Project Manager:
Dr. Uwe Gerstmann

Photonic Materials from ab-initio Theory

Principal Investigators:
Prof. Dr. Wolf Gero Schmidt
Affiliation:
Paderborn University
HPC Platform used:
PC2: CPU cluster

Accurate parameter-free calculations of optical response functions for real materials and nanostructures still represent a major challenge for computational materials science. Our project focusses on the development and application of efficient but accurate ab-initio methods that give access to the linear and nonlinear optical spectra. We explore, on the atomistic level, how the material structure, its composition and defects, but also external parameters like stress, temperature or magnetic fields influence the optical response. It thus leads to a better understanding of existing materials and contributes to the design of new photonic materials.

Project Manager:
Prof. Dr. Christof Schütte

Machine Learning and Simulation for pH-Dependent Opioids

Principal Investigators:
Dr. Markus Weber
HPC Platform used:
NHR@ZIB: Lise

Strong painkillers (such as opioids) are essential to medicine. However, they are mostly addictive and have potentially deadly side effects. Simulation and machine learning techniques help in the search for tailor-made active substances that do not have these side effects. The interaction of the various algorithms raises questions about which computer architecture can support the calculations most effectively.

Project Manager:
Marius Trollmann

Resolving the Structure of mRNA-Vaccine Lipid Nanoparticles

Principal Investigators:
Prof. Dr. Rainer Böckmann
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen
HPC Platform used:
NHR@FAU: Alex GPU cluster

Lipid nanoparticles (LNPs) are very successfully employed as novel transport vehicles for mRNA vaccines. A major gap in our understanding and thus obstacle for future developments of nanoparticle-mRNA drugs, however, is the lack of a molecular picture and molecular insight into LNPs. In this project we aim to provide unique insight at the atomistic scale into the structure and mechanisms of these carriers.

Project Manager:
Chen Shen

CCDCGAN: Inverse design of crystal structures

Principal Investigators:
Jun. Prof. Dr. Hongbin Zhang
Affiliation:
Technische Universität Darmstadt
HPC Platform used:
NHR4CES@TUDa: Lichtenberg Cluster Darmstadt

Autonomous materials discovery with desired properties is one of the ultimate goals for modern materials science, and the current studies have been focusing mostly on high-throughput screening based on density functional theory calculations and forward modelling of physical properties using machine learning. Applying the deep learning techniques, we have developed a generative model which can predict distinct stable crystal structures by optimizing the formation energy in the latent space. It is demonstrated that the optimization of physical properties can be integrated into the generative model as on-top screening or backwards propagator, both with their own advantages. Applying the generative models on the binary Bi-Se system reveals that

Atomistic Simulations abonnieren