252 citations · 819 across the 18 of their papers we have counts for
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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 +1
Molecular dynamics (MD) employing machine-learned interatomic potentials (MLIPs) serve as an efficient, urgently needed complement to ab initio molecular dynamics (aiMD). By traini…
Carrier Mobility of Strongly Anharmonic Materials from First Principles
Jingkai Quan, Christian Carbogno, Matthias Scheffler
First-principle approaches for phonon-limited electronic transport are typically based on many-body perturbation theory and transport equations. With that, they rely on the validit…
Self-interaction corrected SCAN functional for molecules and solids in the numeric atom-center orbital framework
Sheng Bi, Christian Carbogno, Igor Ying Zhang +1
Semilocal density-functional approximations (DFAs), including the state-of-the-art SCAN functional, are plagued by the self-interaction error (SIE). While this error is explicitly…