Summary
Modern computational chemistry methods are helpful tools for interpreting experimental and field measurements. The proposed HYDRO-CLUSTER project uses quantum-chemical calculations to provide molecular insight into the effect of humidity on molecular cluster formation in the atmosphere. These atmospheric clusters may grow in size and form aerosols, tiny particles dispersed in the air that hugely impact climate and human health. Water vapour is known to vary the particle formation rate. However, the presence of water molecules in the initial steps of new-particle formation (NPF) is not well understood, as very few theoretical studies on this topic have been published. On one side, hydrated molecular clusters become larger in size, and thus their collision with other molecules is more probable. On the other side, the cluster stability can be either lower or higher, complicating the fragmentation/evaporation of hydrated clusters. Current theoretical models can be several orders of magnitude wrong in predicting NPF rates as they do not account for the effect of water properly. In this project, configurational sampling of micro-hydrated acid-base clusters will be performed to reveal molecular insight into their thermodynamic stability. Machine-learning models for quantum systems will enhance both configurational sampling and molecular dynamics simulations, allowing us for the first time to reveal the role of water in cluster formation. Finally, we will model the effect of humidity on the atmospheric NPF and suggest a methodology for improving global climate models.
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| Web resources: | https://cordis.europa.eu/project/id/101105506 |
| Start date: | 01-06-2023 |
| End date: | 31-05-2025 |
| Total budget - Public funding: | - 214 934,00 Euro |
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Original description
Modern computational chemistry methods are helpful tools for interpreting experimental and field measurements. The proposed HYDRO-CLUSTER project uses quantum-chemical calculations to provide molecular insight into the effect of humidity on molecular cluster formation in the atmosphere. These atmospheric clusters may grow in size and form aerosols, tiny particles dispersed in the air that hugely impact climate and human health. Water vapour is known to vary the particle formation rate. However, the presence of water molecules in the initial steps of new-particle formation (NPF) is not well understood, as very few theoretical studies on this topic have been published. On one side, hydrated molecular clusters become larger in size, and thus their collision with other molecules is more probable. On the other side, the cluster stability can be either lower or higher, complicating the fragmentation/evaporation of hydrated clusters. Current theoretical models can be several orders of magnitude wrong in predicting NPF rates as they do not account for the effect of water properly. In this project, configurational sampling of micro-hydrated acid-base clusters will be performed to reveal molecular insight into their thermodynamic stability. Machine-learning models for quantum systems will enhance both configurational sampling and molecular dynamics simulations, allowing us for the first time to reveal the role of water in cluster formation. Finally, we will model the effect of humidity on the atmospheric NPF and suggest a methodology for improving global climate models.Status
SIGNEDCall topic
HORIZON-MSCA-2022-PF-01-01Update Date
31-07-2023
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