Publications of Johannes Margraf
All genres
Journal Article (25)
2022
Journal Article
Wengert, Simon, , Karsten Reuter and Johannes Margraf: A Hybrid Machine Learning Approach for Structure Stability Prediction in Molecular Co-crystal Screenings.
2021
Journal Article
, , Ke Chen, Johannes Margraf, and Karsten Reuter: Subgroup Discovery Points to the Prominent Role of Charge Transfer in Breaking Nitrogen Scaling Relations at Single-Atom Catalysts on VS2.
Journal Article
Staacke, Carsten, Hendrik Heenen, Christoph Scheurer, , Karsten Reuter and Johannes Margraf: On the Role of Long-Range Electrostatics in Machine-Learned Interatomic Potentials for Complex Battery Materials.
Journal Article
Timmermann, Jakob, Yonghyuk Lee, Carsten Staacke, Johannes Margraf, and Karsten Reuter: Data-Efficient Iterative Training of Gaussian Approximation Potentials: Application to Surface Structure Determination of Rutile IrO2 and RuO2.
Journal Article
, , Karsten Reuter and Johannes Margraf: Data-efficient machine learning for molecular crystal structure prediction.
Book Chapter (1)
2021
Book Chapter
Wengert, Simon, Christian Kunkel, Johannes Margraf and Karsten Reuter: Accelerating molecular materials discovery with machine-learning.
Talk (20)
2023
Talk
Margraf, Johannes: Science-Driven Chemical Machine Learning.
(MCIC 2023: Materials Science Meets Artificial Intelligence – Advancements in Research and Innovation, Bochum, Germany, Aug 2023).
Talk
Margraf, Johannes: Robust and Electrostatics-Aware Machine Learning Potentials.
(CECAM Psi-k Research Conference, Bridging Length Scales with Machine Learning, Berlin, Germany, Jun 2023).
Talk
Margraf, Johannes: Science-Driven Chemical Machine Learning.
(Colloquium for theoretical chemistry, Universität Marburg, Online Event, Apr 2023).
Talk
Margraf, Johannes: Integrating Machine Learning and Electronic Structure Theory.
(Seminar, Department of Chemistry, Humboldt-Universität zu Berlin, Berlin, Germany, Feb 2023).
Talk
Margraf, Johannes: Physical Description of Long-Range Interactions in Atomistic Machine Learning Models.
(Seminars on Machine Learning in Quantum Chemistry and Quantum Computing for Quantum Chemistry (SMLQC), Online Event, May 2023).
Talk
Margraf, Johannes: Science Driven Chemical Machine Learning.
(12th SolTech Conference 2023, Würzburg, Germany, Oct 2023).
Talk
Margraf, Johannes: A Personal Perspective on ML Interatomic Potentials.
(Crash TEsting machine learning force fields: Applicability, best practices, limitations (TEA 2023), Luxembourg, Luxembourg, Oct 2023).
Talk
Margraf, Johannes: Science Driven Chemical Machine Learning.
(Thomas Young Center-FHI Workshop, London, UK, Feb 2023).
2022
Talk
Margraf, Johannes: Heterogeneous Catalysis in Grammar School.
(FHI-Workshop on Current Research Topics at the FHI, Potsdam, Germany, May 2022).
Talk
Margraf, Johannes: Data-Efficient Chemical Machine Learning.
(KAIST Theory Seminar, Seoul, South Korea, Online Event, Jan 2022).
Talk
Margraf, Johannes: Data-Efficient Chemical Machine Learning.
(Institutskolloquium, Institute of Chemistry, University of Potsdam, Online Event, Jan 2022).
Talk
Margraf, Johannes: Describing Complex Polar Materials With Physics-Enhanced Machine Learning.
(ACS Spring Meeting 2022, Symposium, Complexity in Computational Catalysis: Balancing Model and Method Accuracy: Machine Learning and Kinetic Modeling, Online Event, Mar 2022).
Talk
Margraf, Johannes: Predicting Molecular Properties through Machine Learned Energy Functionals.
(ML4M 2022, Young Researcher’s Workshop on Machine Learning for Materials 2022, Trieste, italy, May 2022).
Talk
Margraf, Johannes: Predicting Molecular Properties through Machine Learned Energy Functionals.
(Seminar, VirtMat, Karlsruhe Institute of Technology (KIT), Online Event, Jun 2022).