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Page 20 of 33 Liu et al. J Mater Inf 2024;4:33 https://dx.doi.org/10.20517/jmi.2024.48

Figure 9. Multivariable multimetric optimization of self-assembled photocatalytic CO reduction performance using machine learning
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algorithms. (A) Overview of the workflow; (B) Holistic improvement measured by objective function alongside improvement of both
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Yield and TOF ; (C) Ascending Yield and corresponding TOF . Copyright 2024, American Chemical Society. TON: Turnovers
CO CO CO CO
for CO formation; TOF: turnover frequency; QY: quantum yield.
photocatalytic CO reduction over metal-organic frameworks . Gated recurrent unit (GRU) neural
[140]
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networks have forecasted hydrogen production in continuous water splitting systems . Moreover, GBR
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methods have modeled the degradation of persistent pollutants such as perfluorooctanoic acid (PFOA) .
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Through the integration of experimental data and theoretical models, deep learning has significantly
enhanced the ability to predict and optimize key photocatalytic properties, such as light absorption,
photocatalytic efficiency, and degradation performance, driving improvements across diverse applications
in photocatalyst design.

Novel deep learning approaches of photocatalyst design
Beyond the common deep learning models, novel approaches continue to emerge, offering enhanced
capabilities for photocatalyst design. These advanced methods improve adaptability and the ability to
handle complex relationships in large datasets, driving significant progress in the field. Figure 10 reveals a
hybrid method combining computational fluid dynamics (CFD), ANNs, and genetic algorithms (GA) for
the analysis and optimization of micro-photocatalytic reactors aimed at NOx abatement . CFD
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simulations were used to investigate the effects of key variables, while ANN models were developed to
predict NO conversion with high accuracy (R = 0.9997). GA was then employed to optimize operating
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conditions, achieving full NO conversion (100%) under specific parameters, with residence time identified
as the most influential factor. A multi-objective optimization further applied GA to maximize NO
consumption while minimizing pressure drop, offering valuable insights into balancing performance
metrics.