Pillar C




  • Model complex catalytic processes on the mesoscale under realistic conditions
  • Use quantum mechanics and scale the results to the  relevant regime (millions of atoms) via machine learning
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Task Leaders & Team

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Principal Investigators and links to their websites

Bingqing Cheng: computer simulations to understand and predict material properties. She focuses on exploiting machine-learning methods to extend the scope of atomistic simulations.

Leticia González: computational photochemistry and photodynamics. She uses advanced electronic structure calculations and develops reaction dynamics methods for the ground and excited state properties of molecules, both isolated and embedded in complex environments.

One PD will extend excited state nonadiabatic simulations within SHARC. Extensions include dealing with multi-chromophoric systems, periodic boundary conditions, embedding schemes and machine learning potentials. PhD1 will do calculations on MOF-based photocatalytic systems, and PhD2 will investigate molecular antennas embedded in matrices. In both cases, the PhD students will take care to predict spectroscopic properties, follow charge-transfer processes in real time and disentangle reaction pathways with their corresponding intermediates.

Georg Kresse: developing algorithms to solve the density functional theory and many-body Schrödinger equations for condensed matter systems. His expertise covers ground-state and excited-state properties, as well as machine-learning techniques.

The group will contribute to Pillar C with state-of-the-art developments and applications in machine-learned force fields (already fully integrated in VASP), time-dependent density functional theory, in particular the implementation of interatomic forces in excited states, and machine-learning of self-consistent charge densities to open a pathway towards million-atom calculations within VASP. We are looking for two PhD students and one postdoctoral researcher to achieve these goals. The work will be supported by a University Assistant and all implementation work will be done in close collaboration with the VASP team.

Florian Libisch: embedding techniques for multi-scale simulations such as combining highly accurate many-body approaches with density functional theory for surface catalysis. His expertise also covers large-scale tight-binding frameworks for two-dimensional materials.

The Libisch group is looking for a PhD in embedding methods for surface catalysis: embedding approaches allow for combining different levels of theory (density functional theory, quantum-chemical correlated wavefunction approaches or large-scale tight binding) in one calculation [Accounts of Chemical Research, 47, 2768 (2014)]. In the proposed project, we will develop excited-state modeling using embedding methods. The second PhD will work on improving the description of long-range electrostatic interactions coupling a Poisson solver to the tight binding description npj Computational Materials 8, 116 (2022). Employed techniques include density functional theory using VASP, coding in Python / C++, and machine learning approaches for parametrizing material properties, tight-binding approaches or embedding frameworks. We are also looking for a post-doctoral candidate with experience in embedding methods, large-scale tight-binding approaches or machine learning techniques for material science.

Georg Madsen: theoretical materials chemistry. He has expertise in density functional theory, machine learning force fields, and transport properties. His expertise also covers materials for energy conversion, such as thermoelectric materials and catalysts.

One PhD will focus on the inclusion of long-range interactions in the MLFF, while the second PhD will focus on the Bayesian methods to sample the PES. A Postdoc with experience in molecular dynamics will overlook the project and focus on simulation of the CO2RR at bimetallic metals surface in the presence of charge and explicit solvents.