机器学习加速多孔吸附剂筛选发现的研究进展

doi:10.16085/j.issn.1000-6613.2025-0189

摘要/Abstract

摘要：

吸附剂研究是吸附分离研究的核心，加速新型吸附分离技术发展的关键在于多孔吸附剂的筛选。金属有机框架等新型多孔材料在吸附分离领域受到了广泛关注，近年其数量呈爆炸式增长，但这也给吸附剂筛选带来了压力。机器学习引领了多孔材料在发现、设计和应用上的创新突破，正推动多孔吸附剂研究进入数据驱动的全新范式。本文介绍了近年来机器学习在多孔吸附剂领域的研究现状，通过关键案例研究梳理了多孔材料数据库、吸附性能预测及其他相关机器学习任务上的进展，分析了在多孔材料机器学习中模型输入的原理和特点。最后总结出标准化数据库、促进知识迁移、弥合实验与模拟数据的差异及可解释模型是未来多孔吸附剂机器学习研究的发展方向。本文为使用机器学习开发新型多孔吸附剂的研究者提供了简明的资源。

关键词: 吸附剂, 多孔材料, 机器学习, 数据驱动, 预测, 分离, 算法

Abstract:

Adsorbent research is crucial in the field of adsorption and separation, so the key to accelerating the development of new adsorption separation technology lies in the screening of porous adsorbents. New porous materials of metal-organic frameworks have received widespread attention in the field of adsorption and separation. The number of them has exploded in recent years, but it has also brought pressure to the screening of adsorbents. Machine learning has brought innovative breakthroughs in the discovery, design and application of porous materials, leading the research of porous adsorbents into a new data-driven paradigm. This article introduced the current status of machine learning research in the field of porous adsorbents in recent years. Through key case studies, it sorted out the progress in the database of porous materials, adsorption performance prediction and other related machine learning works, and analyzed the principles and characteristics of model input in porous material machine learning. Finally, it was concluded that standardized databases, knowledge transfer, bridging the gap between experimental and simulation data and interpretable models were the future development directions of machine learning research on porous adsorbents.The article provided concise resources for researchers who wanted to use machine learning to develop new porous adsorbents.

Key words: adsorbent, porous materials, machine learning, data-driven, prediction, separation, algorithm

中图分类号:

TQ424

杨证禄, 杨立峰, 路晓飞, 锁显, 张安运, 崔希利, 邢华斌. 机器学习加速多孔吸附剂筛选发现的研究进展[J]. 化工进展, 2025, 44(8): 4288-4301.

YANG Zhenglu, YANG Lifeng, LU Xiaofei, SUO Xian, ZHANG Anyun, CUI Xili, XING Huabin. Advances in machine learning accelerating the screening and discovery of porous adsorbents[J]. Chemical Industry and Engineering Progress, 2025, 44(8): 4288-4301.

图/表 13

参考文献 55

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名称	材料种类	数据来源	样本量	包含信息
CoRE MOF 2019	MOF	实验	14142	结构文件，几何结构特征
hMOF	MOF	计算机模拟	137953	结构文件，几何结构特征，35bar（1bar=0.1MPa）和298K下的模拟甲烷吸附量
CoRE COF	COF	实验	187	结构文件
CURATED COF	COF	实验	324	经过DFT计算和赋予电荷的结构文件
ReDD-COFFEE	COF	计算机模拟	268687	结构文件
IZA结构数据库	沸石	实验	258以上^①	结构文件，几何结构特征，XRD，核磁共振（NMR）
虚拟沸石数据库^②	沸石	计算机模拟	260万以上	结构文件
虚拟ABC-6沸石数据库^②	沸石	计算机模拟	84292	结构文件
PCM的H₂吸附数据库^②	多孔碳	实验	2072	68种多孔碳的材料特征以及在不同温度压力下的氢吸附数据
PPN模拟建模结构数据库^②	多孔聚合物	计算机模拟	10237	结构文件
DigiMOF	MOF	实验	52680	MOF的合成方法条件
SynMOF	MOF	实验	983	结构文件，MOF的合成方法条件

名称	材料种类	数据来源	样本量	包含信息
CoRE MOF 2019	MOF	实验	14142	结构文件，几何结构特征
hMOF	MOF	计算机模拟	137953	结构文件，几何结构特征，35bar（1bar=0.1MPa）和298K下的模拟甲烷吸附量
CoRE COF	COF	实验	187	结构文件
CURATED COF	COF	实验	324	经过DFT计算和赋予电荷的结构文件
ReDD-COFFEE	COF	计算机模拟	268687	结构文件
IZA结构数据库	沸石	实验	258以上^①	结构文件，几何结构特征，XRD，核磁共振（NMR）
虚拟沸石数据库^②	沸石	计算机模拟	260万以上	结构文件
虚拟ABC-6沸石数据库^②	沸石	计算机模拟	84292	结构文件
PCM的H₂吸附数据库^②	多孔碳	实验	2072	68种多孔碳的材料特征以及在不同温度压力下的氢吸附数据
PPN模拟建模结构数据库^②	多孔聚合物	计算机模拟	10237	结构文件
DigiMOF	MOF	实验	52680	MOF的合成方法条件
SynMOF	MOF	实验	983	结构文件，MOF的合成方法条件

