All-electron formalism for total energy strain derivatives and stress tensor components for numeric atom-centered orbitals. - Franz Knuth; Christian Carbogno; Viktor Atalla; Volker Blum; Matthias Scheffler (Comput. Phys. Commun., 2015)

Lattice dynamics calculations based on density-functional perturbation theory in real space. - Honghui Shang; Christian Carbogno; Patrick Rinke; Matthias Scheffler (Comput. Phys. Commun., 2017)

ELPA: A Parallel Solver for the Generalized Eigenvalue Problem. - Hans-Joachim Bungartz; Christian Carbogno; Martin Galgon; Thomas Huckle; Simone S. Köcher; Hagen-Henrik Kowalski; Pavel Kus; Bruno Lang; Hermann Lederer; Valeriy Manin; Andreas Marek; Karsten Reuter; et al. (PARCO, 2019)

Optimizations of the eigensolvers in the ELPA library. - Pavel Kus; Andreas Marek; Simone S. Köcher; Hagen-Henrik Kowalski; Christian Carbogno; Christoph Scheurer; Karsten Reuter; Matthias Scheffler; Hermann Lederer (Parallel Comput., 2019)

FHI-vibes: _Ab Initio_ Vibrational Simulations. - Florian Knoop; Thomas A. r. Purcell; Matthias Scheffler; Christian Carbogno (J. Open Source Softw., 2020)

SISSO++: A C++ Implementation of the Sure-Independence Screening and Sparsifying Operator Approach. - Thomas A. r. Purcell; Matthias Scheffler; Christian Carbogno; Luca M. Ghiringhelli (J. Open Source Softw., 2022)

Heat flux for semi-local machine-learning potentials. - Marcel F. Langer; Florian Knoop; Christian Carbogno; Matthias Scheffler; Matthias Rupp (CoRR, 2023)

A machine learning route between band mapping and band structure. - R. Patrick Xian; Vincent Stimper; Marios Zacharias; Maciej Dendzik; Shuo Dong; Samuel Beaulieu; Bernhard Schölkopf; Martin Wolf; Laurenz Rettig; Christian Carbogno; Stefan Bauer; Ralph Ernstorfer (Nat. Comput. Sci., 2023)

Accelerating the Training and Improving the Reliability of Machine-Learned Interatomic Potentials for Strongly Anharmonic Materials through Active Learning. - Kisung Kang; Thomas A. r. Purcell; Christian Carbogno; Matthias Scheffler (CoRR, 2024)