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!
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
May 24, 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.