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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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Graph Neural Network-Accelerated Multitasking Genetic Algorithm for Optimizing PdTi1-H Surfaces under Various CO2

Changzhi Ai1, Shuang Han1, Xin Yang1

  • 1Department of Energy Conversion and Storage, Technical University of Denmark, Anker Engelunds Vej, 2800 Kongens Lyngby, Denmark.

ACS Applied Materials & Interfaces
|March 4, 2024
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Summary

We used a deep learning genetic algorithm to find new palladium-titanium hydride catalysts for CO2 reduction. Several Pd-Ti-H surfaces show high activity for CO2 reduction and syngas production.

Keywords:
CO2 reductionPdxTi1−xHydeep learningevolutionary multitaskinggenetic algorithmglobal optimizationgraph neural networksurface free energy

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Area of Science:

  • Materials Science
  • Catalysis
  • Computational Chemistry

Background:

  • Palladium (Pd) hydride catalysts show promise for CO2 reduction reaction (CO2RR) and hydrogen evolution reaction (HER).
  • Previous research indicated Ti-doped and Ti-alloyed Pd hydrides enhance CO2RR performance over pure Pd hydride.
  • Surface composition, ordering, and reaction conditions significantly influence catalyst stability, activity, and selectivity.

Purpose of the Study:

  • To screen for stable and active Pd-Ti-H surfaces with multiple adsorbates for CO2RR under diverse reaction conditions.
  • To overcome the complexity of multi-adsorbate systems and vast search spaces in theoretical catalyst screening.
  • To identify novel catalyst compositions for efficient CO2 conversion and syngas generation.

Main Methods:

  • Application of a deep learning-assisted multitasking genetic algorithm for catalyst screening.
  • Utilizing an ensemble deep learning model to accelerate structure relaxation and ensure accuracy of energy and force calculations.
  • Simultaneously identifying globally stable surface structures across various reaction conditions.

Main Results:

  • Screened 23 stable Pd-Ti-H surface structures under different reaction conditions.
  • Identified specific compositions (e.g., Pd0.56Ti0.44H1.06 + 25%CO, Pd0.31Ti0.69H1.25 + 50%CO) exhibiting high activity for CO2RR.
  • Determined that certain Pd-Ti-H structures are suitable for generating syngas (CO and H2).

Conclusions:

  • The deep learning-guided genetic algorithm effectively screens complex catalytic systems.
  • Optimized Pd-Ti-H compositions demonstrate significant potential for CO2RR and syngas production.
  • This approach provides a pathway for discovering advanced catalysts under realistic reaction conditions.