Screening Cu-Zeolites for Methane Activation using Curriculum-based Training

09 October 2023, Version 3
This content is a preprint and has not undergone peer review at the time of posting.


Machine learning (ML), when used synergistically with atomistic simulations, has recently emerged as a powerful tool for accelerated catalyst discovery. However, the application of these techniques has been limited by the lack of interpretable and transferable ML models. In this work, we propose a new philosophy called curriculum-based training (CBT) to systematically develop reactive machine learning potentials (rMLPs) for high-throughput screening zeolite catalysts. Our CBT approach combines several different types of calculations to gradually teach the ML model about the relevant regions of the reactive potential energy surface. The resulting rMLPs are accurate, transferrable, and interpretable. We further demonstrate the effectiveness of this approach by exhaustively screening thousands of [CuOCu]2+ sites across hundreds of Cu-zeolites for the industrially relevant methane activation reaction. Specifically, this large-scale analysis of the entire International Zeolite Association (IZA) database identifies a set of previously unexplored zeolites (i.e., MEI, ATN, EWO, and CAS) that show the highest ensemble-averaged rates for [CuOCu]2+-catalyzed methane activation. We believe that this CBT philosophy can be generally applied to other zeolite-catalyzed reactions and subsequently, to other types of heterogeneous catalysts. Thus, this represents an important step towards overcoming the long- standing barriers within the computational heterogeneous catalysis community.


methane activation
Cu-exchanged zeolites
machine learning potential

Supplementary materials

Supporting Information
Section 1. MAZE Structure Generation Workflow Section 2. rCuZEO23 Database Introduction Section 3. DFT Computational Details Section 4. rMLP Model Training Details Section 5. Relevant Energy Distribution and TOF Evaluations Section 6. Model Transferability Evaluation

Supplementary weblinks


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