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机器学习研究PFAS在过渡金属中的中间吸附:用于环境催化剂设计的缩放关系

Junyao Wu^1,2, Wei Gao², Ziyun Wang¹

¹School of Chemical Sciences, University of Auckland, Auckland 1010, New Zealand.

Environmental science & technology

|October 20, 2025

概括

这项研究引入了一个DFT-ML框架,以加快发现用于降解持久性基和多基物质 (PFAS) 的催化剂. 该方法识别了关键中间体,并使用机器学习来预测环境污染物去除催化剂的性能.

科学领域:

环境化学环境化学
材料科学材料科学材料科学
计算化学计算化学

背景情况:

和多基基物质 (PFAS) 是持续的全球污染物.
传统的补救方法对PFAS无效,需要开发新的催化剂.

研究的目的:

开发一个集成的密度功能理论 (DFT) 和机器学习 (ML) 框架,以加速发现PFAS降解催化剂.
提供对过渡金属表面的PFAS降解途径的机械洞察力.

主要方法:

在12个过渡金属表面上对18个关键降解中间体进行系统的DFT计算.
确定脱和碳链裂变的线性缩放关系.
开发一种ML模型,使用基于知识的特征来对吸附模式进行分类.

主要成果:

对于PFAS降解中间体,发现了一致的热力学趋势和线性缩放关系.
在对吸附相关性模式进行分类时,ML模型获得了0.90的F1得分.
该框架使复杂的退化网络的维度减少和路径重建成为可能.

结论:

集成的DFT-ML框架为有效的PFAS催化剂查提供了机制性见解和描述器引导的策略.
这种方法对发现其他持久性有机污染物的催化剂具有潜在的适用性.
该框架可以扩展到更大的数据集,以涵盖不同的PFAS家族和催化剂.

关键词:

在PFAS降解退化过程中.催化剂是一种催化剂.密度函数理论密度函数理论机器学习是机器学习.扩展关系的扩展关系.三酸三酸三酸是什么意思

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