Research
The theme of our work is well described by a famous quote of Dirac:
"It therefore becomes desirable that approximate practical methods of applying quantum mechanics should be developed, which can lead to an explanation of the main features of complex atomic systems without too much computation."
Non-adiabatic effects near metal surfaces
Electron transfer between metal and molecules is known to be important for heterogeneous catalysis. This electron transfer can be responsible for fast vibrational energy relaxation. Additionally, the role of quantum effects of high frequency molecular modes to energy transfer and reactions is not clear. We develop new theoretical methods to incorporate both non-adiabatic effects as well quantum nuclear effects in simulations.
The applications of this is quite vast: heterogeneous catalysis where hydrogen atoms are generated for example. Or chemicurrents observed on impinging small atoms on metallic surfaces.
Electronic Energy transfer in molecular wires
Molecular wires designed by nature are highly efficient and robust. There are recent evidences that they might employ quantum coherences for efficiency and robustness. We look at why the parameters chosen by nature are efficient or robust. How the coherences play a role. And what do we learn from it? Can we artificially design molecular wires that can compete with nature?
We employ numerically exact quantum dynamical method, quasi-classical method (surface hopping) and rate theories to answer the above questions with special attention to quantum effects that vibrational motion can have.
Efficient MD-simulations that include quantum nuclear effects
Lighter nuclei like proton shows quantum (wavelike) behaviour. Molecular simulations on the other hand usually treat nuclei as particles moving smoothly on potential energy surfaces. Including this wavelike behaviour of nuclei in simulations is a challenge.
We develop new methods to include these quantum effects towards efficient on-the-fly simulations. For this we are employing machine learning to predict the wavelike behavior of certain nuclei and include in the simulations.
