Stefan Chmiela

Orcid: 0000-0003-0892-952X

According to our database1, Stefan Chmiela authored at least 13 papers between 2017 and 2026.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

Online presence:

On csauthors.net:

Bibliography

2026
Learning Hamiltonian Flow Maps: Mean Flow Consistency for Large-Timestep Molecular Dynamics.
CoRR, January, 2026

2025
Atomic orbits in molecules and materials for improving machine learning force fields.
Mach. Learn. Sci. Technol., 2025

Sampling 3D Molecular Conformers with Diffusion Transformers.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, 2025

2024
Euclidean Fast Attention: Machine Learning Global Atomic Representations at Linear Cost.
CoRR, 2024

2023
Local Function Complexity for Active Learning via Mixture of Gaussian Processes.
Trans. Mach. Learn. Res., 2023

From Peptides to Nanostructures: A Euclidean Transformer for Fast and Stable Machine Learned Force Fields.
CoRR, 2023

2022
Reconstructing Kernel-based Machine Learning Force Fields with Super-linear Convergence.
CoRR, 2022

Algorithmic Differentiation for Automatized Modelling of Machine Learned Force Fields.
CoRR, 2022

2021
BIGDML: Towards Exact Machine Learning Force Fields for Materials.
CoRR, 2021

Detect the Interactions that Matter in Matter: Geometric Attention for Many-Body Systems.
CoRR, 2021

SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects.
CoRR, 2021

2019
sGDML: Constructing accurate and data efficient molecular force fields using machine learning.
Comput. Phys. Commun., 2019

2017
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017


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