arxiv_ml 90% Match Research Paper Materials scientists,Computational chemists,ML researchers in scientific domains 19 hours ago

COFAP: A Universal Framework for COFs Adsorption Prediction through Designed Multi-Modal Extraction and Cross-Modal Synergy

graph-neural-networks › molecular-modeling

📄 Abstract

Abstract: Covalent organic frameworks (COFs) are promising adsorbents for gas adsorption and separation, while identifying the optimal structures among their vast design space requires efficient high-throughput screening. Conventional machine-learning predictors rely heavily on specific gas-related features. However, these features are time-consuming and limit scalability, leading to inefficiency and labor-intensive processes. Herein, a universal COFs adsorption prediction framework (COFAP) is proposed, which can extract multi-modal structural and chemical features through deep learning, and fuse these complementary features via cross-modal attention mechanism. Without Henry coefficients or adsorption heat, COFAP sets a new SOTA by outperforming previous approaches on hypoCOFs dataset. Based on COFAP, we also found that high-performing COFs for separation concentrate within a narrow range of pore size and surface area. A weight-adjustable prioritization scheme is also developed to enable flexible, application-specific ranking of candidate COFs for researchers. Superior efficiency and accuracy render COFAP directly deployable in crystalline porous materials.

Key Contributions

Introduces COFAP, a universal framework for COF adsorption prediction using designed multi-modal extraction and cross-modal synergy. It extracts and fuses structural and chemical features via deep learning and cross-modal attention, achieving SOTA performance without relying on gas-specific features, thus enabling efficient high-throughput screening.

Business Value

Accelerates the discovery and design of novel COFs for gas adsorption and separation applications, leading to more efficient industrial processes and new materials for environmental or energy solutions.

Paper Metadata

Innovation Type

Algorithmic Framework

Deployment Feasibility

Moderate. Requires expertise in materials science and ML. The framework itself is deployable as a predictive tool.

Limitations Addressed

Conventional ML predictors for COFs rely on time-consuming, gas-specific features, limiting scalability and efficiency. Vast design space of COFs requires efficient screening.

Performance Gains

Outperforms previous approaches on the hypoCOFs dataset by setting a new SOTA.

Technical Tags

Covalent Organic Frameworks (COFs)adsorption predictionmachine learningmulti-modal extractioncross-modal synergydeep learningfeature fusionstructure-property predictionhigh-throughput screening

Research Topics

Predictive Modeling for Materials ScienceMachine Learning for COF DesignMulti-modal Feature LearningHigh-Throughput Screening of Adsorbents

Methods & Architectures

Deep learningMulti-modal feature extractionCross-modal attention mechanismFeature fusionWeight-adjustable prioritization scheme Deep learning modelsModels with cross-modal attention

Applications & Tasks

Materials Science Chemistry Adsorption and Separation Computational Chemistry PredictionScreeningStructure-Property Relationship Predicting COF adsorption propertiesIdentifying optimal COF structuresAccelerating materials discovery

Datasets & Benchmarks

Datasets

hypoCOFs dataset

Benchmarks

Sets new SOTA performance on hypoCOFs dataset.

Prediction accuracyPerformance compared to previous approaches

Related Fields

Materials ScienceMachine LearningChemistryDeep LearningComputational Modeling

Keywords

COFsadsorptionpredictionmachine learningdeep learningmulti-modalfeature fusionmaterials sciencehigh-throughput screeningcross-modal attention

Academic Context

#Predictive Modeling for Materials Science#Machine Learning for COF Design#Multi-modal Feature Learning#High-Throughput Screening of Adsorbents

Commercial Potential

Potential Products

Materials discovery platformsCOF design softwareAdsorbent selection tools

Target Industries

ChemicalsEnergyEnvironmental TechnologyMaterials Manufacturing

Use Case Examples

Designing new COFs for carbon capture.Screening COFs for efficient gas separation in industrial processes.Predicting the performance of COFs for hydrogen storage.

Competitive Edge

Offers a universal framework that bypasses the need for specific gas-related features, enabling broader applicability and higher efficiency than conventional ML predictors for COFs.

Market Opportunity

Growing market for advanced materials, particularly for energy and environmental applications.

Revenue Models

Licensing of the COFAP frameworkmaterials design servicesconsulting.

Resource Requirements

Compute Needs

High, for training deep learning models on large feature sets.

Data Requirements

Requires datasets of COF structures and their adsorption properties.

Deployment Constraints

Requires integration with materials databases and simulation tools.

Scalability

The framework is designed for high-throughput screening, implying good scalability for prediction once trained.

Production Readiness

Maturity Level

Research

Time to Market

3-5 years for integration into materials design workflows.

Patent Potential

Moderate, for the COFAP framework and specific feature extraction/fusion techniques.

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