设计,合成和选COF用于CO2通过高斯过程吸附
Jian Guan1, Zhenhua Dai1, Hang Zhou2
1Department of Environmental Science, Institute of Eco-Chongming, School of Ecological and Environmental Sciences, East China Normal University, Shanghai 200241, China.
ACS applied materials & interfaces
|October 13, 2025
概括
设计用于二氧化碳捕获的共价有机框架 (COF) 通过新的计算-实验框架得到了改进. 这种机器学习方法整合了化学和合成数据,以准确地预测二氧化碳吸附率.
科学领域:
- 材料科学 材料科学 材料科学
- 化学工程是化学工程的重要组成部分.
- 计算化学计算化学
背景情况:
- 有效的二氧化碳 (CO2) 捕获对于减缓气候变化至关重要.
- 共价有机框架 (COFs) 对二氧化碳吸附有希望,但它们的设计复杂.
- 现有的计算方法往往忽略了影响现实世界COF性能的重要化学和合成因素.
研究的目的:
- 开发一个集成的计算-实验框架,用于预测COF中的二氧化碳吸附.
- 将机器学习与实验验证相结合,以改进COF设计.
- 确定影响COF二氧化碳吸附能力的关键结构和合成参数.
主要方法:
- 对240个独特的COF (617个样本) 进行分析,实验测量了CO2吸附数据.
- 开发高斯过程 (GP) 和CatBoost机器学习模型来预测二氧化碳吸附.
- 将化学结构,合成参数和测量协议纳入预测模型.
- 利用SHAP分析来解释模型预测并识别有影响力的特征.
主要成果:
- 与CatBoost模型相比,GP模型表现出更高的概括性和不确定性量化.
- SHAP分析强调了COF类型和合成条件在吸附预测中的重要性.
- 从181个构建块中生成了5557个潜在的COF结构,在推的合成条件下.
- 实验验证证证实了该框架对新合成的COF的预测准确性.
结论:
- 集成的计算-实验框架为优化CO2捕获CO2的COF设计提供了一个实用的方法.
- 机器学习模型,特别是GP,可以通过考虑各种化学和合成因素,有效地预测二氧化碳吸附.
- 这一框架加速了用于碳捕获应用的高性能COF的发现和开发.
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