MICADO - Microdroplet catalysis of CO2-amine C-N bond formation, from machine learning-based si[...]

MICADO
- Microdroplet catalysis of CO2-amine C-N bond formation, from machine learning-based simulations to predictive control

18/08/2026 Financement de l'Union européenne

MICADO
- Microdroplet catalysis of CO2-amine C-N bond formation, from machine learning-based simulations to predictive control

Theoretical chemistry, Machine learning, Raman spectroscopy, Microdroplets, CO2 transformation

PRISM programme

The PRISM (PhD Research Programme for International Training in Sustainable Soft Matter) programme has launched its first call for applications, offering up to 14 fully funded PhD fellowships starting from 1 March 2027 at Paris Sciences & Lettres (PSL) University. The programme trains researchers to address ecological transition challenges through sustainable soft matter science, with projects focused on eco-friendly chemical processes, circular economy, renewable energies, and carbon capture, storage, and valorisation. Co-funded by the European Union under Horizon Europe MSCA COFUND (Grant Agreement 101261637) and partner institutions, PRISM provides interdisciplinary, international, and intersectoral training, including mobility opportunities, secondments, and courses in sustainability, innovation, entrepreneurship, career development, and transferable skills.

The PhD project

Carbon–nitrogen (C–N) bond formation from CO₂ and ammonia or amines is central to carbon capture technologies, atmospheric chemistry, and potentially prebiotic chemistry. A landmark recent study by our experimental partner at ETH Zürich (Signorell group) demonstrated that aqueous microdroplets promote the spontaneous formation of urea from CO₂ and ammonia under ambient conditions, a reaction that does not proceed in bulk solution. This striking microdroplet catalysis reveals reactivity fundamentally different from that of homogeneous solution, yet its molecular origin remains unknown. No predictive framework currently exists to describe or control such chemistry in confined aqueous environments.

The MICADO project aims to uncover the molecular mechanisms underlying microdroplet catalysis of CO₂–amine C–N bond formation and to build the first predictive, multiscale model linking droplet physicochemical properties to reaction rates and product distributions. To achieve this, the ENS team will develop machine-learning interatomic potentials (MLIPs) trained at hybrid-DFT accuracy using active learning protocols, building on the group’s ArcaNN automated training framework and state-of-the-art foundation models such as MACE-OMOL. These MLIPs will enable large-scale reactive molecular dynamics simulations with quantum accuracy and extensive statistical sampling, which are inaccessible to conventional ab initio approaches.

Reaction mechanisms will be explored using MLIP-based molecular dynamics combined with transition path sampling, enabling unbiased identification of competing pathways (sequential vs. concerted amidation, partial vs. full amidation to urea), free‑energy profiles, and the key catalytic factors, including interfacial electric fields, local acidity, confinement, and reactant enrichment. Simulations will be performed in bulk solution, at the air–water interface, and in droplets of varying sizes. The molecular-level information will be integrated into mesoscale reaction–diffusion models to connect atomistic mechanisms to droplet-scale observables such as reaction kinetics and product distributions.

The project follows a strict theory–experiment synergy: controlled single-droplet measurements from the Signorell group at ETH Zürich (covering reaction kinetics, product identification, and systematic variation of droplet conditions) will serve as benchmarks to validate and refine the models. Discrepancies will guide targeted improvements of the ML potentials and identification of missing physical ingredients. This iterative loop between simulation and experiment is essential to establish a truly predictive framework. Beyond the CO₂–NH₃ system, the project will extend to CO₂–amine reactions in the presence of atmospherically relevant organic acids, providing design principles for interfacial catalysis more broadly. The methodology, combining foundation-model fine-tuning, active learning, and automated mechanism discovery, will be transferable to other chemically reactive systems. The project will demonstrate how AI-based molecular simulation can transform the understanding and prediction of complex interfacial reactivity, with implications for carbon capture, atmospheric chemistry, and prebiotic processes.

INTERNATIONAL: The project is built around a close collaboration with the group of Prof. Ruth Signorell at ETH Zürich, one of the world’s leading laboratories in the physical chemistry of individual aerosol particles and droplets. The Signorell group recently reported the spontaneous formation of urea from CO₂ and ammonia in aque