算法	特点	优点	缺点	适用范围
支持向量机	通过核技巧（kernel trick）处理非线性问题	适合小样本高维数据，对噪声和过拟合的抗干扰能力强	计算复杂度高，不适合大规模数据	处理中小规模的高维特征数据或非线性边界问题
随机森林	通过构建多个决策树来拟合目标变量与特征	抗过拟合、噪声和缺失值能力强，支持并行训练	性能对参数敏感但依赖调参，模型可解释性差	处理中大规模高维特征的数据
多元线性回归	通过最小二乘法拟合目标变量与特征的线性关系	模型简单，计算效率高，可拓展性和解释性强	无法处理非线性关系	处理低维且特征间独立性较强的非线性关系数据
人工神经网络	基于多层非线性计算节点的映射结构，实现对高维度非线性关系的建模	模型拟合上限高，自动学习特征且不需要特征工程	计算及数据耗费高、调参复杂、可解释较差	处理大规模数据的复杂非线性问题和端到端问题
梯度增强回归	通过集成多棵弱决策树来预测，通过梯度下降减小误差	预测精度高，可处理混合类型特征和缺失值	训练速度慢，难以并行化；易过拟合	对中小规模数据的复杂非线性关系进行预测
极限梯度提升（XGBoost）	梯度增强算法的优化实现，集成正则化项防过拟合、支持多线程计算并自带缺失值自处理机制	计算效率高，支持分布式训练	内存占用较大，因此不适合超大规模数据	对特征维度较高但样本量适中的数据进行预测
轻量级梯度提升机器学习（LGBM）	使用直方图算法的梯度提升决策树框架	训练速度快且内存占用小，支持分布式训练和图形处理器（GPU）加速	小数据易过拟合，对噪声和稀疏数据敏感，可解释性差	对特征维度较高且样本量较大的数据进行快速预测
最小绝对值收缩和选择算子（LASSO）回归	在线性回归中加入L1正则化项来实现特征选择	可自动筛选重要特征，模型训练速度快	限于线性或类线性关系，对高相关特征的选择稳定性差	高维并且需要特征选择的稀疏数据
核岭回归	岭回归与核方法的结合，通过L2正则化减轻过拟合	可处理非线性关系，避免显式高维计算，抗过拟合	存储和计算复杂度高，不适合大规模数据	中小规模数据的非线性回归问题
高斯过程分类	基于贝叶斯框架，假设数据服从高斯过程，输出预测概率	不需要复杂的特征工程，可给出预测不确定性	计算复杂度高，不适合大规模数据	中小规模数据的非线性回归问题或需概率解释的场景

算法	特点	优点	缺点	适用范围
支持向量机	通过核技巧（kernel trick）处理非线性问题	适合小样本高维数据，对噪声和过拟合的抗干扰能力强	计算复杂度高，不适合大规模数据	处理中小规模的高维特征数据或非线性边界问题
随机森林	通过构建多个决策树来拟合目标变量与特征	抗过拟合、噪声和缺失值能力强，支持并行训练	性能对参数敏感但依赖调参，模型可解释性差	处理中大规模高维特征的数据
多元线性回归	通过最小二乘法拟合目标变量与特征的线性关系	模型简单，计算效率高，可拓展性和解释性强	无法处理非线性关系	处理低维且特征间独立性较强的非线性关系数据
人工神经网络	基于多层非线性计算节点的映射结构，实现对高维度非线性关系的建模	模型拟合上限高，自动学习特征且不需要特征工程	计算及数据耗费高、调参复杂、可解释较差	处理大规模数据的复杂非线性问题和端到端问题
梯度增强回归	通过集成多棵弱决策树来预测，通过梯度下降减小误差	预测精度高，可处理混合类型特征和缺失值	训练速度慢，难以并行化；易过拟合	对中小规模数据的复杂非线性关系进行预测
极限梯度提升（XGBoost）	梯度增强算法的优化实现，集成正则化项防过拟合、支持多线程计算并自带缺失值自处理机制	计算效率高，支持分布式训练	内存占用较大，因此不适合超大规模数据	对特征维度较高但样本量适中的数据进行预测
轻量级梯度提升机器学习（LGBM）	使用直方图算法的梯度提升决策树框架	训练速度快且内存占用小，支持分布式训练和图形处理器（GPU）加速	小数据易过拟合，对噪声和稀疏数据敏感，可解释性差	对特征维度较高且样本量较大的数据进行快速预测
最小绝对值收缩和选择算子（LASSO）回归	在线性回归中加入L1正则化项来实现特征选择	可自动筛选重要特征，模型训练速度快	限于线性或类线性关系，对高相关特征的选择稳定性差	高维并且需要特征选择的稀疏数据
核岭回归	岭回归与核方法的结合，通过L2正则化减轻过拟合	可处理非线性关系，避免显式高维计算，抗过拟合	存储和计算复杂度高，不适合大规模数据	中小规模数据的非线性回归问题
高斯过程分类	基于贝叶斯框架，假设数据服从高斯过程，输出预测概率	不需要复杂的特征工程，可给出预测不确定性	计算复杂度高，不适合大规模数据	中小规模数据的非线性回归问题或需概率解释的场景

参数	评估对象	定义	特征
决定系数（R²），交叉验证中与交叉验证决定系数（Q²）等效	回归任务	表示模型对数据的拟合程度	取值范围是[0,1]，越大预测越准；量纲为1指标；对异常值敏感
均方误差（MSE）	回归任务	预测与真实值的平均平方差	数值越小预测越准，量纲为原数据量纲平方，对异常值敏感
均方根误差（RMSE）	回归任务	MSE的算术平方根	量纲与原数据一致，对异常值敏感
平均绝对误差（MAE）	回归任务	预测值与真实值绝对差的平均值	量纲与原数据一致，对异常数据不敏感
准确率（ACC）	分类任务	分类正确的样本数占比	取值范围是[0,1]，越大预测越准；无法反映少数类的预测准确性
F1分数（F1 score）	分类任务	精确率和召回率的调和平均	取值范围是[0,1]，值越高模型越准确；对类别不平衡敏感