Javier Carrasco

Javier Carrasco
CIC Energigune · Electrochemical Energy Storage



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Dr. Javier Carrasco obtained his PhD in 2006 from the Universitat de Barcelona. His Ph.D thesis was devoted to the theoretical description of point defects in metal oxides using ab initio quantum chemistry methods. In 2007 he joined the Theory Department of the Fritz Haber Institute of the Max Planck Society, Berlin, as an Alexander von Humboldt fellow, working in the area of water-metal interfaces using density functional theory. In 2009 he moved to University College London, London, as a Newton International fellow. During this time he focused his research on the molecular-level understanding of ice formation on metal surfaces. Following this, in 2011 he moved to Instituto de Catálisis y Petroleoquímica del Consejo Superior de Investigaciones Científicas, Madrid, as a Ramón y Cajal fellow. Much of his work during this time was centred upon theoretical catalysis for hydrogen production and hydrogenation of hydrocarbons, as well as the application of van de Waals density functionals to molecular adsorption on metal and oxide surfaces. Since September 2013 he leads the Computational Studies group at the CIC Energigune.


Projects (4)
Development and exploitation of hierarchical macro-nanoporous metals for a broad range of applications such as shape-stabilized phase change materials, boiling, batteries, superhydrophobic surfaces, and more.
The overarching goal of ION-SELF is to transform the battery development cycle to enable accelerated, autonomous discovery of new electroactive materials, ultimately allowing for energy and power densities reaching the theoretical limits. To this end our general objectives are: (i) To achieve efficient utilization of AI algorithms for experimental planning; (ii)To control automated materials synthesis through in line receipt optimization. ION-SELF will be built in a systematic and modular way to validate models and workflows. Models will be based on first-principles atomistic calculations, machine learning algorithms, and artificial intelligence techniques, specifically developed to assist decision-making, automated experiments. Crucially, the ION-SELF project will integrate these models and experiments through a consistent close-loop transfer of inputs and outputs. In particular, initial materials compositions and synthesis conditions will be set up based on plausible parameters from known systems, and the results of the models and executed experiments will then be used to navigate the chemical spaces efficiently, transferring key findings back and forth in successive loops between atomistic modelling and experimental results.
electrolyte&hydrogel&ATRP&Znbattery&Mn electrode