Member since 10/2017
- Crystal Structure Prediction
- Materials Property Prediction
- Kernel-based Machine Learning
- Active Learning
- Density Functional Theory
M. F. Langer, F. Knoop. C. Carbogno, M. Scheffler, and M. Rupp,
Heat flux for semi-local machine-learning potentials.
Phys. Rev. B (Letter) 108, L100302 (2023); https://doi.org/10.1103/PhysRevB.108.L100302
M. F. Langer, A. Goeßmann, and M. Rupp,
Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning.
npj Computational Materials 8, 41 (2022); https://doi.org/10.1038/s41524-022-00721-x
Within the publication lists the label [abs,src,ps] links to abstract, source file, and postscript version of the respective paper on the e-print archives of xxx.lanl.gov. Source and postscript files are usually transfered as gz-compressed tar-files. If your browser cannot automatically unpack those files you should proceed as follows:
Save the file as paper.tar.gz Execute the following commands (on a UNIX machine): gunzip paper.tar.gz tar -xvf paper.tar