Catalysis Data Science
In the Bligaard research group we develop a data science-enhanced approach to studying catalytic systems, materials, and processes.
We:
- Create data bases of atomic-scale simulations and experiments
- Enhance search with atomic-scale simulations though the use of genetic algorithms
- Device methods for uncertainty quantification in catalysis simulations
- Accelerate simulations through the use of surrogate machine learning models and active learning
- Make algorithms for treating large and complex reaction networks
- Develop arbitrary-precision arithmetic tools for solving microkinetic models
List of Publications:
Selected Publications:
Fundamental Concepts in Heterogeneous Catalysis, J.K. Nørskov, F. Studt, F. Abild-Pedersen, T. Bligaard, John Wiley & Sons, Inc. (2014), http://doi.wiley.com/10.1002/9781118892114
Low-Scaling Algorithm for Nudged Elastic Band Calculations Using a Surrogate Machine Learning Model, J.A.G. Torres, P.C. Jennings, M.H. Hansen, J.R. Boes, T. Bligaard, Phys. Rev. Lett. 122, 15, 156001 (2019) https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.122.156001
Graph Theory Approach to High-Throughput Surface Adsorption Structure Generation, J.R. Boes, O. Mamun, K. Winther, T. Bligaard, J. Phys. Chem. A 123, 11, 2281-2285 (2019) https://pubs.acs.org/doi/10.1021/acs.jpca.9b00311
Catalysis-Hub.org an open electronic structure database for surface reactions, K.T. Winther, M.J. Hoffmann, J.R. Boes, O. Mamun, M. Bajdich, T. Bligaard, Scientific Data 6, 75 (2019) http://www.nature.com/articles/s41597-019-0081-y, https://www.catalysis-hub.org
Genetic algorithm for computational materials discovery accelerated by machine learning, P.C. Jennings, S. Lysgaard, J.S. Hummelshøj, T. Vegge, T. Bligaard, NPJ Computational Materials 5, 46 (2019) http://www.nature.com/articles/s41524-019-0181-4