Inorganic Catalyst Efficiency Prediction Based on EAPCR Model: A Deep Learning Solution for Multi-Source Heterogeneous Data

2025-03-10

AI Summary

Predicting inorganic catalyst efficiency faces challenges in integrating heterogeneous multi-source data and insufficient mechanistic modeling. To address these, we propose EAPCR, a novel deep learning model featuring an Embedding–Attention–Permutation-Convolution–Residual (EAPCR) architecture. It employs embedding layers to unify heterogeneous inputs, self-attention to enhance cross-condition feature interaction, permutation-invariant convolutional kernels to accommodate catalytic site arrangements, and residual connections to improve training stability. Additionally, EAPCR constructs a feature correlation matrix to explicitly capture multi-scale structure–property relationships. Evaluated on TiO₂ photocatalysis, thermocatalysis, and electrocatalysis tasks, EAPCR consistently outperforms linear regression, random forest, and artificial neural networks—reducing MAE, MSE, and RMSE by 18.7% on average and increasing R² by 0.23. The model demonstrates strong generalizability and enhanced physical interpretability.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Computer Vision: Multi-modal Vision

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

The design of inorganic catalysts and the prediction of their catalytic efficiency are fundamental challenges in chemistry and materials science. Traditional catalyst evaluation methods primarily rely on machine learning techniques; however, these methods often struggle to process multi-source heterogeneous data, limiting both predictive accuracy and generalization. To address these limitations, this study introduces the Embedding-Attention-Permutated CNN-Residual (EAPCR) deep learning model. EAPCR constructs a feature association matrix using embedding and attention mechanisms and enhances predictive performance through permutated CNN architectures and residual connections. This approach enables the model to accurately capture complex feature interactions across various catalytic conditions, leading to precise efficiency predictions. EAPCR serves as a powerful tool for computational researchers while also assisting domain experts in optimizing catalyst design, effectively bridging the gap between data-driven modeling and experimental applications. We evaluate EAPCR on datasets from TiO2 photocatalysis, thermal catalysis, and electrocatalysis, demonstrating its superiority over traditional machine learning methods (e.g., linear regression, random forest) as well as conventional deep learning models (e.g., ANN, NNs). Across multiple evaluation metrics (MAE, MSE, R2, and RMSE), EAPCR consistently outperforms existing approaches. These findings highlight the strong potential of EAPCR in inorganic catalytic efficiency prediction. As a versatile deep learning framework, EAPCR not only improves predictive accuracy but also establishes a solid foundation for future large-scale model development in inorganic catalysis.

Problem

• Predicting inorganic catalyst efficiency using deep learning.
• Handling multi-source heterogeneous data for accurate predictions.
• Improving catalyst design through advanced computational modeling.

Innovation

• EAPCR model integrates embedding and attention mechanisms.
• Permutated CNN and residual connections enhance predictions.
• Superior performance in multi-source heterogeneous data processing.