A Transfer Learning Study of Gas Adsorption in Metal-Organic Frameworks

Small Data ◽

Data Sets ◽

Target Task ◽

Direct Training ◽

Adsorption Data ◽

Metal-organic frameworks (MOFs) are a class of materials promising for gas adsorption due to their highly tunable nano-porous structures and host-guest interactions. While machine learning (ML) has been leveraged to aid the design or screen of MOFs for different purposes, the needs of big data are not always met, limiting the applicability of ML models trained against small data sets. In this work, we introduce a transfer learning technique to improve the accuracy and applicability of ML models trained with small amount of MOF adsorption data. This technique leverages potentially shareable knowledge from a source task to improve the models on the target tasks. As demonstrations, a deep neural network (DNN) trained on H2 adsorption data with 13,506 MOF structures at 100 bar and 243 K is used as the source task. When transferring knowledge from the source task to H2 adsorption at 100 bar and 130 K (one target task), the predictive accuracy on target task was improved from 0.960 (direct training) to 0.991 (transfer learning). We also tested transfer learning across different gas species (i.e. from H2 to CH4), with predictive accuracy of CH4 adsorption being improved from 0.935 (direct training) to 0.980 (transfer learning). Based on further analysis, transfer learning will always work on the target tasks with low generalizability. However, when transferring the knowledge from the source task to Xe/Kr adsorption, the transfer learning does not improve the predictive accuracy, which is attributed to the lack of common descriptors that is key to the underlying knowledge.

Transfer Learning Study of Gas Adsorption in Metal–Organic Frameworks

ACS Applied Materials & Interfaces ◽

10.1021/acsami.0c06858 ◽

2020 ◽

Vol 12 (30) ◽

pp. 34041-34048 ◽

Cited By ~ 4

Author(s):

Ruimin Ma ◽

Yamil J. Colón ◽

Tengfei Luo

Keyword(s):

Transfer Learning ◽

Gas Adsorption ◽

Learning Study ◽

Message Passing Neural Networks for Partial Charge Assignment to Metal-Organic Frameworks

10.26434/chemrxiv.12298487 ◽

2020 ◽

Author(s):

Ali Raza ◽

Arni Sturluson ◽

Cory Simon ◽

Xiaoli Fern

Keyword(s):

Electronic Structure ◽

Message Passing ◽

Gas Adsorption ◽

Computational Cost ◽

Electronic Structure Calculations ◽

High Fidelity ◽

Partial Charges ◽

Metal Organic ◽

Structure Calculations

Virtual screenings can accelerate and reduce the cost of discovering metal-organic frameworks (MOFs) for their applications in gas storage, separation, and sensing. In molecular simulations of gas adsorption/diffusion in MOFs, the adsorbate-MOF electrostatic interaction is typically modeled by placing partial point charges on the atoms of the MOF. For the virtual screening of large libraries of MOFs, it is critical to develop computationally inexpensive methods to assign atomic partial charges to MOFs that accurately reproduce the electrostatic potential in their pores. Herein, we design and train a message passing neural network (MPNN) to predict the atomic partial charges on MOFs under a charge neutral constraint. A set of ca. 2,250 MOFs labeled with high-fidelity partial charges, derived from periodic electronic structure calculations, serves as training examples. In an end-to-end manner, from charge-labeled crystal graphs representing MOFs, our MPNN machine-learns features of the local bonding environments of the atoms and learns to predict partial atomic charges from these features. Our trained MPNN assigns high-fidelity partial point charges to MOFs with orders of magnitude lower computational cost than electronic structure calculations. To enhance the accuracy of virtual screenings of large libraries of MOFs for their adsorption-based applications, we make our trained MPNN model and MPNN-charge-assigned computation-ready, experimental MOF structures publicly available.

Classification of jujube defects in small data sets based on transfer learning

Neural Computing and Applications ◽

10.1007/s00521-021-05715-2 ◽

2021 ◽

Author(s):

Jianping Ju ◽

Hong Zheng ◽

Xiaohang Xu ◽

Zhongyuan Guo ◽

Zhaohui Zheng ◽

...

Keyword(s):

Transfer Learning ◽

Loss Function ◽

Training Model ◽

Parameter Distribution ◽

Test Accuracy ◽

Small Data ◽

Data Sets ◽

Data Set ◽

Small Data Sets

AbstractAlthough convolutional neural networks have achieved success in the field of image classification, there are still challenges in the field of agricultural product quality sorting such as machine vision-based jujube defects detection. The performance of jujube defect detection mainly depends on the feature extraction and the classifier used. Due to the diversity of the jujube materials and the variability of the testing environment, the traditional method of manually extracting the features often fails to meet the requirements of practical application. In this paper, a jujube sorting model in small data sets based on convolutional neural network and transfer learning is proposed to meet the actual demand of jujube defects detection. Firstly, the original images collected from the actual jujube sorting production line were pre-processed, and the data were augmented to establish a data set of five categories of jujube defects. The original CNN model is then improved by embedding the SE module and using the triplet loss function and the center loss function to replace the softmax loss function. Finally, the depth pre-training model on the ImageNet image data set was used to conduct training on the jujube defects data set, so that the parameters of the pre-training model could fit the parameter distribution of the jujube defects image, and the parameter distribution was transferred to the jujube defects data set to complete the transfer of the model and realize the detection and classification of the jujube defects. The classification results are visualized by heatmap through the analysis of classification accuracy and confusion matrix compared with the comparison models. The experimental results show that the SE-ResNet50-CL model optimizes the fine-grained classification problem of jujube defect recognition, and the test accuracy reaches 94.15%. The model has good stability and high recognition accuracy in complex environments.

