用主动学习和分子动力学预测PFAS扩散系数
Archana Jagadisan1, Hakim Boukhalfa1, Mohamed Mehana1
1Earth and Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87454, United States.
Environmental science & technology
|November 11, 2025
概括
科学家们开发了一种机器学习框架,以预测14000多种酸和多酸物质 (PFAS) 的环境扩散. 这种方法有效地模拟污染物运输,帮助风险评估和补救策略.
科学领域:
- 环境化学环境化学
- 计算化学的计算化学
- 毒理学 毒理学 毒理学
背景情况:
- 和多醇基物质 (PFAS) 是持久性,有毒的合成化合物,具有广泛的环境污染.
- 预测PFAS的环境命运对于风险评估至关重要,但由于缺乏扩散系数数据而受到阻碍.
- 实验和传统的计算方法对于庞大的PFAS化学空间是不够的.
研究的目的:
- 开发一个数据高效的计算框架来预测PFAS扩散系数.
- 为了在广泛的PFAS中实现准确的环境运输建模.
- 支持知情的环境风险评估和补救计划.
主要方法:
- 集成机器学习 (ML) 和分子动力学 (MD) 与主动学习.
- 用于ML模型的化学图形表示和物理化学描述符.
- 采用以不确定性为基础的抽样,以指导有针对性的MD模拟和模型再培训.
主要成果:
- 在扩散系数预测方面取得了显著的性能改善 (R2从0.095到0.907,平均相对误差减少88%).
- 证明了基于不确定性的主动学习对随机抽样的有效性,以有效地进行化学空间探索.
- 成功启用了数千个PFAS分子的属性预测.
结论:
- 开发的框架提供了一个计算效率高,准确的方法来预测PFAS扩散系数.
- 这种方法促进了大规模的环境命运评估和设计有效的补救策略.
- 支持积极管理PFAS污染风险.
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