Integrated Computational Materials Engineering​

Main research lines

  • Virtual material discovery for functional applications, through the use of DFT, cluster expansion and atomistic approaches combined with artificial intelligence.
  • Virtual processing: Integration of modelling tools (atomistic, computational thermodynamics and kinetics, phase field) to simulate the microstructural development of materials during processing.
  • Virtual testing of metallic alloys: Development, calibration and numerical implementation of microstructural-based constitutive models to predict the mechanical behaviour of single crystals and polycrystals.
  • Virtual testing of composites: Implementation of the constitutive models in finite element codes to simulate the mechanical behaviour of structural components.
  • Smart manufacturing: Multiphysics models of autoclave and out-of-autoclave curing of composite materials accounting for porosity evolution during the process. Simulation based smart manufacturing processes. Sensoring and process control.
  • These approaches are applied to several materials, in particular
    • Light (Al, Mg and Ti) metallic alloys and their composites. 
    • Ni-based superalloys. 
    • Multifunctional composite materials and structures. 
    • Materials for cathalisis.

  • First principles calculations.
  • Molecular mechanics and molecular dynamics.
  • Dislocation dynamics.
  • Object and lattice Kinetic Monte Carlo.
  • Computational thermodynamics and kinetics.
  • Phase field.
  • Finite Element solvers for multiphysical problems.
  • Fast Fourier based solvers for computational homogenization.
  • Bottom-up approaches (scale bridging). 
  • Development of modular multi-scale tools. 
  • High throughput screening integration. 
  • Concurrent models. 
  • Mean field homogenisation
  • Computational homogenization including FEM and Fast Fourier Transform –FFT–based solvers
  • Modelling and simulation of multiscale transport phenomena (application to advanced materials for batteries).
  • Multiscale modelling of dendritic growth (dendritic needle network approach).
  • Numerical methods for solids (finite elements and FFT approaches).
  • Computational mechanics and micromechanics.
  • Material informatics for analysis of large material datasets.
  • Modelling and simulation of H2 embrittlement in metallic tanks and pipes.
  • Study of hydrogen diffusion mechanisms in metals.
  • Discovery of new catalysts for H2 production and fuel cells.
  • Discovery of new catalysts for CO2 reduction reaction.
  • Virtual design and testing of mechanical metamaterials.
  • Simulation of AM process in metals including macroscopic simulation of the thermo-mechanical process by multiphysyical finite element models, microstructure evolution through phase-field and prediction of mechanical response using computational polycrystalline homogenization.
  • Discovery of porous materials for energy applications (CO2 capture, methane storage).
  • Design of ionic liquids.
  • Materials discovery: structures with high H2 working capacity and H2 adsorption-desorption performance.
  • Porous material design for capture and storage of CO2.
  • Design of Metal Organic Frameworks for separation of gases for anesthesia (Xe/Kr).