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Machine Learning Models Predict Calculation Outcomes with the Transferability Necessary for Computational Catalysis.

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Area of Science:

  • Catalysis
  • Computational Chemistry
  • Materials Science

Background:

  • Virtual high-throughput screening (VHTS) and machine learning (ML) accelerate catalyst design.
  • VHTS faces challenges with high failure rates and wasted computational resources due to complex reactive intermediates.
  • Converging calculations for all intermediates to desired geometries and electronic states is difficult.

Purpose of the Study:

  • To develop a dynamic classifier for real-time identification of geometry optimization failures in catalyst design.
  • To improve the efficiency and reduce computational costs associated with VHTS of catalysts.
  • To demonstrate the transferability and accuracy of the dynamic classifier across different intermediates and metal centers.

Main Methods:

  • Implemented a dynamic classifier using a convolutional neural network (CNN) to monitor geometry optimizations.
  • Trained the CNN on electronic structure and geometric information from density functional theory (DFT) calculations.
  • Utilized uncertainty quantification alongside the dynamic classifier to assess model confidence.

Main Results:

  • The dynamic classifier accurately identified geometry optimization failures for all reactive intermediates in a methane-to-methanol conversion cycle.
  • The model demonstrated excellent transferability, generalizing to chemically distinct intermediates and metal centers not present in the training data.
  • The dynamic classifier, combined with uncertainty quantification, saved over 50% of computational resources by eliminating unsuccessful calculations.

Conclusions:

  • Dynamic classifiers offer a robust and transferable solution for identifying failed calculations in catalyst VHTS.
  • This approach significantly enhances computational efficiency in the design of single-site transition-metal catalysts.
  • The method shows promise for accelerating the discovery of new catalysts by reducing resource expenditure.