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Stefan Chmiela
Researcher at Technical University of Berlin
Publications - 40
Citations - 5478
Stefan Chmiela is an academic researcher from Technical University of Berlin. The author has contributed to research in topics: Molecular dynamics & Deep learning. The author has an hindex of 16, co-authored 33 publications receiving 3340 citations.
Papers
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Journal Article
Molecular Force Fields with Gradient-Domain Machine Learning: Dynamics of Small Molecules with Coupled Cluster Forces
Journal Article
SchNet - A Deep Learning Architecture for Molecules and Materials
Kristof T. Schütt,Huziel E. Sauceda,Pieter-Jan Kindermans,Stefan Chmiela,Klaus-Robert Müller,Alexandre Tkatchenko +5 more
Journal ArticleDOI
Author Correction: Efficient interatomic descriptors for accurate machine learning force fields of extended molecules
White Paper: “Advancing Quantum Mechanics with Mathematics and Statistics” (IPAM Long Program, Spring 2022)
Mihail Bogojeski,Eric Cancès,Stefan Chmiela,Brian G. Ernst,Fabian M. Faulstich,Carlos,García-Cervera,Szabolcs Góger,Matteo Gori,Muhammad Hassan,Scott Jensen,Almaz,Khabibrakhmanov,Philip Kurian,Rafael Lainez Reyes,Marcel F. Langer,Remi Leano,Kangbo Li,Lin Lin,Vincent Martinetto,Abigail N. Poteshman,Trine K. Quady,Zeno,Schätzle,Zachary Sparrow,Martin Stöhr,Kevin D. Stubbs,Lucas Tecot,Alexandre,Tkatchenko,Guido Falk von Rudorff +30 more
TL;DR: Mihail Bogojeski, Eric Cances, Stefan Chmiela, Brian Ernst, Fabian M. Faulstich, Carlos García-Cervera, Szabolcs Góger, Matteo Gori, Muhammad Hassan, Scott Jensen, Almaz Khabibrakhmanov, Philip Kurian, Rafael Lainez Reyes, Marcel Langer, Remi Leano, Kangbo Li, Lin Lin, Vincent Martinetto, Abigail Poteshman, Trine Kay Quady, Zeno Schätzle, Zachary Sparrow, Martin Stöhr, Kevin D Stubbs, Lucas Tecot, Alexandre Tkatchenko, and Guido Falk von Rudorff
Posted Content
Detect the Interactions that Matter in Matter: Geometric Attention for Many-Body Systems.
Thorben Frank,Stefan Chmiela +1 more
TL;DR: In this article, the authors propose a variant to describe geometric relations for arbitrary atomic configurations in Euclidean space that also respects all relevant physical symmetries, and demonstrate how the successive application of their learned attention matrices effectively translates the molecular geometry into a set of individual atomic contributions on-the-fly.