Stefan Heinen

Together — United States

Materials Science · Physical Sciences

8h-index480citations27works6.22yr avg
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Research Topics

Machine Learning in Materials Science(24), Computational Drug Discovery Methods(9), Advanced Chemical Physics Studies(5), Quantum Computing Algorithms and Architecture(4), Catalysis and Oxidation Reactions(4)

Publications27 total

Nationwide Heart Failure Telemonitoring
JACC Advances·2025Open Access
Structuring telemonitoring in heart failure care in The Netherlands: design and operational protocol of a nationwide initiative
European Heart Journal - Digital Health·2025· 1 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
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
Combining Hammett $σ$ constants for $Δ$-machine learning and catalyst discovery
arXiv (Cornell University)·2024Open Access
Reducing training data needs with minimal multilevel machine learning (M3L)
Machine Learning Science and Technology·2024· 18 citedOpen Access
Autonomous data extraction from peer reviewed literature for training machine learning models of oxidation potentials
Machine Learning Science and Technology·2024· 6 citedOpen Access
Combining Hammett <i>σ</i> constants for Δ-machine learning and catalyst discovery
Digital Discovery·2024· 3 citedOpen Access
Evolutionary Monte Carlo of QM Properties in Chemical Space: Electrolyte Design
Journal of Chemical Theory and Computation·2023· 3 citedOpen Access
Reducing Training Data Needs with Minimal Multilevel Machine Learning (M3L)
arXiv (Cornell University)·2023· 1 citedOpen Access
Supplementary Information for: Reducing Training Data Needs with Minimal Multilevel Machine Learning (M3L)
Zenodo (CERN European Organization for Nuclear Research)·2023· 1 citedOpen Access
Autonomous data extraction from peer reviewed literature for training machine learning models of oxidation potentials
Zenodo (CERN European Organization for Nuclear Research)·2023Open Access
Autonomous data extraction from peer reviewed literature for training machine learning models of oxidation potentials
arXiv (Cornell University)·2023Open Access
Kernel based quantum machine learning at record rate: Many-body distribution functionals as compact representations
The Journal of Chemical Physics·2023· 19 citedOpen Access
Transition state search and geometry relaxation throughout chemical compound space with quantum machine learning
The Journal of Chemical Physics·2022· 22 citedOpen Access
Supplementary Information of Geometry Relaxation and Transition State Search with Quantum Machine Learning
Zenodo (CERN European Organization for Nuclear Research)·2022Open Access
Supplementary Information of Geometry Relaxation and Transition State Search with Quantum Machine Learning
Zenodo (CERN European Organization for Nuclear Research)·2022Open Access
Toward the design of chemical reactions: Machine learning barriers of competing mechanisms in reactant space
The Journal of Chemical Physics·2021· 78 citedOpen Access
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Frequent Co-authors

O. Anatole von Lilienfeld(24), Guido Falk von Rudorff(12), Danish Khan(10), Alastair J. A. Price(7), Marco Bragato(5), Konstantin Karandashev(4), Daniel Jose Arismendi Arrieta(4), Kersti Hermansson(4), Igor Poltavsky(4), Mirela Puleva(4), Anton Charkin-Gorbulin(4), Grégory Fonseca(4), Ilyes Batatia(4), Nicholas J. Browning(4), Stefan Chmiela(4), Mengnan Cui(4), J. Thorben Frank(4), Bing Huang(4), Silvan Käser(4), Adil Kabylda(4)