This work is part of the project CNS2023-144785, funded by MICIU/AEI/10.13039/501100011033 and the European Union “NextGenerationEU”/PRTR. Moreover, this work was funded by the Xunta de Galicia (GRC ED431C 2024/06). This work was done within the framework of project HI$\_$MOV – “Corredor Tecnológico Transfronterizo de Movilidad con Hidrógeno Renovable”, with reference 0160$\_$HI$\_$MOV$\_$1$\_$E, co-financed by the European Regional Development fund (ERDF), in the scope of Interreg VI A Spain – Portugal Cooperation Program (POCTEP) 2021-2027. M. O. L. thanks the Xunta de Galicia for his ``Axudas de apoio á etapa predoutoral" grant (ED481A 2022/236). H. M. C. and R. L. C. acknowledge their contracts under the framework of the project CNS2023-144785, funded by MICIU/AEI /10.13039/501100011033 and the European Union “NextGenerationEU”/PRTR. A. M. R. thanks the Ministry of Science, Innovation and Universities for his FPU23/02994 grant. This publication and the contract of T. M. M. are part of the grant RYC2022-036679-I, funded by MICIU/AEI/10.13039/501100011033 and FSE+. 

Training data, input files and validation metrics for the article "Neural Network-based Simulations of Solid Electrolyte Interfaces"