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Progress in Chemistry 2022, Vol. 34 Issue (12): 2619-2637 DOI: 10.7536/PC220524 Previous Articles   Next Articles

• CONTENTS •

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: Revised: Online: Published:
  • 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)
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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
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
Fig. 2 The general procedure of ML-assisted high-throughput computational screening of MOFs for application
Fig. 3 The classification of descriptors
Fig. 4 Diagram of Machine learning algorithm
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%
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
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]
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]
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
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
Fig. 7 Schematic diagrams for training DNN: (a) normal process; (b) transfer learning process[93]
Fig. 8 Schematic of decision tree model for design of UiO-66-Ds[97]
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]
Fig. 10 Workflows for curating datasets for solvent removal and thermal stability[113]
Fig. 11 Distributions of several illustrative ML descriptors used to represent the local environment of MOF and COF atoms[118]
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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