Search Results for author: Christian Carbogno

Found 4 papers, 2 papers with code

Accelerating the Training and Improving the Reliability of Machine-Learned Interatomic Potentials for Strongly Anharmonic Materials through Active Learning

no code implementations18 Sep 2024 Kisung Kang, Thomas A. R. Purcell, Christian Carbogno, Matthias Scheffler

Molecular dynamics (MD) employing machine-learned interatomic potentials (MLIPs) serve as an efficient, urgently needed complement to ab initio molecular dynamics (aiMD).

Active Learning

A machine learning route between band mapping and band structure

1 code implementation20 May 2020 Rui Patrick Xian, Vincent Stimper, Marios Zacharias, Shuo Dong, Maciej Dendzik, Samuel Beaulieu, Bernhard Schölkopf, Martin Wolf, Laurenz Rettig, Christian Carbogno, Stefan Bauer, Ralph Ernstorfer

Electronic band structure (BS) and crystal structure are the two complementary identifiers of solid state materials.

Data Analysis, Statistics and Probability Materials Science Computational Physics

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