Brief description of the Centre/Research Group

The research group CIMACO focuses its efforts on the multiscale computational modelling of advanced engineering materials. High throughput materials by design strategies in combination with external experimental validation are undertaken daily in our laboratory. Machine learning, computational screening and data mining techniques, all of them encompassed inside the artificial intelligence framework, are all hot topics in our current investigations, within a multidisciplinary and scientifically challenging atmosphere.

The group is composed of three senior researchers, an associated senior researcher, a doctoral student and an undergraduate student. It is currently involved in two running COST Actions, a National Research Project, and a national networking proposal. The group currently runs three in-house computing clusters and is a frequent awardee of Spanish- and occasionally European-based facilities supercomputing projects.

Project description

Integrated Computational Materials Engineering (ICME) approaches to Multiscale Materials Modelling (M3) are necessarily dependant on systematic and accurate first-principles based simulations. The time and length scales covered by these computationally intensive techniques should be particularly well represented in all consortia built up to tackle cutting edge research dealing with the excitement of the prediction and discover of advanced new materials. The CIMACO research group has reached scientific maturity within this particular field, driven firstly by the mastering of tools and codes archetypical of Density Functional Theory (DFT). As a side benefit, the group has garnered a renowned expertise in database feeding of relevant input to the construction and validation of interatomic potentials, via classic or heterodox approaches, to be used in Molecular Dynamics (MD) simulations. Additionally, computational results on diffusivity prefactors and activation energies are routinarily obtained by our team, and they represent essential input to parametrise advanced kinetic Monte Carlo simulations involving machine learning procedures.

Three main present and future working ideas making use of the above techniques and tools may be highlighted, depending on their domain of application: nuclear materials degradation and improved performance, critical raw materials uncompromising reduction and eventual substitution, and multiscale and multidisciplinary approach to ionising radiation modelling in structural, optical and biomaterials.

Research Area/s: Chemistry (CHE) / Information Science and Engineering (ENG) / Life Sciences (LIF) and Physics (PHY)  

Applications: documents to be submitted: Brief CV (Resumé), not exceeding one page, names of three potential referees, and a statement of research interests