在水和恶劣环境中的金属有机框架稳定性,基于在多种WS24数据集上训练的数据驱动模型
Gianmarco G Terrones1, Shih-Peng Huang2, Matthew P Rivera1
1Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.
Journal of the American Chemical Society
|July 10, 2024
概括
预测金属有机框架 (MOF) 的水稳定性对于其应用至关重要. 这项研究显著扩大了机器学习模型的可用数据,改善了强大的MOF发现的预测.
科学领域:
- 材料科学
- 化学学
- 计算科学
背景情况:
- 金属有机框架 (MOF) 是气体分离和催化有前途的多孔材料.
- 限制水的稳定性阻碍了许多MOF的实际应用.
- 目前对水稳定的MOF的设计策略往往是限制性的.
研究的目的:
- 通过机器学习提高MOF水稳定性的预测.
- 扩大用于MOF稳定性的机器学习模型的数据集.
- 为了促进各种应用的新型,水稳定的MOF的发现.
主要方法:
- 通过半自动手稿分析添加911个含水稳定性标签的MOF,编辑了新的数据集 (WS24).
- 开发并改进了用于预测水和酸性稳定的机器学习模型.
- 综合稳定模型与基因算法用于MOF的高通量选.
主要成果:
- 增加了400%以上的MOF水稳定训练数据.
- 在预测水和酸性稳定性方面实现了改进的机器学习模型性能 (测试ROC-AUC> 0.8).
- 确定了强大的MOF的金属和几何特异性设计原则.
结论:
- 扩展的数据集和改进的ML模型加速了水稳定的MOF的发现.
- 开发的工具可以快速选大型MOF库的多变量稳定性.
- 这项工作有助于在直接捕获空气和水处理等领域推进MOF应用.
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