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Designing catalysts via evolutionary-based optimization techniques
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DOI：10.1016/j.commatsci.2022.111833.png)
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Abstract
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Methodologies to design efficient, affordable, and sustainable catalysts have advanced rapidly in recent years. With advances in computational power and the rapid development of computational methods, the scientific community is turning to material simulations to investigate the hidden potential of a plethora of possibly un-discovered materials in incredibly short timeframes, usually impossible via trial-and-error experimental ap-proaches. This review article provides an overview of evolutionary-based optimization techniques with a special focus on Genetic Algorithms (GA's) and their potential use in the catalyst design process. The 'descriptors' required to design catalysts via evolutionary-based optimization techniques are discussed explicitly for five key chemical reactions, namely, the Oxygen Evolution Reaction (OER), the Oxygen Reduction Reaction (ORR), the Hydrogen Evolution Reaction (HER), the Nitrogen Reduction Reaction (NRR), and the CO2 Reduction Reaction (CO2RR). The descriptors and their appraisal discussed in this review will facilitate researchers using evolutionary-based optimization techniques for catalyst design and discovery.
Keywords:
Genetic algorithm
Machine learning
Oxygen evolution reaction
Oxygen reduction reaction
Hydrogen evolution reaction
Nitrogen reduction reaction
Carbon dioxide reduction reaction
Descriptors
Catalysis
Optimization techniques
Evolutionary algorithm
Journal
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3.3
Papers:
1.3W
Citations:
3.6W
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A Perovskite Oxide Optimized for Oxygen Evolution Catalysis from Molecular Orbital Principles.png)
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SCIENCEIF45.8
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