Publications of Christian Carbogno

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

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

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

N.S. Müller, 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 and A. Paarmann: Full Crystallographic Imaging of Hexagonal Boron Nitride Monolayers with Phonon-Enhanced Sum-Frequency Microscopy., in press.

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

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

J. Quan, M.-Y. Zhang, M. Scheffler and C. Carbogno: Temperature-Dependent Electronic Spectral Functions From Band-Structure Unfolding., in press.

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

S. Lu, L.M. Ghiringhelli, C. Carbogno, J. Wang and M. Scheffler: On the Uncertainty Estimates of Equivariant-Neural-Network-Ensembles Interatomic Potentials., in press.