This seminar series is open to interested researcher and aims to foster the discussion of new theories and novel methods related to electronic structure theory, machine learning, molecular dynamics, and structure exploration.
If you want to join a seminar, please register below, we will then send you a link via e-Mail. We are looking forward to fruitful discussions!
Winter term 2021/22
January 14, 2022, 1.00 pm GMT
Tensor-valued atomic cluster expansion for inference of dynamical systems
by Ass.-Prof. Dr. Matthias Sachs
School of Mathematics, University of Birmingham, United Kingdom
 M. Bachmayr et al., “Atomic Cluster Expansion: Completeness, Efficiency and Stability,” Nov. 2019.
 R. Drautz, “Atomic cluster expansion for accurate and transferable interatomic potentials,” Phys. Rev. B, vol. 99, no. 1, p. 014104, 2018.
Autumn term 2021
October 8, 2021, 1.00 pm BST
Charge Transfer Mechanisms & Selectivity in Heterogeneous
by Dr. Vanessa J. Bukas*
Theory Department, Fritz-Haber-Institute of the Max-Planck-Society,
Faradayweg 4-6, 14195, Berlin, Germany
Heterogeneous electrocatalysis holds promise for the continued production of fuels and chemicals in a sustainable, fossil-free future. Despite substantial progress over the past decade, however, there is still an urgent need for improved electrocatalysts before clean energy technologies can be considered viable for widespread application. A key challenge in meeting this goal will be to better control catalyst selectivity, especially toward high-value products such as e.g. long hydrocarbons, oxygenates, and ammonia. Such efforts are presently hindered by our limited fundamental understanding of electrocatalytic selectivity in general. Even for extremely well-defined experimental systems, identity of the favored product is often unexplained and cannot be rationalized by popular thermodynamic models of concerted proton-electron transfers (CPETs) that are commonly used in computational electrocatalysis.
This talk will challenge prevalent mechanistic assumptions and discuss microscopic reaction mechanisms under conditions of limiting electron or proton availability, as a means to selectively suppress specific CPET steps or reaction routes. The idea will be showcased for a handful of systems where delayed charge transfer is suggested as a determining factor to the resulting product selectivity. Atomistic models, based mainly on density functional theory, will further be used to show that such mechanisms can explain key experimental findings and even be exploited to systematically steer product selectivity in technologically important electrochemical processes. This suggests departure from the common-practice approach of predicting activity trends based on thermodynamic descriptors of specific charge-neutral surface intermediates and underlines interfacial charge transfer kinetics as a promising research direction that may help gain better control over electrocatalytic selectivity.
October 29, 2021, 10.00 am BST
Ehrenfest TD-DFTB and its applications in transient absorption and impulsive Raman spectroscopies
by Dr. Franco Bonafé
Max Planck Institute for the Structure and Dynamics of Matter, 22761 Hamburg, Germany
Several scientific and technological fields demand fast and efficient computational tools to understand the ultrafast quantum dynamics after photoexcitation of relatively large matter systems. Such tools should also be able to account for nuclear motion, as in many situations coupling of electronic excited states with molecular vibrations can be crucial to describe these processes. In this seminar I will describe an implementation of semiclassical Ehrenfest dynamics within time-dependent density functional tight-binding (TD-DFTB) in the free, open-source DFTB+ package. I will show some successful applications of this tool to describe light-induced impulsive vibrations in metal nanoparticles and molecules. Finally, I will explain how this method can be used to simulate transient absorption spectra and impulsive Raman spectra.
November 11, 2021, 10.00 am BST
Setting benchmarks for theoretical models of gas-surface scattering
by Dr. Helen Chadwick
Department of Chemistry, College of Science, Swansea University, Swansea, UK
Several factors can affect the outcome of a collision of a gas phase molecule with a surface, from the velocity of the incoming molecule to the internal energy states of that molecule. The most stringent tests of the accuracy of theoretical models of these collisions are arguably provided by state-resolved experiments, which remove the averaging over the many degrees of freedom that influence the gas-surface interaction. One particularly challenging quantum state to control is the rotational orientation projection (mJ) state, especially for ground state molecules. Here, a new technique will be presented , which allows the rotational orientation of ground state molecules to be prepared, manipulated and controlled using a series of homogeneous and inhomogeneous magnetic fields. The results that have been obtained using this method for the scattering of H2 from LiF will be shown . Analysis of the data shows that scattering from a LiF crystal can in itself produce a rotationally oriented H2 beam, as predicted theoretically over 20 years ago . In addition, we can determine both the amplitude and the phase change of the wavefunction as the molecules scatter from the surface, which provides a further, more stringent test of current state of the art theory, and will help to develop and benchmark theoretical models.
