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2020 – today
- 2023
[j2]Danny Panknin, Stefan Chmiela, Klaus-Robert Müller, Shinichi Nakajima:
Local Function Complexity for Active Learning via Mixture of Gaussian Processes. Trans. Mach. Learn. Res. 2023 (2023)- [i6]J. Thorben Frank, Oliver T. Unke, Klaus-Robert Müller, Stefan Chmiela:
From Peptides to Nanostructures: A Euclidean Transformer for Fast and Stable Machine Learned Force Fields. CoRR abs/2309.15126 (2023) - 2022
- [i5]Niklas Frederik Schmitz, Klaus-Robert Müller, Stefan Chmiela:
Algorithmic Differentiation for Automatized Modelling of Machine Learned Force Fields. CoRR abs/2208.12104 (2022) - [i4]Stefan Blücher, Klaus-Robert Müller, Stefan Chmiela:
Reconstructing Kernel-based Machine Learning Force Fields with Super-linear Convergence. CoRR abs/2212.12737 (2022) - 2021
- [i3]Oliver T. Unke, Stefan Chmiela, Michael Gastegger, Kristof T. Schütt, Huziel E. Sauceda, Klaus-Robert Müller:
SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects. CoRR abs/2105.00304 (2021) - [i2]J. Thorben Frank
, Stefan Chmiela:
Detect the Interactions that Matter in Matter: Geometric Attention for Many-Body Systems. CoRR abs/2106.02549 (2021) - [i1]Huziel E. Sauceda, Luis E. Gálvez-González, Stefan Chmiela, Lauro Oliver Paz-Borbón, Klaus-Robert Müller, Alexandre Tkatchenko:
BIGDML: Towards Exact Machine Learning Force Fields for Materials. CoRR abs/2106.04229 (2021)
2010 – 2019
- 2019
- [j1]Stefan Chmiela, Huziel Enoc Sauceda Felix, Igor Poltavsky, Klaus-Robert Müller, Alexandre Tkatchenko:
sGDML: Constructing accurate and data efficient molecular force fields using machine learning. Comput. Phys. Commun. 240: 38-45 (2019) - 2017
- [c1]Kristof Schütt, Pieter-Jan Kindermans, Huziel Enoc Sauceda Felix, Stefan Chmiela, Alexandre Tkatchenko, Klaus-Robert Müller:
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions. NIPS 2017: 991-1001
Coauthor Index
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