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Towards operando computational modeling in heterogeneous catalysis.

Lukáš Grajciar1, Christopher J Heard, Anton A Bondarenko

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Computational catalysis is advancing beyond 0 K/UHV models to operando conditions. New methods like machine learning and microkinetics offer more realistic simulations for heterogeneous catalysis.

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Area of Science:

  • Heterogeneous catalysis
  • Computational chemistry
  • Materials science

Background:

  • Experimental catalysis has shifted towards operando conditions.
  • Computational models are evolving from 0 K/UHV to more realistic simulations.
  • Advancements in computing power and software facilitate these transitions.

Purpose of the Study:

  • To review new computational methods bridging the gap between 0 K/UHV and operando conditions.
  • To highlight the importance of realistic simulation conditions in catalysis.
  • To connect and compare various advanced computational techniques.

Main Methods:

  • Global optimization techniques
  • Ab initio constrained thermodynamics
  • Biased molecular dynamics
  • Microkinetic modeling of reaction networks
  • Machine learning approaches

Main Results:

  • New methods enable more accurate modeling of catalytic processes under operando conditions.
  • Simulations accounting for chemical environment, pressure, and temperature reveal changes in catalytic site behavior and mechanisms.
  • Synergy between experimental and theoretical catalysis is increasing.

Conclusions:

  • The transition to operando computational models is crucial for understanding heterogeneous catalysis.
  • Advanced methods provide a more accurate molecular-level picture of catalytic reactions.
  • Combining these methods offers powerful insights into complex catalytic systems.