@prefix adms: <http://www.w3.org/ns/adms#> .
@prefix bfo: <http://purl.obolibrary.org/obo/BFO_> .
@prefix chebi: <http://purl.obolibrary.org/obo/CHEBI_> .
@prefix dcat: <http://www.w3.org/ns/dcat#> .
@prefix dcatap: <http://data.europa.eu/r5r/> .
@prefix dct: <http://purl.org/dc/terms/> .
@prefix foaf: <http://xmlns.com/foaf/0.1/> .
@prefix obi: <http://purl.obolibrary.org/obo/OBI_> .
@prefix prov: <http://www.w3.org/ns/prov#> .
@prefix schema1: <http://schema.org/> .
@prefix sio: <http://semanticscience.org/resource/SIO_> .
@prefix skos: <http://www.w3.org/2004/02/skos/core#> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .

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    dct:description "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." ;
    dct:identifier "https://doi.org/https://doi.org/10.18419/DARUS-5169" ;
    dct:issued "2025-07-29"^^xsd:date ;
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    dct:subject <https://doi.org/https://doi.org/10.18419/DARUS-5169#sample> ;
    dct:title "Data for: Machine learning potentials for hydrogen absorption in TiCr2 Laves phases" ;
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            skos:notation "https://doi.org/10.18419/DARUS-5169" ] ;
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