O Overview
Data-driven catalyst design in our group brings together lab and in silico automation, kinetic analysis, in situ spectroscopy, and machine learning to enable faster, broader, and less biased catalysis research and development. We develop automated workflows for sampling catalytic systems and reaction networks, extracting mechanistic insight from kinetic and spectroscopic data, and screening the catalyst space in silico. Recent advances include automated operando modeling tools, graph-based reaction exploration workflows, microfluidic platforms for reaction optimization, and ConforFormer, a geometry-first molecular representation framework for screening and similarity analysis based on conformationally aware 3D embeddings.
van Putten et al., ChemSusChem 2022, 15, e202200333 — Automation and microfluidics for the efficient, fast, and focused reaction development of asymmetric hydrogenation catalysis. https://doi.org/10.1002/cssc.202200333
ConforFormer — a geometry-first molecular representation framework for screening and similarity analysis based on conformationally aware 3D embeddings. https://doi.org/10.26434/chemrxiv-2025-x68vd-v2
Hashemi et al., J. Chem. Inf. Model. 2023, 63, 6081–6094 — HiRex: High-Throughput Reactivity Exploration for Extended Databases of Transition Metal Catalysts. https://doi.org/10.1021/acs.jcim.3c00660
P Publications
Selected directly from the master publications page; one paper may appear in multiple research areas.
2026
266
A. Kalikadien, E.A. Pidko* "Performance of Meta's Universal Model for Atoms Across the Conformational and Configurational Space of Diverse Transition-Metal Catalysts.", J. Phys. Chem. A 2026, 130, 1897 Link
263
A.A. Kolganov, S. Bougueroua, M.-P. Gaigeot, M.P. Conley, E.A. Pidko* "Graph theory-based exploration of structure and dynamics of surface organometallic catalysis", J. Catal. 2026, 453, 116521 Link
2025
262
E.A. Pidko*, N. López "Introduction to Digital Catalysis", Catal. Sci. Technol. 2025, 15, 6925 Link
259
A.V. Kalikadien, C. Valsecchi, N. J. van der Lem, L. Lefort, E.A. Pidko* "Unveiling the Impact of Ligand Configurations and Structural Fluxionality on Virtual Screening of Transition-Metal Complexes", Digital Discovery 2025, 4, 2033 Link
256
S. Finta, A. Kalikadien, E.A. Pidko* "Data-driven virtual screening of conformational ensembles of transition metal complexes", J. Chem. Theory Comput. 2025, 21, 5334 Link
251
S. Bougueroua, A. Kolganov, C. Zens, D. Barth, E.A. Pidko, M.-P. Gaigeot "Exploiting graph theory in MD simulations for extracting chemical and physical properties of materials", Phys. Chem. Chem. Phys. 2025, 27, 1298 Link PCCP 25th Anniversary
2024
248
A.V. Kalikadien, C. Valsecchi, R. van Putten, T. Maes, M. Muuronen, N. Dyubankova, L. Lefort, E.A. Pidko* "Probing Machine Learning Models Based on High-Throughput Experimentation Data for the Discovery of Asymmetric Hydrogenation Catalysts", Chem. Sci. 2024, 15, 13618 Link Front Cover
245
S. Bougueroua, Y. Aboulfath, A. Cimas, A. Hashemi, E.A. Pidko, D. Barth, M.-P. Gaigeot "Topological graphs: a review", C. R. Chim. 2024 Link
244
M.S. Baidun, A.V. Kalikadien, L. Lefort, E.A. Pidko* "Impact of Model Selection and Conformational Effects on the Descriptors for In Silico Screening Campaigns", J. Phys. Chem. C 2024, 128, 7987 Link
242
I. Yu. Chernyshov, E.A. Pidko* "MACE: Automated Assessment of Stereochemistry of Transition Metal Complexes and its Applications in Computational Catalysis", J. Chem. Theory Comput. 2024, 20, 2313 Link
240
A. V. Kalikadien, A. Mirza, A. Najl Hossaini, A. Sreenithya, E.A. Pidko* "Paving the road towards automated homogeneous catalyst design", ChemPlusChem 2024, e202300702 Link Cover Feature
2023
238
A. K. Lavrienko, I. Yu. Chernyshov, E.A. Pidko* "Machine Learning Approach for the Prediction of Eutectic Temperatures for Metal-Free Deep Eutectic Solvents", ACS Sust. Chem. Eng. 2023, 11, 15492 Link
236
A. Hashemi, S. Bougueroua, M.-P. Gaigeot, E.A. Pidko* "High-Throughput Reactivity Exploration for Extended Databases of Transition Metal Catalysts", J. Chem. Inf. Model. 2023, 63, 6081 Link
234
N. Jiscoot, E. Uslamin, E.A. Pidko* "Model-based evaluation and data requirements for parallel kinetic experimentation and data-driven reaction identification and optimization", Digital Discovery 2023, 2, 994 Link
2022
230
A. Hashemi, S. Bougueroua, M.P. Gaigeot, E.A. Pidko* "ReNeGate: Reaction Network Graph Theoretical tool for automated mechanistic studies in computational homogeneous catalysis", J. Chem. Theory Comput. 2022, 18, 7470 Link
227
R. van Putten, N.S. Eyke, L.M. Baumgartner, V.L. Schultz, G.A. Filonenko, K.F. Jensen, E.A. Pidko* "Automation and microfluidics for the efficient, fast, and focused reaction development of asymmetric hydrogenation catalysis", ChemSusChem 2022, 15, e202200333 Link
214
A. V. Kalikadien, E.A. Pidko*, V. Sinha "ChemSpaX: Exploration of chemical space by automated functionalization of molecular scaffold", Digital Discovery 2022, 1, 8 Link Front Cover
2021
208
R. van Putten, E.A. Uslamin, E.A. Pidko "Automated high-resolution sampling and multi-mode operando spectroscopy of (bio-)chemical reactions", Invention Disclosure 2021, 1, 100002 Link
2020
178
I.Yu. Chernyshov, I.V. Ananyev, E.A. Pidko* "Revisiting van der Waals radii: from comprehensive structural analysis to knowledge-based classification of interatomic contacts", ChemPhysChem 2020, 21, 370 Link Front Cover
2019
173
I. Vollmer et al. (incl. E. Pidko) "Activity descriptors derived from comparison of Mo and Fe as active metal for methane conversion to aromatics", J. Am. Chem. Soc. 2019, 141, 18814 Link