Stefan Chmiela

Beijing Institute of Big Data Research — China

Materials Science · Physical Sciences

16h-index5.4kcitations52works10.92yr avg
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Research Topics

Machine Learning in Materials Science(45), Protein Structure and Dynamics(22), Computational Drug Discovery Methods(17), Force Microscopy Techniques and Applications(4), Model Reduction and Neural Networks(4)

Publications52 total

Molecular Simulations with a Pretrained Neural Network and Universal Pairwise Force Fields
Journal of the American Chemical Society·2025· 28 citedOpen Access
Atomic orbits in molecules and materials for improving machine learning force fields
Machine Learning Science and Technology·2025· 1 citedOpen Access
Sampling 3D Molecular Conformers with Diffusion Transformers
arXiv (Cornell University)·2025· 1 citedOpen Access
Molecular Simulations with a Pretrained Neural Network and Universal Pairwise Force Fields
2025· 3 citedOpen Access
Atomic Orbits in Molecules and Materials for Improving Machine Learning Force Fields
2025Open Access
Stable molecular dynamics simulations of halide perovskites from a temperature-ensemble gradient-domain machine learning approach
Chemical Physics Letters·2025· 2 citedOpen Access
Molecular Simulations with a Pretrained Neural Network and Universal Pairwise Force Fields
2025· 5 citedOpen Access
Crash testing machine learning force fields for molecules, materials, and interfaces: molecular dynamics in the TEA challenge 2023
Chemical Science·2025· 12 citedOpen Access
Crash testing machine learning force fields for molecules, materials, and interfaces: model analysis in the TEA Challenge 2023
Chemical Science·2025· 10 citedOpen Access
Euclidean Fast Attention -- Machine Learning Global Atomic Representations at Linear Cost
arXiv (Cornell University)·2024· 2 citedOpen Access
Molecular Simulations with a Pretrained Neural Network and Universal Pairwise Force Fields
2024· 17 citedOpen Access
Crash Testing Machine Learning Force Fields for Molecules, Materials, and Interfaces: Model Analysis in the TEA Challenge 2023
2024· 7 cited
Crash Testing Machine Learning Force Fields for Molecules, Materials, and Interfaces: Molecular Dynamics in the TEA Challenge 2023
2024· 5 cited
A Euclidean transformer for fast and stable machine learned force fields
Nature Communications·2024· 60 citedOpen Access
From Peptides to Nanostructures: A Euclidean Transformer for Fast and Stable Machine Learned Force Fields
arXiv (Cornell University)·2023· 1 citedOpen Access
Author Correction: Efficient interatomic descriptors for accurate machine learning force fields of extended molecules
Nature Communications·2023Open Access
Efficient interatomic descriptors for accurate machine learning force fields of extended molecules
Nature Communications·2023· 37 citedOpen Access
Reconstructing Kernel-Based Machine Learning Force Fields with Superlinear Convergence
Journal of Chemical Theory and Computation·2023· 2 citedOpen Access
Accurate global machine learning force fields for molecules with hundreds of atoms
Science Advances·2023· 154 citedOpen Access
Reconstructing Kernel-based Machine Learning Force Fields with Super-linear Convergence
arXiv (Cornell University)·2022Open Access
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Frequent Co-authors

Alexandre Tkatchenko(36), Klaus‐Robert Müller(34), Huziel E. Sauceda(21), Igor Poltavsky(13), Adil Kabylda(13), Oliver T. Unke(12), J. Thorben Frank(12), Kristof T. Schütt(9), Valentín Vassilev-Galindo(6), O. Anatole von Lilienfeld(6), Anton Charkin-Gorbulin(5), Michael Gastegger(4), Luis E. Gálvez-González(4), Lauro Oliver Paz‐Borbón(4), Sergio Suarez Dou(4), Almaz Khabibrakhmanov(4), Leonardo Medrano Sandonas(4), Klaus-Robert Müller(4), Mirela Puleva(4), Grégory Fonseca(4)