Neural network and experimental thermodynamics study of YCrO3-δ for efficient solar thermochemical hydrogen production.

ABO 3 -type perovskites have been demonstrated as promising redox materials for solar thermochemical H 2 production. In this work, a new energy material screening method based on the neural network system is designed as a feasible way to search for promising H 2 production materials. The predicted oxygen vacancy formation energy of the selected YCrO 3-δ is 4.199 eV, hinting excellent H 2 production potential. Thermogravimetric analysis shows that the doping of Zr into YCrO 3-δ improves oxygen formation capacity, leading to the maximum δ of 0.106. The molar enthalpy and entropy of YCr 0.75 Zr 0.25 O 3-δ have the positive relationship with δ , and the maximum values of which are 273.7 kJ mol−1 and 164.9 J mol−1 K−1 respectively. Based on the equilibrium thermodynamic principle, the peak H 2 yield is predicted to be 444.6 μmol g−1. Considering material kinetic limitation, gas-solid heat recovery and parameter sensitivity, the maximum H 2 production efficiency of YCr 0.75 Zr 0.25 O 3-δ is 17.3%. The combination of neural network and material thermodynamics provides a new pathway to design promising H 2 production materials, and the screened YCrO 3-δ presents excellent solar thermochemical H 2 production capacity. [Display omitted] [ABSTRACT FROM AUTHOR]