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化学进展 2022, Vol. 34 Issue (12): 2619-2637 DOI: 10.7536/PC220524 前一篇   后一篇

• 综述 •

机器学习辅助高通量筛选金属有机骨架材料

李炜1,*(), 梁添贵1, 林元创1, 吴伟雄1, 李松2   

  1. 1 暨南大学能源电力研究中心 珠海 519070
    2 华中科技大学能源与动力工程学院 武汉 430074
  • 收稿日期:2022-05-16 修回日期:2022-07-08 出版日期:2022-12-24 发布日期:2022-09-19
  • 通讯作者: 李炜
  • 作者简介:

    李炜 博士,副教授。2015年与2020年于华中科技大学获得学士和博士学位. 2021年加入暨南大学能源电力研究中心从事金属有机骨架材料高通量筛选及其在能源存储与转化方面的应用研究。

  • 基金资助:
    中央高校基本科研业务经费(21621039); 广州市基础与应用基础研究基金项目(202201010433); 中国博士后面上项目(2021M701413)
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Machine Learning Accelerated High-Throughput Computational Screening of Metal-Organic Frameworks

Wei Li1(), Tiangui Liang1, Yuanchuang Lin1, Weixiong Wu1, Song Li2   

  1. 1 Energy and Electricity Research Center, Jinan University,Zhuhai 519070, China
    2 School of Energy and Power Engineering, Huazhong University of Science and Technology,Wuhan 430074, China
  • Received:2022-05-16 Revised:2022-07-08 Online:2022-12-24 Published:2022-09-19
  • Contact: Wei Li
  • Supported by:
    Fundamental Research Funds for the Central Universities(21621039); GuangZhou Basic and Applied Basic Research Foundation(202201010433); China Postdoctoral Science Foundation(2021M701413)

金属有机骨架(Metal-organic Frameworks, MOFs)材料具有高比表面积、大孔容和可调控合成等优点,在气体储存、吸附分离、催化等领域受到了广泛关注,近年来其数量呈爆炸式增长的趋势。而高通量计算筛选(High-throughput Computational Screening, HTCS)是从大量材料中发现高性能目标材料与挖掘构效关系最有效的研究方法。在高通量计算筛选过程中产生的数据具有量大、维度多等特点,尤其适合采用机器学习(Machine Learning, ML)进行训练,从而进一步提升筛选效率、深入挖掘多维数据间的构效关系。本综述概述了机器学习辅助高通量筛选金属有机骨架材料的一般流程与常用方法,包括常用描述符、算法与评价标准等,对其在气体储存、分离及催化等领域的研究进展进行了总结,以此明确当前研究中面临的挑战与后续发展方向,助力MOFs材料设计研发。

关键词： 金属有机骨架, 机器学习, 高通量筛选, 分子模拟, 吸附

Metal-organic frameworks (MOFs) with ultrahigh surface area, large pore volume and tunable pore environment are regarded as promising adsorbents in gas adsorption and separation. With the exploding number of possible MOFs, it is an imperative challenge to discover high-performing MOFs for a specific application. High-throughput computational screening (HTCS) has been frequently adopted to identify the suitable adsorbents with remarkably reduced computational time and cost in the past decades. Recently, machine learning (ML) is used to accelerate the HTCS process and explore the structure-property relationship. In this review, we summarize the ML algorithms for gas storage, separation and catalysis by MOFs. Four categories of descriptors widely used in ML are reviewed, including geometrical descriptor (i.e., pore size, surface area), topological descriptor (i.e., pore connectivity, cavity size), chemical descriptor (i.e., atom type, degree of unsaturation), and energy-based descriptor (i.e., cohesive energies, Voronoi energies). Unsupervised learning and supervised learning, such as linear regression (LR), artificial neural network (ANN), support vector machine (SVM), and random forest (RF) are introduced and a complete overview of how these ML algorithms are effectively utilized to assist MOF discovery is provided. Recent research progress on ML algorithms in various applications, such as H2, CH4 storage, carbon capture, noble gas, Cx/Cy separation and catalysis are presented in this work. It is anticipated that ML will play a more vital role in identifying top candidates with increasing number of MOFs. Thus, this review aims to outline fundamental knowledge of ML algorithms for MOF discovery in the fields of gas storage, separation and catalysis.

