Method for generating and screening MOFs with high methane adsorption rate

A technology of adsorption rate and methane, applied in the field of intelligent generation and screening of functional MOFs, can solve problems such as slow experimental progress, and achieve the effects of accurate classification, saving time and manpower, and high screening accuracy and efficiency.

Pending Publication Date: 2020-10-09
BEIJING UNIV OF CHEM TECH
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However, the traditional method requires a lot of manu...

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  • Method for generating and screening MOFs with high methane adsorption rate
  • Method for generating and screening MOFs with high methane adsorption rate
  • Method for generating and screening MOFs with high methane adsorption rate

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Embodiment 1

[0038] The actual data is used to analyze the effect of the model proposed by the present invention. Experimental basis figure 1 The flowchart shown is implemented in Python and the model is trained using an NVIDIA Tesla K80 GPU. The database used in the present invention is the crystal information file (CIF) containing 51163 MOFs obtained from the paper "In silico discovery of metal-organicframeworks for precombustion CO2capture using a genetic algorithm" published by Chung YG et al. on "Science Advances" in 2016 database. SMILES uses a string of strings to describe a chemical structure. Molecular fingerprinting deconstructs and encodes molecules into bit strings, and checks whether there is a predefined set of functional groups through a simple "yes-no" check. MACCS, RDKit, Morgan, and Avalon fingerprints are Binary strings of varying lengths. The present invention uses Open Babel software to convert CIF into SMILES and uses the RDKit toolkit to calculate 4 kinds of molec...

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Abstract

The invention discloses a method for generating and screening MOFs with a high methane adsorption rate. The method comprises the steps of acquiring simplified molecular linear input specifications ofMOFs in a data set, and calculating molecular fingerprints through SMILES; generating an SMILES representation form of a large number of hypothetical MOFs by using a recurrent neural network model; designing an evaluation model to judge the effectiveness of the generated sample; designing and comparing classification effects of different classifiers and different input data, and screening out theclassifier with the optimal performance; finally, using the trained classifier for predicting the methane adsorption performance of the samples evaluated as effective hypothesis MOFs, screening out the hypothesis MOFs with the high methane adsorption rate, and providing a reference for synthesizing the MOFs with the high methane adsorption rate in a laboratory.

Description

technical field [0001] The invention belongs to the technical field of intelligent generation and screening of functional MOFs, and in particular relates to a method for generating and screening MOFs with high methane adsorption rate. Background technique [0002] Methane is a widely used clean energy, but its traditional storage method is relatively energy-intensive, and people are still looking for better storage methods. Since metal-organic frameworks (Metal-Organic Frameworks, MOFs) are a type of crystalline porous structure with a periodic network structure formed by self-assembly of inorganic metal centers (metal ions or metal clusters) and bridging organic ligands. Materials, which have nanoscale pores, these pores enable MOFs to effectively capture gas molecules, so MOFs can be used for methane storage, and MOFs with higher methane adsorption rates are the focus of research by technicians. [0003] For the problem of finding or designing new MOFs, the traditional re...

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Application Information

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IPC IPC(8): G16C60/00G06K9/62G06N3/04G06N3/08G06N7/00G06N20/10G01N33/00
CPCG16C60/00G06N3/049G06N3/08G06N20/10G01N33/0047G06N7/01G06N3/045G06F18/24
Inventor 卢罡郭安然阳庆元李睿琪
Owner BEIJING UNIV OF CHEM TECH
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