Computational modelling of metallic systems
Computational modelling of metallic systems is a computational and workflow service for modelling the atomic, electronic, magnetic and mechanical behaviour of metals, alloys, intermetallic compounds, thin films, nanostructures and functional metallic materials. The service supports research tasks where experimental characterization needs to be complemented by first-principles calculations, atomistic modelling, parameter studies, reproducible workflows and computational interpretation of results. The service is scientifically supported by the IMP Computational Physics Laboratory and may use shared digital resources hosted through the Cloud Infrastructure Access Portal of ITP, including Quantum ESPRESSO, AiiDA and Jupyter-based environments where available. In this model, IMP provides domain-specific modelling expertise for metallic systems, while ITP provides the technical cloud and workflow execution environment. The service can support electronic structure analysis, density of states and band-structure calculations, total-energy and stability assessment, magnetic-property modelling, defect and interface modelling, molecular dynamics simulations, comparison with experimental measurements, and preparation of reusable computational datasets.
What the user gets
The user receives a computational modelling package agreed before the work starts. Depending on the research task, the package may include prepared structural models, input files, calculation setup, executed simulations, processed results, visualizations, plots, scripts, calculation logs and a short technical report.
Typical outputs may include relaxed atomic structures, total energies, forces, stress tensors, convergence-test results, molecular dynamics trajectories, defect or interface models, parameter-sweep tables, electronic band structures, total and partial density of states, magnetic moments, calculated spectra where applicable, visualization files and post-processing scripts.
For workflow-based tasks, the user may also receive workflow descriptions, Jupyter notebooks, AiiDA provenance records, reusable input and output folders, README documentation and metadata needed for verification, reuse or repository deposition. The final result can be prepared as a reusable computational data package for publication support, project reporting or further analysis.
Service category: Computational and Workflow Service
Hosting partner: Kurdyumov Institute for Metal Physics
Related node / facility: IMP Computational Physics Laboratory
Resources used
Access modes
- Remote access
- Virtual access
- Data access
Typical data outputs
- Simulation input files
- Simulation output files
- Calculation logs
FAIR requirements
Each computational modelling task should be accompanied by sufficient metadata to make the calculation understandable, reproducible and reusable. The user should provide the material system, composition, crystal or structural model, target properties, available experimental data, expected accuracy, relevant temperature or field conditions, and any constraints on publication or reuse.
The service provider should document the computational method, software name and version, input parameters, pseudopotentials or potential files, exchange-correlation functional where applicable, convergence criteria, boundary conditions, supercell or k-point settings, calculation date, responsible team, file formats and processing steps.
Recommended metadata elements include material name or formula, structure file, sample or phase description, modelling method, software stack, workflow version, input parameters, calculation conditions, data type, units, processing scripts, access status and licence or reuse conditions. Where possible, data should be exported in reusable formats such as TXT, CSV, JSON, CIF, XYZ, POSCAR-like structure files, trajectory files, PNG, SVG or PDF plots.
For publication-oriented datasets, the output package should include clear file names, README documentation, method description, software and parameter information, units, provenance information and enough metadata for deposition in DataverseUA, Zenodo or another repository.
User obligations
The user should provide a clear description of the research task, material system and expected modelling result before the work starts. The user should specify whether the main interest is electronic structure, magnetic properties, stability, defects, interfaces, mechanical response, molecular dynamics, comparison with experiment or another calculated property.
The user should provide available structural information, composition, phase data, crystallographic files if available, sample preparation or processing history where relevant, experimental reference data, preferred assumptions and known physical constraints. If the task involves comparison with experiment, the user should provide the corresponding experimental conditions, measurement results and uncertainty where available.
The user and the facility team should agree in advance on the modelling scope, software environment, computational resources, expected accuracy, workflow design, output formats, deadlines, confidentiality conditions, authorship or acknowledgement rules and publication plans.
The user should not treat computational results as certified experimental measurements. Scientific interpretation, validation against experimental data and final conclusions should be agreed within the research team or remain the responsibility of the user, depending on the collaboration model.