Publikationen von Christian Carbogno

Carbogno, C.:

Carbogno, C.:

Carbogno, C.:

Carbogno, C.:

Carbogno, C.:

Carbogno, C.:

Carbogno, C.:

Carbogno, C.:

Carbogno, C.:

Carbogno, C.:

Carbogno, C.:

Carbogno, C.:

Carbogno, C.:

Fiebig, F.: Assessing Electronic Transport in Solid Materials via the Fluctuation-Dissipation Theorem. Technische Universität Berlin

Zhu, X.: Ab Initio Green-Kubo Calculations for Strongly Anharmonic Solids: A comparative Benchmark of Lattice Thermal Conductivities. Technische Universität Darmstadt

Ahmetcik, E.: Machine Learning of the Stability of Octet Binaries. Technische Universität Berlin

Carbogno, C., N. Rybin, S. Panahian Jand, A. Akkoush, Z. Yuan, M. Rossi und C.M. Acosta: Polarisation, Born Effective Charges, and Topological Invariants via a Berry-Phase Approach., im Druck.

Müller, N.S., A.P. Fellows, B. John, A.E. Naclerio, C. Carbogno, K. Gharagozloo-Hubmann, D. Balaz, R.A. Kowalski, H. Heenen, C. Scheurer, K. Reuter, J.D. Caldwell, M. Wolf, P.R. Kidambi, M. Thämer und A. Paarmann: Full Crystallographic Imaging of Hexagonal Boron Nitride Monolayers with Phonon-Enhanced Sum-Frequency Microscopy., im Druck.

Stocco, E., C. Carbogno und M. Rossi: Electric-Field Driven Nuclear Dynamics of Liquids and Solids from a Multi-Valued Machine-Learned Dipolar Model., im Druck.

Kang, K., M. Scheffler, C. Carbogno und T. Purcell: Accelerating the Training and Improving the Reliability of Machine-Learned Interatomic Potentials for Strongly Anharmonic Materials Through Active Learning., im Druck.