...

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.

...

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

...

February 10, 2025

Positions
Open PhD Position

Fully funded PhD position to work on machine learning-guided directed evolution for oligopeptide catalyst design starting between June 2025 and October 2025.

See position

...

February 3, 2025

Members
Fareeda joins

On February 3, Fareeda joined the group as a Master student. Fareeda will work on computational exploration of asymmetric catalysts aided by machine learning.

See bio

...

February 3, 2025

Members
Maciej joins

On February 3, Maciej joined the group as new PhD student. Maciej will work on data-driven design of organocatalyzed reactions.

See bio

Social Media

Recent Papers

...

February 5, 2025

Artificial Design
Developing Large Language Models for Quantum Chemistry Simulation Input Generation

P. F. Jacobs, R. Pollice*

Digit. Discov. 2025, In Press.

...

December 24, 2024

Molecular Catalysis
Case studies of dimensionality in chemical data

A. Blokhuis*, R. Pollice*

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

...

September 19, 2024

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

S. Chen, R. Pollice*

Chem Cat. 2024, 4, 101111.