Three new RCN FRIPRO Grants to Members of the Hylleraas Centre
Three members of the Hylleraas Centre have received FRIPRO grants from the Research Council in Norway 2021
TThe grant winners from left to right: David Balcless, Abril Castro, and Luca Frediani
We are proud to announce that three members of the Hylleraas Centre, David Balcells, Abril Castro, and Luca Frediani, have won FRIPRO grants from the Research Council of Norway in 2021. These projects will allow the project winners to continue and expand their already successful research lines within the Hylleraas Centre. The Young Research Talent funding received by Abril Castro will allow her to establish her own independent research group. Congratulations to all!
David Balcells won a FRIPRO Ground-Breaking Research Grant for Machine Learning Molecular Legos for Catalyst Discovery within Large Chemical Spaces (catLEGOS). catLEGOS is four-year project with a budget of 12 MNOK, allowing the project leader to hire one PhD student two two postdocs.
Abril Castro won a FRIPRO Young Researcher Talents Grant for Accurate Prediction and Interpretation of NMR Spectra in Transition- Metal Complexes (SpecTram). The SpecTram project is for 3.5 years. Its budget of 8 MNOK will cover the salary of one PhD student in addition to that of the project leader.
Luca Frediani won a FRIPRO Ground-Breaking Research Grant for Relativistic Multiresolution Chemistry: Heavy-Element Compounds at the Complete Basis-set Limit (ReMRCheM). The project will run for five years, Two 3-year postdocs and one engineer (50% position) will be hired on ReMRCheM, on budget of 12 MNOK.
Abstract for catLEGOS: Machine learning is revolutionizing the fields of materials and drug discovery but remains largely underused in catalysis. We recently showed how Gaussian processes can be trained with small DFT datasets to predict the energy barriers of the fundamental reactions involved in homogeneous catalysis (Balcells et al., Chem. Sci., 2020, 11, 4584). The cat LEGOS project will take this approach to the next level by coupling the Gaussian processes to a recommender system based on deep neural networks. The resulting predictive models will enable catalyst discovery within large chemical spaces.
Abstract SpecTram: The accurate prediction of NMR (Nuclear Magnetic Resonance) properties in transition-metal complexes provides a useful, but challenging, strategy to help in the interpretation of the spectra. Magnetic shieldings have been said to be sensitive to everything and the enormous challenge when modelling NMR spectra of heavy-nuclei or paramagnetic molecules, arises from the extreme sensitivity to relativistic and environmental effects. SpecTraM will contribute to this challenge, combining state-of-the-art relativistic approaches with robust quantum-chemistry methodologies to reproduce accurate chemical shifts for heavy transition-metal complexes.
Abstract ReMRCheM: The ReMRChem project will transform the field of electronic structure calculations of heavy-element compounds by developing a novel approach based on multiwavelets (MWs), instead of AOs or PWs. MWs are a kind of wavelets: the functions for which Yves Meyer received the Abel Prize in 2017. Our ambitious goal will be achieved by implementing: (1) the full spectrum of relativistic Hamiltonians (1-, 2- and 4-components) for density-functional theory (DFT). (2) the open-ended response formalism for arbitrary-order properties using MWs. Combining these two developments together, within a MW framework, it will for the first time be possible to generate relativistic results with unprecedented precision at the DFT level, for energies and properties of any order. ReMRChem will make our MW code for quantum chemistry a unique tool for the simulation of heavy- element compounds: a significant step forward in the state of the art.
Published June 24, 2021 4:22 PM
- Last modified June 25, 2021 8:55 AM