Publications of Johannes Margraf

Journal Article (21)

2021
Journal Article
Wengert, Simon, Gábor Csányi, 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 at the Interface of Physics and Chemistry, 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).
Talk
Margraf, Johannes: ∆-Learning with DFTB: What makes a good baseline?
(Workshop, Multi-Scale Quantum Mechanical Analysis of Condensed Phase Systems: Methods and Applications, Telluride Science Research Center, Telluride, CO, USA, Jul 2022).
Talk
Margraf, Johannes: Science Driven Chemical Machine Learning.
(Seminar, Debye Institute for Nanomaterials Science, Utrecht University, Utrecht, The Netherlands, Nov 2022).
2021
Talk
Margraf, Johannes: Chemical ML Beyond Established Benchmark Datasets.
(Workshop, ELLIS Machine Learning (ML) for Molecule Discovery, Online Event, Dec 2021).
Talk
Margraf, Johannes: Predicting Molecular Properties Through Machine Learned Energy Functionals.
(Discussion Meeting, GdR REST Machine Learning (ML), Online Event, Dec 2021).
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