Auto-sharing parameters for transfer learning based on multi-objective optimization

Integrated Computer-Aided Engineering ◽

10.3233/ica-210655 ◽

2021 ◽

pp. 1-13

Author(s):

Hailin Liu ◽

Fangqing Gu ◽

Zixian Lin

Keyword(s):

Transfer Learning ◽

Optimization Problem ◽

Data Sets ◽

Multi Objective Optimization ◽

Particle Swarm Optimizer ◽

Real World Data ◽

Data Set ◽

Target Task ◽

Main Research ◽

Multi Objective

Transfer learning methods exploit similarities between different datasets to improve the performance of the target task by transferring knowledge from source tasks to the target task. “What to transfer” is a main research issue in transfer learning. The existing transfer learning method generally needs to acquire the shared parameters by integrating human knowledge. However, in many real applications, an understanding of which parameters can be shared is unknown beforehand. Transfer learning model is essentially a special multi-objective optimization problem. Consequently, this paper proposes a novel auto-sharing parameter technique for transfer learning based on multi-objective optimization and solves the optimization problem by using a multi-swarm particle swarm optimizer. Each task objective is simultaneously optimized by a sub-swarm. The current best particle from the sub-swarm of the target task is used to guide the search of particles of the source tasks and vice versa. The target task and source task are jointly solved by sharing the information of the best particle, which works as an inductive bias. Experiments are carried out to evaluate the proposed algorithm on several synthetic data sets and two real-world data sets of a school data set and a landmine data set, which show that the proposed algorithm is effective.

Predictive Acid Gas Adsorption in Rare Earth DOBDC Metal–Organic Frameworks via Complementary Cluster and Periodic Structure Models

The Journal of Physical Chemistry C ◽

10.1021/acs.jpcc.0c08282 ◽

2020 ◽

Vol 124 (49) ◽

pp. 26801-26813

Author(s):

Dayton J. Vogel ◽

Zachary R. Lee ◽

Caitlin A. Hanson ◽

Susan E. Henkelis ◽

Caris M. Smith ◽

...

Keyword(s):

Rare Earth ◽

Periodic Structure ◽

Gas Adsorption ◽

Acid Gas ◽

Non-Interpenetrated Metal–Organic Frameworks Based on Copper(II) Paddlewheel and Oligoparaxylene-Isophthalate Linkers: Synthesis, Structure, and Gas Adsorption

Journal of the American Chemical Society ◽

10.1021/jacs.5b12312 ◽

2016 ◽

Vol 138 (10) ◽

pp. 3371-3381 ◽

Cited By ~ 78

Author(s):

Yong Yan ◽

Michal Juríček ◽

François-Xavier Coudert ◽

Nicolaas A. Vermeulen ◽

Sergio Grunder ◽

...

Keyword(s):

Gas Adsorption ◽

Screening the Effect of Water Vapour on Gas Adsorption Performance: Application to CO2Capture from Flue Gas in Metal-Organic Frameworks

ChemSusChem ◽

10.1002/cssc.201601816 ◽

2017 ◽

Vol 10 (7) ◽

pp. 1543-1553 ◽

Cited By ~ 40

Author(s):

Nicolas Chanut ◽

Sandrine Bourrelly ◽

Bogdan Kuchta ◽

Christian Serre ◽

Jong-San Chang ◽

...

Keyword(s):

Water Vapour ◽

Flue Gas ◽

Gas Adsorption ◽

Adsorption Performance ◽

Effect Of Water ◽

Enhanced performance in gas adsorption and Li ion batteries by docking Li+ in a crown ether-based metal–organic framework

Chemical Communications ◽

10.1039/c5cc09935h ◽

2016 ◽

Vol 52 (14) ◽

pp. 3003-3006 ◽

Cited By ~ 38

Author(s):

Linyi Bai ◽

Binbin Tu ◽

Yi Qi ◽

Qiang Gao ◽

Dong Liu ◽

...

Keyword(s):

Crown Ether ◽

Metal Ions ◽

Gas Adsorption ◽

Metal Organic Framework ◽

Li Ion Batteries ◽

Metal Organic ◽

Li Ion ◽

Organic Framework

Incorporating supramolecular recognition units, crown ether rings, into metal–organic frameworks enables the docking of metal ions through complexation for enhanced performance.

Metal–organic frameworks with the gyroid surface: structures and applications

Dalton Transactions ◽

10.1039/d0dt04234j ◽

2021 ◽

Vol 50 (14) ◽

pp. 4757-4764

Author(s):

Yan Yan Li ◽

Dong Luo ◽

Kun Wu ◽

Xiao-Ping Zhou

Keyword(s):

Gas Adsorption ◽

Review Article ◽

Surface Structures ◽

Luminescent Materials ◽

This review article summarizes the assembly, structures, and topologies of gyroidal metal–organic frameworks. Their applications in gas adsorption, catalysis, sensors, and luminescent materials are also discussed in detail.