 O. Godsi, G. Corem, Y. Alkoby, J.T. Cantin, R. V Krems, M.F. Somers, J. Meyer, G.J. Kroes, T. Maniv, and G. Alexandrowicz, Nat. Commun. 8, 15357 (2017).
 Y. Alkoby, H. Chadwick, O. Godsi, H. Labiad, M. Bergin, J.T. Cantin, I. Litvin, T. Maniv, and G. Alexandrowicz, Nat. Commun. 11, 3110 (2020).
 E. Pijper and G.J. Kroes, Phys. Rev. Lett. 80, 488 (1998).
Summer term 2021
April 30, 2021, 1.00 pm BST
Tunneling and Zero-Point Energy Effects in Multidimensional Hydrogen Transfer Reactions
by Dr. Yair Litman
Max Planck Institute for the Structure and Dynamics of Matter, 22761 Hamburg, Germany
 J. Waluk, Chem. Rev. 117, 2447 (2017)
 Y. Litman, J. O. Richardson, T. Kumagai, and M. Rossi, J. Am. Chem. Soc. 141, 2526 (2019)
 Y. Litman, J. Behler, and M. Rossi, Faraday Discuss. 221, 526 (2020)
 Y. Litman and M. Rossi, Phys. Rev. Lett. 125, 216001 (2020)
May 7, 2021, 1.00 pm BST
Accurate computation of core- and valence-level excitations of large systems from GW-based methods
by Dr. Dorothea Golze
Department of Applied Physics, Aalto University, 02150 Espoo, FinlandGW has become the method of choice for the calculation of valence photoemission spectra . To apply GW also to deep core excitations as measured by X-ray photoelectron spectroscopy (XPS), we recently advanced the GW methodology and our implementation by combining exact numeric algorithms in the real frequency domain  with partial self-consistency  and relativistic corrections [3,4]. We benchmarked our core-level GW implementation for small molecules and combined our GW methodology with machine learning (ML) models to address also larger system sizes. We developed a powerful XPS prediction tool for materials and molecules containing carbon, hydrogen and oxygen by combining density functional theory and GW calculations with Kernel Ridge Regression ML models. Another strategy to reach larger system sizes is to reduce the scaling of the GW algorithms. In this talk, our latest work  on the development of an accurate low-scaling GW algorithm and its application to valence-level excitations of phosphorene nanosheets will be presented.
 D. Golze, M. Dvorak, P. Rinke, Front. Chem, 7 (2019), 377
 D. Golze, J. Wilhelm, M. van Setten, P. Rinke, JCTC, 14 (2018), 4856
 D. Golze, L. Keller, P. Rinke, JPCL, 11 (2020), 1840
 L. Keller, V. Blum, P. Rinke, D. Golze, JCP, 153 (2020), 114110 J. Wilhelm, P. Seewald, D. Golze, JCTC, 17 (2021), 1662
June 4th, 2021, 1.00 pm BST
Generating 3d molecular structures with deep neural networks
by Niklas Gebauer
Machine Learning Group, Technical University of Berlin, 10587 Berlin, Germany
Deep learning has proven to yield fast and accurate predictions of quantum-chemical properties to accelerate the discovery of novel molecules and materials.
As an exhaustive exploration of the vast chemical space is still infeasible, we require generative models that guide our search towards systems with desired properties.
While graph-based models have previously been proposed, they are restricted by a lack of spatial information such that they are unable to recognize spatial isomerism and non-bonded interactions.
Generative models for 3-dimensional structures are an emerging field promising to overcome these restrictions.
In this talk we will introduce the challenges of 3d molecule generation and focus on the autoregressive deep neural network G-SchNet which generates molecular structures by placing one atom after another in 3d space.
The model is capable of generating novel molecules that capture the characteristics of the training data and exhibit desired properties.