About ↑
NaviCat is a platform that collects tools and databases for digital catalyst optimization and discovery developed at the LCMD in EPFL. This hub is currently under construction, hence some of the tools listed may be in development or not currently public yet.
Packages ↑
- NaviCatGA, a Genetic Algorithm for catalyst optimization.
- volcanic, an automated tool for volcano plot and activity map building.
- mikimo, an automated tool for microkinetic volcano plot building.
- marc, an automated tool to select representative conformers from a large ensemble.
- EPSim, a tool to calculate the distance to Sabatier's ideal catalyst.
- MORESIM, a module to perform replica exchange simulations.
- LKR, a step-by-step demonstration of local kernel ridge regression for machine learning applications.
- b2r2, a reaction-based representation for machine learning applications.
- Reaction representation of organocatalysts, a step-by-step demonstration of how to generate reaction representations for the prediction of selectivity in enantioselective organocatalysis. Includes a database of 754 activation energies.
Databases ↑
- Data mining the C-C cross-coupling genome, a database of C-C cross-coupling transition metal catalysts and their predicted performance for different reactions.
- OSCAR, an extensive repository of chemically and functionally diverse organocatalysts. Contains thousands of organocatalysts with relevant properties, and can be visualized using Chemiscope.
- Hydroform-22-TS, a database of 2350 structures of intermediates before and after the alkene insertion transition state in the catalytic cycle of olefin hydroformylation and the corresponding energies.
Examples ↑
Will be added as more tools are added to the project.
References ↑
- R. Laplaza, S. Gallarati, and C. Corminboeuf, “Genetic Optimization of Homogeneous Catalysts”, Chem.- Methods. 2, e202100107 (2022)
- P. van Gerwen, A. Fabrizio, M. D. Wodrich, and C. Corminboeuf, "Physics-based representations for machine learning properties of chemical reactions", Mach. Learn.: Sci. Technol. 3, 045005 (2022)
- R. Fabregat, P. van Gerwen, M. Heberle, F. Eisenbrand, and C. Corminboeuf, "Metric learning for kernel ridge regression: assessment of molecular similarity", Mach. Learn.: Sci. Technol. 3, 035015 (2022)
- R. Laplaza, S. Das, M. D. Wodrich, and C. Corminboeuf, "Constructing and interpreting volcano plots and activity maps to navigate homogeneous catalyst landscapes", Nat. Protoc. 17, 2550–2569 (2022)
- S. Gallarati, R. Laplaza, and C. Corminboeuf, "Harvesting the fragment-based nature of bifunctional organocatalysts to enhance their activity", Org. Chem. Front. 9, 4041-4051 (2022)
- R. Laplaza, J. G. Sobez, M. D. Wodrich, M. Reiher, and C. Corminboeuf, "The (not so) simple prediction of enantioselectivity – a pipeline for high-fidelity computations", Chem. Sci. 13, 6858-6864 (2022)
- M. D. Wodrich, M. Chang, S. Gallarati, Ł. Woźniak, N. Cramer, and C. Corminboeuf, "Mapping Catalyst–Solvent Interplay in Competing Carboamination/Cyclopropanation Reactions", Chem. Eur. J. 28, e202200399 (2022)
- S. Das, R. Laplaza, J. T. Blaskovits, C. Corminboeuf, "Mapping Active Site Geometry to Activity in Immobilized Frustrated Lewis Pair Catalysts", Angew. Chem. Int. Ed. 61, e202202727 (2022)
- S. Gallarati, R. Fabregat, R. Laplaza, S. Bhattacharjee, M. D. Wodrich, and C. Corminboeuf, "Reaction-based machine learning representations for predicting the enantioselectivity of organocatalysts", Chem. Sci. 12, 6879-6889 (2021)
- T. Worakul, R. Laplaza, S. Das, M. D. Wodrich, C. Corminboeuf, "Microkinetic Molecular Volcano Plots for Enhanced Catalyst Selectivity and Activity Predictions", ACS Catal. 14, 9829–9839 (2024)
Acknowledgements ↑
The NaviCat project is funded by NCCR Catalysis of the Swiss National Science Foundation and by the European Research Council (ERC, Grant Agreement No. 817977) within the framework of European Union's H2020.
