Project Manager:
Knut Vietze

Quantum Penomena in low-dimensional Nanostructures

Principal Investigators:
Prof. Dr. Thomas Heine
Affiliation:
TU Dresden
HPC Platform used:
NHR@TUD: TAURUS

We explore new materials in the nanoworld, nanomaterials that behave different from what we know from daily life. For the first time we exploit the beautiful symmetry of crystal lattices with the rich diversity of molecular building blocks. Linked together in framework materials or two-dimensional polymers they form a new class of hybrid materials and offer the implementation of new concepts for catalysis without precious metals, high-efficiency hydrogen generation, and precision sensing, to name just a few. These developments have been made possible by the enormous power of the high-performance computing facilities at ZIH Dresden.

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:
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

Project Manager:
Marcel Sadowski

Ab-initio Modeling of Battery Materials

Principal Investigators:
Prof. Dr. rer. nat. Karsten Albe
Affiliation:
Technische Universität Darmstadt
HPC Platform used:
NHR4CES@TUDa: Lichtenberg Cluster Darmstadt

One approach to the realization of safer batteries relies on all solid-state batteries (ASSB) which use a non-flammable solid electrolyte (SE) instead of the commercial flammable liquid organic electrolytes. While many obstacles to the successful production of these batteries have already been overcome, the inner and outer interfaces in a real battery setup remain a major challenge. Thus, a thorough understanding of the interfacial atomistic processes is crucial, highlighting the value of interface simulations on the atomic scale. Currently, these are only possible via ab-initio methods, such as density functional theory (DFT) calculations, because no classical interatomic potentials exist, which can simultaneously describe the SE and

Material Sciences abonnieren