Institute of Solid State Physics

Andreas Jeindl

Contact: ☎ ✉

   Understanding Molecular Monolayer Formation with Machine Learning
   Machine Learning Provides New Insights Into Organic-Inorganic Interfaces
   Huge Computing Time Project Granted

   Computational Material Design with DFT and Machine Learning

Physical Insight for Surface Polymorphs from Machine Learning
My research interests include
  • Structure Determination and Prediction
  • Polymorphism and Metastable Phases
  • Phase Diagrams and Phase Transformations
  • Adsorption Processes of Organic Molecules
  • Defects in Organic Monolayers
which I study (mostly) with the these techniques:
  • Density Functional Theory
  • Machine Learning (mostly Gaussian Process Regression)
  • Ab-initio Thermodynamics
Personal data

Recent talks

Meeting of the American Physical Society, March 2021, Online
Meeting of the German Physical Society, April 2019, Regensburg
IMPRESS Workshop, Jun 2018, Graz

--> last updated: 1. June 2021