Nature of Metal-Support Interaction Discovered by Interpretable Machine Learning
crossref（2024）
University of Science and Technology of China | Shanghai University
Abstract
Metal catalysts supported on oxides play a paramount role in numerous industrial reactions. Modulating metal-support interaction is a key strategy to boost catalytic productivity and stability; however, the nature of metal-support interaction and quantification remain major unsolved problems. By leveraging interpretable machine learning, domain knowledge, and experimental data available, we discover a physical metal-support interaction equation applicable to metal nanoparticles and adatoms on oxides, and oxide films on metals. Though metal-oxygen interaction dominates metal-support interaction and determines the metal composition effect, metal-metal interaction delineates the support effect. This ensures a principle of strong metal-metal interaction for encapsulation of suboxide over metal nanoparticles, substantiated comprehensively by molecular dynamics simulations and previous experiments. The developed theory provides valuable insights and guidance in engineering the metal-support systems.
MoreTranslated text
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined