Publications of Johannes Margraf

Journal Article (11)

2022
Journal Article
Chen, Ke, … Johannes Margraf: Reorganization energies of flexible organic molecules as a challenging target for machine learning enhanced virtual screening.
Journal Article
Keller, Elisabeth, … Johannes Margraf: Regularized second-order correlation methods for extended systems.
Journal Article
Levin, Nikita, … Ulrich Heiz: CO2-Activation by size-selected tantalum cluster cations (Ta1–16+): thermalization governing reaction selectivity.
Journal Article
Margraf, Johannes, … Karsten Reuter: Heterogeneous Catalysis in Grammar School.
Journal Article
Staacke, Carsten, … Christoph Scheurer: Tackling Structural Complexity in Li2 S-P2S5 Solid-State Electrolytes Using Machine Learning Potentials.
Journal Article
Staacke, Carsten, … Johannes Margraf: Kernel charge equilibration: efficient and accurate prediction of molecular dipole moments with a machine-learning enhanced electron density model.
Journal Article
Wengert, Simon, … Johannes Margraf: A Hybrid Machine Learning Approach for Structure Stability Prediction in Molecular Co-crystal Screenings.
2021
Journal Article
Li, Haobo, … 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, … Johannes Margraf: On the Role of Long-Range Electrostatics in Machine-Learned Interatomic Potentials for Complex Battery Materials.
Journal Article
Timmermann, Jakob, … Karsten Reuter: Data-Efficient Iterative Training of Gaussian Approximation Potentials: Application to Surface Structure Determination of Rutile IrO2 and RuO2.
Journal Article
Wengert, Simon, … Johannes Margraf: Data-efficient machine learning for molecular crystal structure prediction.

Book Chapter (1)

2021
Book Chapter
Wengert, Simon, … Karsten Reuter: Accelerating molecular materials discovery with machine-learning.

Talk (11)

2022
Talk
Margraf, Johannes et al.: Heterogeneous Catalysis in Grammar School. (FHI-Workshop on Current Research at the Interface of Physics and Chemistry, Potsdam, Germany, May 2022).
Talk
Margraf, Johannes et al.: Data-Efficient Chemical Machine Learning. (KAIST Theory Seminar, Seoul, South Korea, Online Event, Jan 2022).
Talk
Margraf, Johannes et al.: Data-Efficient Chemical Machine Learning. (Institutskolloquium, Institute of Chemistry, University of Potsdam, Online Event, Jan 2022).
Talk
Margraf, Johannes et al.: 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 et al.: 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 et al.: Predicting Molecular Properties through Machine Learned Energy Functionals. (Seminar, VirtMat, Karlsruhe Institute of Technology (KIT), Online Event, Jun 2022).
Talk
Margraf, Johannes et al.: ∆-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).
2021
Talk
Margraf, Johannes et al.: Chemical ML Beyond Established Benchmark Datasets. (Workshop, ELLIS Machine Learning (ML) for Molecule Discovery, Online Event, Dec 2021).
Go to Editor View