Catalysis Data Science

In the Bligaard research group we develop a data science-enhanced approach to studying catalytic systems, materials, and processes.


  • 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

Selected publications:

Fundamental Concepts in Heterogeneous Catalysis, J.K. Nørskov, F. Studt, F. Abild-Pedersen, T. Bligaard, John Wiley & Sons, Inc. (2014),

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)

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) 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),

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)

Complete list of publications


Thomas Bligaard
DTU Energy
+45 21 12 03 21