Contents

1 Introduction

2 Methodology

2.1 High-throughput computational screening

2.2 Machine learning

3 Machine learning accelerated MOFs screening

3.1 Gas storage

3.2 Gas separation

3.3 MOFs physicochemical property

4 Conclusion and outlook

Key words: metal-organic frameworks, machine learning, high-throughput computational screening, molecular simulation, adsorption
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图1 (A)1972年至2022年CSD数据库中MOFs数目增长趋势; (B)高通量筛选流程示意图
Fig. 1 Schematic diagram of (A)growth trends of the number of MOFs in CSD database from 1972 to 2022.(B)high-throughput computational screening methodology
图2 机器学习辅助高通量筛选的常用流程
Fig. 2 The general procedure of ML-assisted high-throughput computational screening of MOFs for application
图3 描述符的分类
Fig. 3 The classification of descriptors
图4 机器学习算法原理图
Fig. 4 Diagram of Machine learning algorithm
表1 机器学习预测MOFs储氢性能
Table 1 The H2 storage performance of MOFs predicted by ML
Researcher Descriptors Algorithms Best Results
Cao[71] Geometrical + Chemical LSSVM, ANFIS, ANN R2 = 0.990, MAE = 0.050 wt%,
MSE = 0.059 wt%
Bucior[73] Energy-based LASSO R2 = 0.960, MAE = 2.40 g/L,
RMSE = 3.10 g/L
Giappa[74] Energy-based KRR, RFR, SVR MAE = 1.160×10-4 Ha
Ahmed[75,76] Geometrical DT, RF, LR, SVM, ERT, etc. R2 = 0.997, MAE = 0.10 wt%,
RMSE = 0.180 wt%
表2 机器学习预测MOFs储甲烷性能
Table 2 The CH4 storage performance of MOFs predicted by ML
Researcher Descriptors Algorithms Best Results
Pardakhti[79] Geometrical + Chemical RF R2 = 0.980, MAPE = 7.180 cm3/g
Wang[80] Geometrical Graph Convolution Neural Networks (GCNN) AUC = 0.978 cm3/ cm3
Beauregard[78] Geometrical + Chemical RF, GA R2 = 0.950, MAPE = 1.370 cm3/g
Fanourgakis[81] Geometrical + Topological RF R2 = 0.990, RMSE = 9.050 cm3/g
Kim[82] Geometrical + Chemical SVM, DT, RF R2 = 0.947, MSE = 0.918 kJ/mol,
MAPE = 3.903 kJ/mol
Gurnani[19] Geometrical + Chemical Feed-forward Neural Networks R2 = 0.990, RMSE = 7.830 cm3/g
Suyetin[20] Geometrical MLR R2 = 0.990
Lee[83] Topological MSGA-FA *
Taw[84] Geometrical + Chemical BO R2 = 0.960
图5 MOF-NET模型图。“Dense”代表全连接层。(a)拓扑嵌入与拓扑嵌入权重的产生。Wself,NBB代表NBBs自身相互作用产生的权重,Wself,EBB代表EBBs自身相互作用产生的权重,Winter 代表相互作用产生的权重。(b)MOF-NET模型主要组成部分。模型由简单的全连接层组成,但某些层的权重由拓扑嵌入[83]
Fig. 5 Schematics of the MOF-NET architecture. The word “Dense” refers to fully connected layers. (a) Topology embedding and the generated weights from topology embedding. Wself,NBB is the self-interaction weight for NBBs, Wself,EBB is the self-interaction weight for EBBs and Winter is the interaction weight. (b) Main architecture of MOF-NET. The MOF-NET consists of simple fully connected layers, but the weights of some layers are generated from the topology embedding[83]
