Technische Universität München
Lehrstuhl für 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.
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…
Scientific Data
Abstract: Lignin-carbohydrate complexes (LCCs) are bioproducts with high potential as alternatives for petrochemicals. However, the complex structure and the lack of protocols for high-yield production limit…
ChemSusChem
Abstract: Lignin-carbohydrate complexes (LCCs) present a unique opportunity for harnessing the synergy between lignin and carbohydrates for high-value product development. However, producing LCCs in high yields…
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…
Materials and Design
Abstract: For applications in soft robotics and smart textiles, thermally-activated, twisted, and coiled polymer actuators can offer high mechanical actuation with proper optimization of their processing…
Geoscientific Model Development
Abstract: The formation of aerosol particles in the atmosphere impacts air quality and climate change, but many of the organic molecules involved remain unknown. Machine learning could aid in identifying these…
Materials and Design
Abstract: The development of digitally enhanced fabrics is growing, but progress is currently being hampered by a lack of sustainable alternatives to metallic conductors. In particular, the process of testing…
Digital Discovery
Abstract: The investigation of magnetic energy landscapes and the search for ground states of magnetic materials using ab initio methods like density functional theory (DFT) is a challenging task. Complex…
| Titel | Termine | Dauer | Art | Vortragende/r (Mitwirkende/r) |
|---|---|---|---|---|
| Academic Writing Skills: Scientific Publications |
|
2 | SE | |
| Aktuelle Themen in KI-basierte Materialwissenschaften |
|
2 | SE | |
| Beyond Smart Textiles |
|
1 | SE | |
| Computer-Übungen zu Einführung in Machinelles Lernen in den Materialwissenschaften |
|
2 | UE | |
| Einführung in Machinelles Lernen in den Materialwissenschaften |
|
2 | VO | |
| Machinelles Lernen in den Naturwissenschaften |
|
2 | PS | |
| Repetitorium zu Machinelles Lernen in den Naturwissenschaften |
|
2 | RE |
| Titel | Termine | Dauer | Art | Vortragende/r (Mitwirkende/r) |
|---|---|---|---|---|
| Academic Writing Skills: Scientific Publications |
|
2 | SE | |
| Aktuelle Themen in KI-basierten Materialwissenschaften |
|
2 | SE | |
| Computer-Übungen zu Maschinelles Lernen auf der atomaren Skala |
|
2 | UE | |
| Maschinelles Lernen auf der atomaren Skala |
|
2 | VO | |
| PREP: Practical Research Experience Program, Chair of AI-based Materials Science |
|
10 | PR | |
| Seminar des Atomistic Modeling Center |
|
2 | SE | |
| Sommerschule Atomistic Modeling Center |
|
1 | WS |