This dataset contains all input files and selected key output files associated with the first-principles density functional theory (DFT) calculations conducted for the manuscript entitled "Iron–Water Interface at Different Electrochemical Conditions", authored by Adenilson Felipe Sousa-Silva, Dídac-Armand Fenoll, Mariona Sodupe, Luis Rodríguez-Santiago, and Xavier Solans-Monfort. At the time of dataset creation, the article is under peer review. The DFT calculations were performed using the Vienna Ab initio Simulation Package (VASP) [https://www.vasp.at/]. All files are provided in plain text format and can be opened with any standard text editor. The simulations and data generation were carried out on a Linux-based operating system.

METHODOLOGICAL INFORMATION

All calculations were performed with the VASP code at DFT level using the GGA PBE28 exchange-correlation functional. Dispersion corrections were introduced through Grimme’s D3 parametrization. Magnetization is known to be present in all the explored systems, for which spin-polarization was enforced by applying µ = 2.3 µB on all iron centers. Atomic cores were represented with projector augmented wave (PAW) pseudopotentials. Geometry optimizations were performed imposing a kinetic energy cut-off of 600 eV and a 6x6x1 Monkhorst-Pack k-point grid for the supercells, except for the case of the most computationally demanding (111) surface. For this surface, the kinetic energy cut-off was set to 400 eV and the Monkhorst-Pack k-point grid to 4x4x1. Benchmark calculations demonstrated that this selection converged surface energies to 1 meV/Å2 with respect to the k-point grids, and the adsorption energy of one water molecule to 30 meV with respect to the energy cutoff. The ionic relaxation threshold was set to 0.01 eV/Å, ensuring that the norms of all forces are converged within this threshold. Moreover, the effect of the rest of the electrolyte was taken into account using the implicit water solvation model implemented in VASPsol through single-point calculations. Atomic charges were calculated through Bader charge analysis

1. Description of methods used for collection-generation of data: Kresse, G.; Hafner, J. Ab Initio Molecular Dynamics for Liquid Metals. Phys Rev B 1993, 47, 558–561. Kresse, G.; Furthmüller, J. Efficient Iterative Schemes for Ab Initio Total-Energy Calculations Using a Plane-Wave Basis Set. Phys Rev B 1996, 54, 11169–11186. Larsen, A. H.; Mortensen, J. J.; Blomqvist, J.; Castelli, I. E.; Christensen, R.; Dułak, M.; Friis, J.; Groves, M. N.; Hammer, B.; Hargus, C.; Hermes, E. D.; Jennings, P. C.; Jensen, P. B.; Kermode, J.; Kitchin, J. R.; Kolsbjerg, E. L.; Kubal, J.; Kaasbjerg, K.; Lysgaard, S.; et al. The Atomic Simulation Environment—a Python Library for Working with Atoms. Journal of Physics: Condensed Matter 2017, 29, 273002. Perdew, J. P.; Burke, K.; Ernzerhof, M. Generalized Gradient Approximation Made Simple. Phys Rev Lett 1996, 77, 3865–3868. Grimme, S.; Antony, J.; Ehrlich, S.; Krieg, H. A Consistent and Accurate Ab Initio Parametrization of Density Functional Dispersion Correction (DFT-D) for the 94 Elements H-Pu. J Chem Phys 2010, 132. Blöchl, P. E. Projector Augmented-Wave Method. Phys Rev B 1994, 50, 17953. Kresse, G.; Joubert, D. From Ultrasoft Pseudopotentials to the Projector Augmented-Wave Method. Phys Rev B 1999, 59, 1758–1775. Mathew, K.; Sundararaman, R.; Letchworth-Weaver, K.; Arias, T. A.; Hennig, R. G. Implicit Solvation Model for Density-Functional Study of Nanocrystal Surfaces and Reaction Pathways. J Chem Phys 2014, 140, 084106. Henkelman, G.; Arnaldsson, A.; Jónsson, H. A Fast and Robust Algorithm for Bader Decomposition of Charge Density. Comput Mater Sci 2006, 36, 354–360. Bader, R. F. W. A Quantum Theory of Molecular Structure and Its Applications. Chem Rev 1991, 91, 893–928.

2. Methods for processing the data: All the output files are can be read with text file editors. Some of them (CONTAR) can be visualized using VESTA or ase visualization tools.