图6 (a) GM模型预测的533 430种CH4体积吸附量结果;(b~f) 示例结构中GM、NGM与GCMC预测吸附曲线结果对比[19]
Fig. 6 (a) Computed volumetric methane uptake vs predicted volumetric methane uptake for each of 533 430 measurements in the curated hMOFs data set;(b~f) NGM and GM predicted isotherms (light-blue and dark-blue curves, respectively) vs GCMC data (white squares) for five randomly selected MOFs[19]
表3 机器学习预测MOFs的分离性能
Table 3 The separation performance predicted by ML algorithms
Separation system Researcher Algorithms Descriptors
Noble gas Xe/Kr Liang[92] Ridge, LASSO, Elastic NET, SVM, Bayesian, ANN, RF, XGB Geometrical
Xe/Kr Ma[93] DNN Geometrical
Ar/Xe/Kr Anderson[94] DNN Geometrical + Topological
Xe/Kr Liu[95] BPNN Chemical
Cx/Cy ethane/ethylene Halder[96] RF Geometrical + Chemical
ethane/ethylene Wu[97] LR, DT, RF, SVM, KNN, GBT Geometrical + Chemical
C4/C7 Tang[98] MRGP Geometrical + Chemical
p-xylene/m-xylene/o-xylene Qiao[99] BPNN, DT Geometrical
Other CH4/H2S Cho[100] RF Geometrical + Chemical
N2/O2 Yan[101] RF, GBT, XGB Geometrical + Chemical
D2/H2 Zhou[102] SVM, RF, GBT, DNN Geometrical + Chemical
表4 机器学习预测MOFs储二氧化碳性能
Table 4 The CO2 capture performance predicted by ML
Researcher Descriptors Algorithms Results
Burner[21] Geometrical + Topological DNN R2 = 0.975, RMSE = 10.0 mmol/g
Dashti[86] Geometrical + Chemical PSO-ANFIS, RBF-ANN,
DE-ANFIS, LSSVM		 R2 = 0.997, MSE = 0.167 mmol/g
Deng[22] Geometrical + Chemical BPNN, RF, DT, SVM R2 = 0.994
Zhang[87] Chemical + Topological RNN, MCTS *
Li[88] Chemical SVM, KNN, DT, SGD, NN R2 = 0.940
图7 DNN训练流程图:(a)一般流程; (b)迁移学习流程[93]
Fig. 7 Schematic diagrams for training DNN: (a) normal process; (b) transfer learning process[93]
图8 UiO-66-Ds设计的决策树的原理图[97]
Fig. 8 Schematic of decision tree model for design of UiO-66-Ds[97]
图9 (a)MLR,(b)DT,(c)GBM 与(d)RF算法预测3 166 602种级联工质对在吸附式热泵中的COPC,(e)低温级与(f)高温级描述符权重[106]
Fig. 9 COPC predicted by varying machine learning models (a)MLR,(b)DT, (c)GBM, and(d)RF, colored by the number density of cascaded AHPs, weights of descriptors at low (e) and high (f) temperatures[106]
图10 为溶剂移除活化和热稳定性数据集整理流程[113]
Fig. 10 Workflows for curating datasets for solvent removal and thermal stability[113]
图11 MOF和COF中原子的局部环境的描述符分布[118]
Fig. 11 Distributions of several illustrative ML descriptors used to represent the local environment of MOF and COF atoms[118]
图12 ML模型与使用 N2 等温线BET 方法(a)预测MOF实际单层面积结果与其(b)差异示意图[120]
Fig. 12 (a) Predicted areas and (b) schematic of deviation of the ML model compared to the BET method using N2 isotherms for true monolayer prediction[120]
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