S
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 ArticleDOI
Quantum-chemical insights from deep tensor neural networks.
Kristof T. Schütt,Farhad Arbabzadah,Stefan Chmiela,Klaus R. Müller,Klaus R. Müller,Alexandre Tkatchenko,Alexandre Tkatchenko +6 more
TL;DR: In this article, a deep tensor neural network is used to predict atomic energies and local chemical potentials in molecules, reliable isomer energies, and molecules with peculiar electronic structure.
Journal ArticleDOI
Machine learning of accurate energy-conserving molecular force fields
Stefan Chmiela,Alexandre Tkatchenko,Alexandre Tkatchenko,Huziel E. Sauceda,Igor Poltavsky,Kristof T. Schütt,Klaus-Robert Müller,Klaus-Robert Müller,Klaus-Robert Müller +8 more
TL;DR: The GDML approach enables quantitative molecular dynamics simulations for molecules at a fraction of cost of explicit AIMD calculations, thereby allowing the construction of efficient force fields with the accuracy and transferability of high-level ab initio methods.
Journal Article
Quantum-Chemical Insights from Deep Tensor Neural Networks
Kristof T. Sch "utt,Farhad Arbabzadah,Stefan Chmiela,Klaus-Robert M "uller,Alexandre Tkatchenko +4 more
TL;DR: An efficient deep learning approach is developed that enables spatially and chemically resolved insights into quantum-mechanical observables of molecular systems, and unifies concepts from many-body Hamiltonians with purpose-designed deep tensor neural networks, which leads to size-extensive and uniformly accurate chemical space predictions.
Journal ArticleDOI
Towards exact molecular dynamics simulations with machine-learned force fields.
Stefan Chmiela,Huziel E. Sauceda,Klaus-Robert Müller,Klaus-Robert Müller,Klaus-Robert Müller,Alexandre Tkatchenko +5 more
TL;DR: A flexible machine-learning force-field with high-level accuracy for molecular dynamics simulations is developed, for flexible molecules with up to a few dozen atoms and insights into the dynamical behavior of these molecules are provided.
Journal ArticleDOI
Machine Learning Force Fields
Oliver T. Unke,Stefan Chmiela,Huziel E. Sauceda,Michael Gastegger,Igor Poltavsky,Kristof T. Schütt,Alexandre Tkatchenko,Klaus-Robert Müller +7 more
TL;DR: In this article, the authors present an overview of applications of ML-based force fields and the chemical insights that can be obtained from them, and a step-by-step guide for constructing and testing them from scratch is given.