The group is growing FAST: We have five new members this term. Very warm welcome to Ioachim Dusa, Isaac Parker, and Darren Lim who are starting their PhD’s. We are also honoured to have Yixuan Feng & Hubert Beck visiting our group for some time. Continuing our FAST tradition, we’ve convinced some of the new […]
Our latest work, led by Sam and featured on the cover of the JCP, delves into the complexities of the CO2–H2O interface using machine-learned potentials. The work, also highlighted in a Scilight article, offers new insights into interfacial tension and CO2 phase behavior, with implications for climate research and carbon capture technologies.
The question of ion-pairing in sodium chloride has puzzled scientists for decades, with no clear consensus on the relative stability of contact versus solvent-separated ion pairs due to the delicate balance of interactions involved. Published in the Journal of Physical Chemistry Letters J. Phys. Chem. Lett. 2024, 15, 23, 6081–6091, our latest research utilizes explicitly-correlated […]
To welcome Eduard who’s a visitor for three months, we got together to rescue the world by stopping an asteroid headed to earth in the Armageddon escape room at Lockhouse. Before that we had a nice group dinner.Welcome Eduard!
Understanding complex aqueous systems Water remains the most fascinating liquid in our world. It is not only the “elixir of life”, but its understanding is also crucial to tackle some of the most pressing societal challenges of our time, such as climate change, ocean accidification, and clean water supply. In our research, we follow a […]
Reaching Coupled Cluster Accuracy The potential energy surface (PES) of a system, which governs all its structural, dynamic and thermodynamic properties in the Born-Oppenheimer approximation, is currently in many cases best described by coupled cluster theory. This has granted the CCDS(T) approach the title of the “gold standard” in quantum chemistry. Thus, it is desirable […]
Learning structure-energy relations Machine learning in computational chemistry has emerged as an indispensable driving force for innovation. By combining the low cost of traditional empirical potentials with the accuracy of ab initio methods, machine learning potentials deliver long time and large length scales required for insight into complex molecular systems. Instead of physically motivated forms […]
As part of our parent ICE-group we had a great time in Venice full of insightful discussions, excellent science and exciting future potential collaborations! This trip has been the initial meeting of the n-AQUA project, jointly awarded to Angelos Michaelides, Mischa Bonn, and Lyderic Bocquet, which we have the honor to contribute to.
…with this nice video from the Royal Institution. Great to feature as scientific consultant on this topic close to our scientific passion.
Very honored to receive the first TYC Early Career Award for our work on understanding complex aqueous systems with machine learning.