Ruben Staub

Hokkaido University — Japan

Materials Science · Physical Sciences

6h-index147citations32works0.92yr avg
OpenAlex↗ORCID↗Google Scholar↗Lab page↗

Accepting Students?

No reports yet. Know if this professor is taking students?

Research Topics

Machine Learning in Materials Science(18), nanoparticles nucleation surface interactions(5), Computational Drug Discovery Methods(4), Spectroscopy and Quantum Chemical Studies(4), Advanced Chemical Physics Studies(3)

Publications32 total

An approach to identifying viable synthetic routes from side channels in automated reaction-path searches
2026Open Access
Predicting Enantioselectivity via Kinetic Simulations on Gigantic Reaction Path Networks
2026Open Access
An Accurate and Efficient Reaction Path Search with Iteratively Trained Neural Network Potential: Answering the Passerini Mechanism Controversy
Journal of Chemical Theory and Computation·2025· 2 citedOpen Access
Supplementary data
Zenodo (CERN European Organization for Nuclear Research)·2025Open Access
Supplementary data
Zenodo (CERN European Organization for Nuclear Research)·2025Open Access
Towards reaction vessel mimicry: Machine learning assisted automated exploration of alkene and alkyne polymerization
2025Open Access
An accurate and efficient reaction path search with iteratively trained neural network potential: Answering the Passerini mechanism controversy
2025Open Access
“Node” facilitated thermostable mechanophores for rapid self-strengthening in double network materials
Chemical Science·2025· 6 citedOpen Access
Theory-driven Discovery of Thermally Stable Mechanophore for Self-strengthening Materials
2024· 1 citedOpen Access
Chemography‐guided analysis of a reaction path network for ethylene hydrogenation with a model Wilkinson's catalyst
Molecular Informatics·2024Open Access
Chemography-guided analysis of a reaction path network for ethylene hydrogenation with a model Wilkinson’s catalyst
2024· 1 citedOpen Access
Challenges for Kinetics Predictions via Neural Network Potentials: A Wilkinson’s Catalyst Case
Molecules·2023· 15 citedOpen Access
WilkinsonAFIRdb and related
Zenodo (CERN European Organization for Nuclear Research)·2023Open Access
WilkinsonAFIRdb and related
2023Open Access
WilkinsonAFIRdb and related
Zenodo (CERN European Organization for Nuclear Research)·2023Open Access
Replacing Chemical Intuition by Machine Learning: a Mixed Design of Experiments - Reinforcement Learning Approach to the Construction of Training Sets for Model Hamiltonians
2021· 1 citedOpen Access
DockOnSurf: A Python Code for the High-Throughput Screening of Flexible Molecules Adsorbed on Surfaces
Journal of Chemical Information and Modeling·2021· 32 citedOpen Access
DockOnSurf: A Python code for the High-Throughput Screening of Flexible Molecules Adsorbed on Surfaces
2021Open Access
Designing Active Sites for Structure-Sensitive Reactions via the Generalized Coordination Number: Application to Alcohol Dehydrogenation
The Journal of Physical Chemistry C·2021· 12 citedOpen Access
DockOnSurf: A Python code for the High-Throughput Screening of Flexible Molecules Adsorbed on Surfaces
2021· 2 citedOpen Access
page 1 of 2Next →

Frequent Co-authors

Stephan N. Steinmann(15), Yu Harabuchi(7), Satoshi Maeda(7), Alexandre Varnek(7), Carine Michel(5), Carles Martí(4), Sarah Blanck(4), Paul Clabaut(4), Joachim Galiana(3), Élise Antonetti(3), Philippe Gantzer(3), Sophie Loehlé(3), Min Gao(3), Julong Jiang(2), Jian Ping Gong(2), Carine Seraphim(2), Yu Harabuchi(2), Sophie Loehlé(1), Marcella Iannuzzi(1), Rustam Z. Khaliullin(1)