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Artificial Organic Chemistry Lab

Molecular catalysts and chemical reactions designed by artificial intelligence with the help of simulations, realized and tested utilizing automated lab protocols.

Our Team

The Pollice Research Group is committed to create an inclusive research environment where members feel safe and respected. We believe that diversity is an intrinsic and important part of the academic community and, therefore, we appreciate differences in backgrounds, experiences and perspectives, and facilitate them to help everyone in our team to reach their full potential in both scientific and non-scientific matters.

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Our Research

Our research interests rest on four main pillars. Our primary target is the design of molecular catalysts for organic reactions with the help of computers. To realize that, we combine the simulation of chemical reactions with lab automation for high-throughput experiments and artificial molecular design algorithms. This allows us to incorporate data-driven algorithms directly into our workflow enabling us to accelerate the discovery process.

Latest News

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December 12, 2024

Members
Group Afternoon Winter 2024

On December 12, the group held its biannual group afternoon. This time, we tried our hands at a game of sophisticated molecular mechanics simulations with hard spheres, bowling. Later, we enjoyed burgers and cheered to the end of a successful 2024.

See pictures

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December 13, 2024

Positions
Open PhD Position

Fully funded PhD position to work on data-driven models for mixtures of stereoselective catalysts starting in October 2025.

See position

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December 13, 2024

Positions
Open PhD Position

Fully funded PhD position to work on catalytic asymmetric hydrofunctionalization reactions starting in October 2025.

See position

Social Media

Recent Papers

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December 24, 2024

Molecular Catalysis
Case studies of dimensionality in chemical data

A. Blokhuis*, R. Pollice*

Eur. J. Org. Chem. 2024, e202400949.

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September 19, 2024

Artificial Design
Predicting hydroformylation regioselectivity from literature data via machine learning (Preview)

S. Chen, R. Pollice*

Chem Cat. 2024, 4, 101111.

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June 4, 2024

Reaction Simulation
The Fe-MAN Challenge: Ferrates–Microkinetic Assessment of Numerical Quantum Chemistry

R. Rahrt, B. Hein-Janke, K. N. Amarasinghe, M. Shafique, M. Feldt, L. Guo, J. N. Harvey, R. Pollice, K. Koszinowski*, R. A. Mata*

J. Phys. Chem. A 2024, 128, 4663.