determinations. This study signifies a methodological leap in flash point prediction, offering a valuable tool for chemical safety analysis and contributing to the development of safer chemical handling and storage practices.

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利用基于高斯过程的贝叶斯核机器回归和类似于 LASSO 的尖峰和实验室超先验预测材料闪点
闪点的测定是化学品安全的一个重要方面,对于评估与易燃溶液相关的爆炸危险和火灾风险至关重要。随着新型化学混合物的出现和化学废物管理的日益复杂,对准确可靠的闪点预测方法的需求变得更加突出。本研究介绍了一种使用贝叶斯核机器回归(BKMR)和高斯过程先验的新型预测方法,旨在满足在化学品安全方面对精确闪点估计日益增长的需求。以贝叶斯统计为基础的 BKMR 模型提供了一个全面的框架,不仅能量化预测的不确定性,还能在实验数据多变的情况下提高可解释性。我们的比较分析表明,BKMR 在多个指标的准确性和可靠性方面超过了传统的预测模型,包括支持向量机、核岭回归和高斯过程回归。通过阐明分子特征与闪点特性之间错综复杂的相互作用,BKMR 模型对影响闪点测定的化学动力学提供了深刻的见解。这项研究标志着闪点预测方法的飞跃,为化学安全分析提供了宝贵的工具,并有助于开发更安全的化学品处理和储存方法。
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来源期刊
Journal of Chemometrics
Journal of Chemometrics 化学-分析化学
CiteScore
5.20
自引率
8.30%
发文量
78
审稿时长
2 months
期刊介绍: The Journal of Chemometrics is devoted to the rapid publication of original scientific papers, reviews and short communications on fundamental and applied aspects of chemometrics. It also provides a forum for the exchange of information on meetings and other news relevant to the growing community of scientists who are interested in chemometrics and its applications. Short, critical review papers are a particularly important feature of the journal, in view of the multidisciplinary readership at which it is aimed.
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