Materials Modelling
Our modelling capabilities span multiple scales - from electronic structure to continuum mechanics - providing predictive insights into material properties and guiding experimental design.
Contact person
What is Materials Modelling?
Materials modelling uses computational techniques to simulate and predict material behavior under various conditions. This approach accelerates discovery, reduces experimental costs, and enables design of advanced functional materials.
Capabilities
- First-Principles Methods:
- Full Configuration Interaction Quantum Monte Carlo (FCI-QMC)
- Density Matrix Embedding Theory (DMET)
- Density Functional Theory (DFT)
- Atomistic-Scale Techniques:
- Temperature-Dependent Effective Potential (TDEP)
- Machine-learning-based universal potentials (MLUP)
- Atomistic Spin Dynamics (ASD)
-
Continuum-Scale Methods:
- Finite Element Modelling (FEM)
- High-Throughput Screening:
- AI-driven selection of promising candidates from large databases
- Generative AI for novel material design
Properties We Study
- Temperature-dependent properties from phonon calculations.
- Electronic transport via Boltzmann transport equations.
- Thermodynamic and kinetic models linked to CALPHAD.
- Surface, interface, and precipitate features using in-house tools.
- Diffusivity, solubility, and permeability from molecular dynamics.
- Spectroscopic properties (NMR, Berry phase, inelastic neutron scattering).
- Elastic, optical, and magnetic properties.
Applications
- Benchmark studies of selected materials.
- Screening for new materials with improved performance.
- Correlation of nanoscale properties with macroscopic behavior.
- Design of energy materials, semiconductors, and functional coatings.
