Computational Quantum Chemistry - Jensen Group
The main research interest of the group is the development of new computational methods and applying them to important chemical problems.
The group works on the discovery of new molecules and chemical reactions – at the interface of machine learning and quantum chemistry. In particular, the group has revitalised the use of genetic algorithms in molecule discovery and shown that fast quantum mechanical methods can be used in high-throughput screening of chemical reactivity
We are part of the Open Source Antibiotics project – an international consortium of researchers interested in open ways to discover and develop new, inexpensive medicines for bacterial infections. The group is using genetic algorithms and docking software to identify new antibiotics candidates that other consortium members will test experimentally. The challenge is to identify molecules that not only bind to a particular protein, but that also will not be pumped out of the cell again - a common defense mechanism of Gram-negative bacteria.
We are collaborating with colleagues at the University of Copenhagen to find molecules that can store solar energy as chemical energy, without any thermal insulation, which can later be released as heat. The challenge is to find molecules that store as much solar energy per unit mass as possible for weeks to month. We have used a combination of quantum chemistry and machine learning to show that two of the main candidates for such “heat batteries” cannot be tweaked to absorb enough energy to make a real difference and that new fundamentally new type of molecules are needed.
We have just started two new projects on catalysis discovery by combining our genetic algorithm and machine learning tools. One of the goals is to develop a catalyst that can efficiently convert the nitrogen in the atmosphere to fertilizer – a process that currently consumes 1% of the world’s energy consumption. This is a collaboration with Tejs Vegge. Computational catalysts discovery is especially challenging since chemical reactivity one of the most difficult chemical property to predict both accurately and efficiently.
Group leader Jan H. Jensen is a member of the Acceleration Consortium - a global network of government, academia, and industry who are working to realize the age of materials on demand