Technical University of Munich
Chair of AI-based Materials Science (Prof. Rinke)
James-Franck-Str. 1
85748 Garching b. München
The Chair of AI-based Materials Science is developing electronic structure and machine learning methods and applies them to pertinent problems in material science, surface science, physics, chemistry and the nano sciences.The electronic structure gives us an atomistic view on matter that is important for many applications.
Examples are materials for clean energy production, light-emitting devices (LEDs) or information and communication technologies (ICT). Perturbing the electronic structure, as done in spectroscopy, reveals more information about matter.
We develop and use theoretical spectroscopy methods to probe the properties of molecules, molecules on surfaces, nanostructures, as well as semiconductors and their surfaces. We also investigate data as new resource in materials science. We participate in the development of a large scale materials database and study the potential of database driven materials science.
npj Computational Materials
Abstract: The presence of defects strongly influences semiconductor behavior. However, predicting the electronic properties of defective materials at finite temperatures remains computationally expensive even…
Journal of Chemical Physics
Abstract: The study of aerosol formation and chemistry using machine learning is limited by the lack of molecular descriptors suited to atmospheric compounds. Interpretable models are particularly affected…
Scientific Data
Abstract: Data-driven materials discovery to accelerate the development of new catalysts for the green transition shows great promise, but requires machine-interpretable experimental data. For this purpose, we…
Journal of Chemical Physics
Abstract: Active learning (AL) has shown promise to be a particularly data-efficient machine learning approach. Yet, its performance depends on the application, and it is not clear when AL practitioners can…
American Chemical Society Environmental Science and Technology Air
Abstract: New particle formation (NPF) is a major source of atmospheric aerosol particles, significantly influencing particle number concentrations in urban environments. High condensation and coagulation sinks…
Computational Materials Science
Abstract: The GW approximation within many-body perturbation theory is the state of the art for computing quasiparticle energies in solids. Typically, Kohn–Sham (KS) eigenvalues and eigenfunctions, obtained…
Physical Chemistry Chemical Physics
Abstract: Peroxy radicals (RO2) are ubiquitous intermediates in many oxidation processes, especially in the atmospheric gas phase. The recombination reaction of two peroxy radicals (RO2 + R′O2) has been…
npj Computational Materials
Abstract: Infrared (IR) spectroscopy is a pivotal analytical tool as it provides real-time molecular insight into material structures and enables the observation of reaction intermediates in situ. However,…
Physical Review Materials
Abstract: Lead-based perovskite solar cells have reached high efficiencies, but toxicity and lack of stability hinder their wide-scale adoption. These issues have been partially addressed through compositional…
npj Computational Materials
Abstract: Transforming CO2 into methanol represents a crucial step towards closing the carbon cycle, with thermoreduction technology nearing industrial application. However, obtaining high methanol yields and…
| Title | Dates | Duration | Type | Lecturer (assistant) |
|---|---|---|---|---|
| Academic Writing Skills: Scientific Publications |
|
2 | SE | |
| Beyond Smart Textiles |
|
1 | SE | |
| Computer Tutorial to Introduction to Machine Learning for Materials Science |
|
2 | UE | |
| Current Topics in AI-Based Materials Science |
|
2 | SE | |
| Introduction to Machine Learning for Materials Science |
|
2 | VO | |
| Machine Learning for Natural Sciences |
|
2 | PS | |
| Revision Course to Machine Learning for Natural Sciences |
|
2 | RE |
| Title | Dates | Duration | Type | Lecturer (assistant) |
|---|---|---|---|---|
| Academic Writing Skills: Scientific Publications |
|
2 | SE | |
| Atomistic Machine Learning |
|
2 | VO | |
| Computer Tutorial to Atomistic Machine Learning |
|
2 | UE | |
| Current Topics in AI-Based Materials Science |
|
2 | SE | |
| Mentoring in the Bachelor’s Program Physics |
|
0.2 | KO | |
| PREP: Practical Research Experience Program, Chair of AI-based Materials Science |
|
10 | PR | |
| Seminar of the Atomistic Modeling Center |
|
2 | SE | |
| Summer School Atomistic Modeling Center |
|
1 | WS |