报告人：Prof. Tejs Vegge
Technical University of Denmark, Department of Energy Conversion and Storage, Denmark
A transition from fossil to renewable energy sources is of vital importance in the global pursuit of reduced CO2 emissions. Most technologies for conversion and storage of energy from intermittent sources like wind and solar power are still not fully competitive on commercial terms, typically due to poor efficiency, stability or cost. With a global energy consumption on the scale of terawatts, sustainable solutions based extensively on scarce materials are not viable, and the ability to improve and ultimately design superior materials for energy conversion, e.g. electrolysis and fuel cells, and storage, e.g. batteries and synthetic fuels, is paramount for the development of more efficient sustainable energy technologies.
Nanoalloys have been found to display highly promising catalytic properties for a range of chemical and electrochemical reactions, e.g. Pt-based nanoalloys for the oxygen reduction reaction (ORR) in fuel cells and Au-Cuand Cu-Ni nanoalloys for electro-reduction of CO2 (CO2RR) into fuels and chemicals.
The activity, selectivity and stability of the catalysts depends specifically on the composition, structure and ordering of the nanoalloys, and the ability to predict and optimize these properties in silico holds great potential. Here, we combine interatomic potentials and density functional theory (DFT) level calculations with genetic algorithms (GA) and machine learning (ML) to accelerate the search for the compositions, which provide the most stable and catalytically active nanoalloys.
We will present a number of examples for bi- and tri-metallic nanoalloys and core-shell particles, e.g. displaying improved catalytic activity over pure platinum for ORR, and the identification of novel mixed core – mixed shell particles for CO2RR. A traditional GA allows the search to be performed for optimisation of a single variable, e.g. stability or activity, but we alsopresent anew ML-GA approach, which allows for a multi-objective optimisation to be performed, where two or more variables can be searched simultaneously. The MLGA yields a substantial reduction in the number of minimizations that need to be performed to find a range of structures of interest.
Finally, a new and computationally fast approach to identify and correct for systematic DFT-errors is presented. The approach, which utilizes Bayesian statistics on an ensemble of different exchange-correlation functionals, is demonstrated for electro-reduction of CO2. Here, the identification and correction for errors associated with C=O double bonds leads to the identification of the preferred reaction pathways and the design of suitable electro-catalysts for production of formic acid.
Prof. Tejs Vegge
2016- : August-Wilhelm Scheer Visiting Professor, Technical University of Münich (TUM)
2013- : Professor and Head of Section, DTU Energy, Technical University of Denmark (DTU)
2012 : Visiting Professor, SUNCAT@SLAC National Accelerator Laboratory, Stanford University
2012-2013: Associate Professor and Head of Section, DTU Energy Conversion, DTU
2004-2011: Senior Researcher and Group Leader, Materials Research Division, Ris? DTU
2001-2004:“Innovation Postdoc”at Danfoss Corporate Ventures A/S and DTU Physics
1993-2001: MSc (06-07-1998) and PhD (03-12-2001) in Applied Physics from DTU
Primary Research Areas
Integrated computational and experimental prediction and characterization of energy materials
Ab initio computational materials design from model structures, predictors and genetic algorithms
Database and multiscale techniques for materials design
Molecular, ionic, defect and electronic transport in energy materials
Electrode and electrolyte materials for batteries
Nanoparticle electrocatalysts for sustainable production of synthetic fuels
Materials for electrochemical production and solid state storage of ammonia
Materials for hydrogen storage, membranes and electrolytes