Data for: Machine learning potentials for hydrogen absorption in TiCr2 Laves phases

This dataset supports the development and validation of machine learning interatomic potentials (MLIPs) for modeling hydrogen absorption in C14 (hexagonal) and C15 (cubic) TiCr₂-based Laves phases. It includes density functional theory (DFT) calculations, fitted moment tensor potentials (Level 16), and basin-hopping Monte Carlo (BHMC)-sampled structures across a range of hydrogen concentrations and configurations. The data is organized into several directories: database/ contains DFT-calculated energies, forces, and stresses for training (Training_db/) and independent test configurations (Test_db/); dilute_H_configurations/ provides low-concentration hydrogen structures in both C14 and C15 phases, featuring face-connected (1D) and edge-connected (2D) interstitial arrangements computed with DFT; MC_structures/ includes minimum-energy hydrogenated structures from BHMC simulations using the trained MLIPs, along with relaxed DFT configurations, 108-atom C14 structures excluded from training, and the structures from Makarova; and trained_MTPs/ contains the final fitted MLIPs for both C14 (C14.mtp) and C15 (C15.mtp) phases. The dataset captures key aspects of hydrogen behavior in Laves phases, such as interstitial site occupation, hydrogen-hydrogen interactions, and the predictive performance of the force fields, and is fully referenced in the accompanying manuscript and supplementary materials.