Artificial Intelligence and High-Throughput Computational Workflows Empowering the Fast Screening of Metal-Organic Frameworks for Hydrogen Storage.

Wang, Linmeng; Feng, Shihao; Zhang, Chenjun; Zhang, Xi; Liu, Xiaodan; Gao, Hongyi; Liu, Zhiyuan; Li, Rushuo; Wang, Jingjing; Jin, Xu

Wang, Linmeng; Feng, Shihao; Zhang, Chenjun; Zhang, Xi; Liu, Xiaodan; Gao, Hongyi; Liu, Zhiyuan; Li, Rushuo; Wang, Jingjing; Jin, Xu.

Afiliación

Wang L; Beijing Advanced Innovation Center for Materials Genome Engineering, Beijing Key Laboratory of Function Materials for Molecule & Structure Construction, School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing 100083, PR China.
Feng S; Beijing Advanced Innovation Center for Materials Genome Engineering, Beijing Key Laboratory of Function Materials for Molecule & Structure Construction, School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing 100083, PR China.
Zhang C; Research Institute of Petroleum Exploration & Development, PetroChina, Beijing 100083, PR China.
Zhang X; Research Institute of Petroleum Exploration & Development, PetroChina, Beijing 100083, PR China.
Liu X; Research Institute of Petroleum Exploration & Development, PetroChina, Beijing 100083, PR China.
Gao H; Beijing Advanced Innovation Center for Materials Genome Engineering, Beijing Key Laboratory of Function Materials for Molecule & Structure Construction, School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing 100083, PR China.
Liu Z; Shunde Innovation School, University of Science and Technology Beijing, Shunde 528399, P. R. China.
Li R; Beijing Advanced Innovation Center for Materials Genome Engineering, Beijing Key Laboratory of Function Materials for Molecule & Structure Construction, School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing 100083, PR China.
Wang J; Beijing Advanced Innovation Center for Materials Genome Engineering, Beijing Key Laboratory of Function Materials for Molecule & Structure Construction, School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing 100083, PR China.
Jin X; Beijing Advanced Innovation Center for Materials Genome Engineering, Beijing Key Laboratory of Function Materials for Molecule & Structure Construction, School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing 100083, PR China.

ACS Appl Mater Interfaces ; 16(28): 36444-36452, 2024 Jul 17.

Article en En | MEDLINE | ID: mdl-38963298

ABSTRACT

ABSTRACT

Metal-organic frameworks (MOFs) are one of the most promising hydrogen-storing materials due to their rich specific surface area, adjustable topological and pore structures, and modified functional groups. In this work, we developed automatically parallel computational workflows for high-throughput screening of â¼11,600 MOFs from the CoRE database and discovered 69 top-performing MOF candidates with work capacity greater than 1.00 wt % at 298.5 K and a pressure swing between 100 and 0.1 bar, which is at least twice that of MOF-5. In particular, ZITRUP, OQFAJ01, WANHOL, and VATYIZ showed excellent hydrogen storage performance of 4.48, 3.16, 2.19, and 2.16 wt %. We specifically analyzed the relationship between pore-limiting diameter, largest cavity diameter, void fraction, open metal sites, metal elements or nonmetallic atomic elements, and deliverable capacity and found that not only geometrical and physical features of crystalline but also chemical properties of adsorbate sites determined the H2 storage capacity of MOFs at room temperature. It is highlighted that we first proposed the modified crystal graph convolutional neural networks by incorporating the obtained geometrical and physical features into the convolutional high-dimensional feature vectors of period crystal structures for predicting H2 storage performance, which can improve the prediction accuracy of the neural network from the former mean absolute error (MAE) of 0.064 wt % to the current MAE of 0.047 wt % and shorten the consuming time to about 10-4 times of high-throughput computational screening. This work opens a new avenue toward high-throughput screening of MOFs for H2 adsorption capacity, which can be extended for the screening and discovery of other functional materials.

Palabras clave

computational workflow; high-throughput screening; hydrogen storage; metalorganic frameworks; modified crystal graph convolutional neural networks

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: ACS Appl Mater Interfaces Asunto de la revista: BIOTECNOLOGIA / ENGENHARIA BIOMEDICA Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos

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