Stefan Gugler

Berliner Institut für Sozialforschung — Germany

Computer Science · Physical Sciences

7h-index353citations22works1.42yr avg
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Research Topics

Computational Drug Discovery Methods(13), Machine Learning in Materials Science(12), Crystallography and molecular interactions(5), Scientific Computing and Data Management(3), Protein Structure and Dynamics(2)

Publications22 total

Towards Symbolic XAI – Explanation Through Human Understandable Logical Relationships Between Features
2025
Gradient-Guided Furthest Point Sampling for Robust Training Set Selection
arXiv (Cornell University)·2025Open Access
Molecular Similarity in Machine Learning of Energies in Chemical Reaction Networks
arXiv (Cornell University)·2025Open Access
How simple can you go? An off-the-shelf transformer approach to molecular dynamics
arXiv (Cornell University)·2025Open Access
Disentangling Total-Variance and Signal-to-Noise-Ratio Improves Diffusion Models
arXiv (Cornell University)·2025Open Access
Towards symbolic XAI — explanation through human understandable logical relationships between features
Information Fusion·2025· 6 citedOpen Access
Towards Symbolic XAI -- Explanation Through Human Understandable Logical Relationships Between Features
arXiv (Cornell University)·2024· 2 citedOpen Access
Molecular relaxation by reverse diffusion with time step prediction
Machine Learning Science and Technology·2024· 5 citedOpen Access
Towards Symbolic Xai -- Explanation Through Human Understandable Logical Relationships between Features
SSRN Electronic Journal·2024Open Access
Ligand additivity relationships enable efficient exploration of transition metal chemical space
The Journal of Chemical Physics·2022· 23 citedOpen Access
Data set for Ligand additivity relationships enable efficient exploration of transition metal chemical space
Zenodo (CERN European Organization for Nuclear Research)·2022· 1 citedOpen Access
Data set for Ligand additivity relationships enable efficient exploration of transition metal chemical space
Zenodo (CERN European Organization for Nuclear Research)·2022Open Access
Quantum Chemical Roots of Machine-Learning Molecular Similarity Descriptors
Journal of Chemical Theory and Computation·2022· 18 cited
Ligand additivity relationships enable efficient exploration of transition metal chemical space
arXiv (Cornell University)·2022Open Access
Quantum chemical roots of machine-learning molecular similarity descriptors
arXiv (Cornell University)·2022Open Access
Gaussian Process-Based Refinement of Dispersion Corrections
Journal of Chemical Theory and Computation·2019· 60 citedOpen Access
Enumeration of <i>de novo</i> inorganic complexes for chemical discovery and machine learning
Molecular Systems Design & Engineering·2019· 39 citedOpen Access
Enumeration of de novo Inorganic Complexes for Chemical Discovery and Machine Learning
2019· 10 citedOpen Access
Enumeration of de novo Inorganic Complexes for Chemical Discovery and Machine Learning
2019· 1 citedOpen Access
Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry
Industrial & Engineering Chemistry Research·2018· 161 citedOpen Access
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Frequent Co-authors

Jon Paul Janet(10), Heather J. Kulik(10), Aditya Nandy(7), Chenru Duan(7), Klaus‐Robert Müller(7), Shinichi Nakajima(6), Markus Reiher(4), N. Arunachalam(4), Michael G. Taylor(4), Daniel B. K. Chu(4), Thomas Schnake(4), Farnoush Rezaei Jafari(4), Jonas Lederer(4), Grégoire Montavon(4), Ralf Meyer(3), Jonas Oldenstaedt(3), Ping Xiong(2), Khaled Kahouli(2), Ping Xiong(2), Oliver T. Unke(2)