ArticleNature communications2023
Direct prediction of gas adsorption via spatial atom interaction learning.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
12 citing papers in PubMed, 46 citations in OpenAlex.
- PoroNet: An Intrinsically Interpretable Pore Graph Neural Network for Resolving Pore-Level Adsorption in Metal-Organic Frameworks.Journal of chemical theory and computation · 2026Article
- Harnessing confinement effect and interpretable machine learning to predict alkane diffusion in zeolite catalysts.Nature communications · 2026Article
- RetNeXt: A Pretrained Model for Transfer Learning Across the MOF Adsorption Space.Journal of chemical information and modeling · 2026Article
- A data-efficient foundation model for porous materials based on expert-guided supervised learning.Nature communications · 2026Article
- Artificial Intelligence Empowered New Materials: Discovery, Synthesis, Prediction to Validation.Nano-micro letters · 2026Review
- A multi-objective optimization-driven screening approach for maximizing hydrogen storage capacities in MOFs.Scientific reports · 2026Article
- A universal foundation model for transfer learning in molecular crystals.Chemical science · 2025Article
- Artificial Intelligence Paradigms for Next-Generation Metal-Organic Framework Research.Journal of the American Chemical Society · 2025Review
- Investigating the Physical Adsorption of DCPD/Furfural and HMolecules (Basel, Switzerland) · 2025Article
- Gas adsorption meets geometric deep learning: points, set and match.Scientific reports · 2024Article
- From Data to Discovery: Recent Trends of Machine Learning in Metal-Organic Frameworks.JACS Au · 2024Review
- Direct prediction of gas adsorption via spatial atom interaction learning.Nature communications · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 4 institutions in 3 countries.
Funding
Abstract
Physisorption relying on crystalline porous materials offers prospective avenues for sustainable separation processes, greenhouse gas capture, and energy storage. However, the lack of end-to-end deep learning model for adsorption prediction confines the rapid and precise screen of crystalline porous materials. Here, we present DeepSorption, a spatial atom interaction learning network that realizes accurate, fast, and direct structure-adsorption prediction with only information of atomic coordinate and chemical element types. The breakthrough in prediction is attributed to the awareness of global structure and local spatial atom interactions endowed by the developed Matformer, which provides the intuitive visualization of atomic-level thinking and executing trajectory in crystalline porous materials prediction. Complete adsorption curves prediction could be performed using DeepSorption with a higher accuracy than Grand canonical Monte Carlo simulation and other machine learning models, a 20-35% decline in the mean absolute error compared to graph neural network CGCNN and machine learning models based on descriptors. Since the established direct associations between raw structure and target functions are based on the understanding of the fundamental chemistry of interatomic interactions, the deep learning network is rationally universal in predicting the different physicochemical properties of various crystalline materials.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.