TheMeCat
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…
Prof. Patrick Rinke
Prof. Dr. Patrick Rinke
Technical University of Munich
Chair of AI-based Materials Science (Prof. Rinke)
Postal address
James-Franck-Str. 1
85748 Garching b. München
- Phone: +49 89 289 54321
- Room: 5101.EG.093
- Homepage
- E-mail: patrick.rinke@tum.de
Research
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.
187 Publications
An interpretable molecular descriptor for machine learning predictions in atmospheric 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…
Data-efficient optimization of thermally-activated polymer actuators through machine learning
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…
Enhancing Lignin-Carbohydrate Complexes Production and Properties With Machine Learning
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…
Efficient dataset generation for machine learning halide perovskite alloys
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…
Exploring noncollinear magnetic energy landscapes with Bayesian optimization
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…
SP-LCC — a dataset on the structure and properties of lignin-carbohydrate complexes from hardwood
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…
Predicting intersystem crossing rate constants of alkoxy-radical pairs with structure-based descriptors and machine learning
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…
Similarity-based analysis of atmospheric organic compounds for machine learning applications
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…
Machine learning accelerated descriptor design for catalyst discovery in CO2 to methanol conversion
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…
Winter term 2025/26
| 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 |
Summer term 2026
| 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 | |
| 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 |