Stefan Gugler
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  • Publications
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  • Publications
    • Architecture of Chinese chemical element names
    • Generative pseudo-force fields for molecular generation
    • Gradient-guided furthest point sampling for robust training set selection
    • How simple can you go? An off-the-shelf transformer approach to molecular dynamics
    • ReactionAtlas: Ab origine exploration of chemical reaction networks with machine learning
    • Enhancing diffusion models efficiency by disentangling total-variance and signal-to-noise ratio
    • Molecular similarity in machine learning of energies in chemical reaction networks
    • Physical-chemistry-inspired AI for catalyst design
    • Towards Symbolic XAI -- Explanation Through Human Understandable Logical Relationships between Features
    • Molecular relaxation by reverse diffusion with time step prediction
    • qcscine/puffin: Release 1.2. 0
    • qcscine/puffin: Release 1.2.0
    • qcscine/readuct: Release 5.0.0
    • qcscine/utilities: Release 7.0. 0
    • qcscine/utilities: Release 7.0.0
    • SCINE READUCT 5.1. 0
    • SCINE READUCT 5.1.0
    • Ligand additivity relationships enable efficient exploration of transition metal chemical space
    • Quantum chemical roots of machine-learning molecular similarity descriptors
    • Reiher
    • Reiher
    • SCINE READUCT 4.1. 0
    • qcscine/readuct: Release 3.0. 0
    • Enumeration of de novo inorganic complexes for chemical discovery and machine learning
    • Gaussian process-based refinement of dispersion corrections
    • Strategies and software for machine learning accelerated discovery in transition metal chemistry

Generative pseudo-force fields for molecular generation

Jan 1, 2026·
Stefaan S. P. Hessmann
,
Khaled Kahouli
,
Stefan Gugler
,
Michael Plainer
,
Frank Noé
,
Klaus-Robert Müller
,
Niklas W. A. Gebauer
· 0 min read
Cite URL
Type
Journal article
Publication
arXiv preprint arXiv:2605.19050
Last updated on Jan 1, 2026
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© 2026 Stefan Gugler. This work is licensed under CC BY NC ND 4.0

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