We are a young group at Department of Chemistry, University of Copenhagen, studying the relations between atomic structure, properties and the synthesis of new nanomaterials. Our aim is to obtain an atomistic understanding of new advanced materials, taking us to the point where we can make ‘materials by design’ – materials tailored to give specific properties for applications in e.g. catalysis, solar cells and other energy technologies. On this page, you can read more about our research, get to know us, find our latest publications and see what’s new in the group. If you want to know more, please get in touch!
NanostructureUCPH
We are a group at University of Copenhagen, studying the relations between atomic structure, properties and the synthesis of new nanomaterials.
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Repositories
Showing 10 of 11 repositories
- autoXAS Public Forked from UlrikFriisJensen/autoXAS
Automated analysis of X-ray Absorption Spectroscopy (XAS) data
- CHILI Public Forked from UlrikFriisJensen/CHILI
Chemically-Informed Large-scale Inorganic Nanomaterials Dataset for Advancing Graph Machine Learning
- deCIFer Public Forked from FrederikLizakJohansen/deCIFer
deCIFer is a transformer-based model for crystal structure prediction, generating Crystallographic Information Files (CIFs) directly from powder X-ray diffraction (PXRD) data.
- DebyeCalculator Public Forked from FrederikLizakJohansen/DebyeCalculator
A vectorised implementation of the Debye Equation on CPU and GPU
- .github Public
- ML-MotEx Public Forked from AndySAnker/ML-MotEx
Code for Machine Learning based Motif Extractor. A tool to extract motifs from numerous fits using explainable machine learning.
- DeepStruc Public Forked from EmilSkaaning/DeepStruc
DeepStruc is a Conditional Variational Autoencoder which can predict the mono-metallic nanoparticle from a Pair Distribution Function.
- MetalFinder Public Forked from AndySAnker/MetalFinder
MetalFinder is a tree-based supervised learning algorithm which can predict the mono-metallic nanoparticle from a Pair Distribution Function.
- Brute-force-PDF-modelling Public Forked from AndySAnker/Brute-force-PDF-modelling
MetalFinder is a brute-force approach to predict the mono-metallic nanoparticle from a Pair Distribution Function.
- CVAE Public Forked from AndySAnker/CVAE
CVAE is a Conditional Variational Autoencoder which can predict the mono-metallic nanoparticle from a Pair Distribution Function.
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