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Tutorials

Step-by-step tutorials for materials construction, DFT, ML, and simulation workflows on the Mat3ra platform. Each tutorial can also be located through the sidebar navigation.

Other documentation sites

For interface walkthroughs and platform actions, see the User Interface. For explanations of underlying concepts and software reference, see Concepts & Reference. For infrastructure and compute resources, see Resources / Infrastructure. For REST API documentation, see the Developers site. For CLI environment and batch jobs, see the Command-Line Interface site. To get started on the platform, see Accessing the Platform and Restart from Previous Job.

1. Materials Design

Designing and constructing material structures for simulations.

1.1. General

Topic Description Link
Import from files Upload CIF, POSCAR, XYZ and other formats via JupyterLite notebook Link
Combinatorial sets Generate all unique elemental substitutions in a host structure Link
Interpolated sets Create intermediate structures between two endpoints Link
Molecule on a surface Place a molecular adsorbate on a crystalline slab Link
Interface (3D Editor) Build a slab interface with quick visual setup Link
Interface (JupyterLite) Construct a minimal-strain interface via the ZSL algorithm Link
VESTA via Remote Desktop Visualize structures in VESTA through a remote desktop session Link

1.2. Reproducing Published Structures

Step-by-step recipes reproducing structures from the literature. The full overview page contains figures and additional context for each entry.

Structure Type Material Reference Link
Substitutional defect Graphene Fujimoto et al. (2011) Link
Substitutional defect (band structure) Graphene Fujimoto et al. (2011) Link
Vacancy-substitution pair GaN Miceli et al. (2016) Link
Vacancy defect h-BN Bertoldo et al. (2022) Link
Interstitial defect SnO Togo et al. (2006) Link
Island surface defect TiN Sangiovanni et al. (2018) Link
Step surface defect Pt(111) Šljivančanin et al. (2002) Link
Adatom surface defects Graphene Chan et al. (2008) Link
Twisted bilayer h-BN nanoribbons Xian et al. (2019) Link
Twisted bilayer MoS2 Liu et al. (2014) Link
2D–2D interface Graphene / h-BN Jung et al. (2015) Link
3D–3D interface Cu / SiO2 Shan et al. (2011) Link
2D–3D interface Graphene / SiO2 Kang et al. (2008) Link
Interface optimization Graphene / Ni(111) Dahal et al. (2014) Link
Adatom island Pt on MoS2 Saidi et al. (2015) Link
H-passivated nanowire Si Aradi et al. (2007) Link
H-passivated surface Si(100) Hansen et al. (1998) Link
Nanoclusters Au Larsen et al. (2011) Link
Slab SrTiO3 Eglitis et al. (2008) Link
High-k metal gate stack Si/SiO2/HfO2/TiN Muller et al. (1999) Link
Ripple perturbation Graphene Thompson-Flagg et al. (2009) Link
Grain boundary (3D) Cu (FCC) Frolov et al. (2013) Link
Grain boundary (2D) h-BN Li et al. (2015) Link

2. Simulations

2.1. Density Functional Theory

Density Functional Theory property calculations with Quantum ESPRESSO and VASP.

Category Property Method / Functional Software Link
Electronic Band structure DFT (standard) QE Link
Electronic Band structure HSE QE Link
Electronic Band structure HSE VASP Link
Electronic Band structure GW (Full Freq.) QE Link
Electronic Band structure GW (Plasmon Pole) QE Link
Electronic Band gap DFT (standard) QE Link
Electronic Band gap HSE QE Link
Electronic Band gap GW VASP Link
Electronic Density of states DFT QE Link
Electronic Density mesh DFT QE Link
Electronic Fermi surface DFT QE Link
Electronic Valence band offset DFT QE Link
Electronic Effective screening medium ESM QE Link
Electronic Hubbard U correction DFT+U QE Link
Electronic Magnetic properties Spin-polarized QE Link
Electronic Spin-orbit coupling SOC QE Link
Optical Dielectric constant DFT QE Link
Vibrational Zero point energy DFPT QE Link
Vibrational Phonon dispersion / DOS DFPT QE Link
Vibrational Phonons on a grid DFPT QE Link
Thermodynamic Surface energy DFT QE Link
Chemical Reaction energy profile NEB QE Link
Chemical Reaction energy profile NEB VASP Link
Workflow k-point convergence — QE Link
Workflow Structural relaxation — QE Link

2.2. Machine Learning

Machine Learning force fields and predictive models.

Topic Description Link
Train a NN potential End-to-end workflow: QE CP → DeePMD training → LAMMPS MD Link
Python MLFF (MatterSim) Run MatterSim force field on a GPU node via Python workflow Link

3. Other

3.1. Command-Line Jobs

Submitting and managing jobs through the CLI.

Topic Description Link
Create + run a CLI Job Submit a job from the command line and monitor output Link
Import a CLI Job Register a CLI-submitted job in the web interface Link
QE GPU Job Run Quantum ESPRESSO on GPU-accelerated compute nodes Link

3.2. Templating

Customizing simulation input files with the template engine.

Topic Description Link
Flags by elemental composition Set boolean flags based on the elements present Link
Magnetic moment by specie Assign initial magnetic moments per atomic species Link

3.3. Tools and Environments

Platform access, notebook environments, and software management.

Topic Description Link
Accessing the platform Set up an account and access the Mat3ra platform Link
Jupyter Notebook Launch and use a Jupyter notebook on the platform Link
Restart from previous job Resume a calculation from the output of a prior job Link
TensorFlow (GPU) Run TensorFlow workloads on GPU-enabled compute nodes Link
Add new software Install custom packages via the CLI environment Link

  1. Fujimoto et al., Phys. Rev. B 84, 245446 (2011). DOI ↩

  2. Miceli et al., Phys. Rev. B 93, 165207 (2016). DOI ↩

  3. Bertoldo et al., npj Comput. Mater. 8, 72 (2022). DOI ↩

  4. Togo et al., Phys. Rev. B 74, 195128 (2006). DOI ↩

  5. Sangiovanni et al., Phys. Rev. B 97, 035406 (2018). DOI ↩

  6. Šljivančanin et al., Surf. Sci. 515, 235 (2002). DOI ↩

  7. Chan et al., Phys. Rev. B 77, 235430 (2008). DOI ↩

  8. Xian et al., Nano Lett. 19, 4934 (2019). DOI ↩

  9. Liu et al., Nat. Commun. 5, 4966 (2014). DOI ↩

  10. Jung et al., Nat. Commun. 6, 6308 (2015). DOI ↩

  11. Shan et al., Phys. Rev. B 83, 115327 (2011). DOI ↩

  12. Kang et al., Phys. Rev. B 78, 115404 (2008). DOI ↩

  13. Dahal et al., Nanoscale 6, 2548 (2014). DOI ↩

  14. Saidi et al., Cryst. Growth Des. 15, 642 (2015). DOI ↩

  15. Aradi et al., Phys. Rev. B 76, 035305 (2007). DOI ↩

  16. Hansen et al., Phys. Rev. B 57, 13295 (1998). DOI ↩

  17. Larsen et al., Phys. Rev. B 84, 245429 (2011). DOI ↩

  18. Eglitis et al., Phys. Rev. B 77, 195408 (2008). DOI ↩

  19. Muller et al., Nature 399, 758 (1999). Reference ↩

  20. Thompson-Flagg et al., EPL 85, 46002 (2009). DOI ↩

  21. Frolov et al., Nat. Commun. 4, 1899 (2013). DOI ↩

  22. Li et al., Nano Lett. 15, 6004 (2015). DOI ↩