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Infusing theory into deep learning for interpretable reactivity prediction
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DOI：10.1038/s41467-021-25639-8.png)
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摘要
En 中文
Despite recent advances of data acquisition and algorithms development, machine learning (ML) faces tremendous challenges to being adopted in practical catalyst design, largely due to its limited generalizability and poor explainability. Herein, we develop a theory-infused neural network (TinNet) approach that integrates deep learning algorithms with the well-established d-band theory of chemisorption for reactivity prediction of transition-metal surfaces. With simple adsorbates (e.g., *OH, *O, and *N) at active site ensembles as representative descriptor species, we demonstrate that the TinNet is on par with purely data-driven ML methods in prediction performance while being inherently interpretable. Incorporation of scientific knowledge of physical interactions into learning from data sheds further light on the nature of chemical bonding and opens up new avenues for ML discovery of novel motifs with desired catalytic properties. Machine learning faces challenges in catalyst design due to its black-box nature. Here, the authors develop a theory-infused neural network approach that integrates deep learning algorithms with the well-established d-band theory of chemisorption for reactivity prediction of transition-metal surfaces.
Keyword:
DENSITY-FUNCTIONAL THEORY
OXYGEN REDUCTION
AMMONIA OXIDATION
CHEMISORPTION
CO2
ELECTROCATALYSTS
EVOLUTION
CATALYSTS
DESIGN
PERFORMANCE
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期刊
IF:
15.7
论文数:
9.4W
被引数:
91.2W
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引用论文
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OAAI分享收藏Effects of d-band shape on the surface reactivity of transition-metal alloysd带形状对过渡金属合金表面反应性的影响.png)
PHYSICAL REVIEW B
PHYSICAL REVIEW BIF3.7
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