FAST Group

Protons Accumulate at the Graphene–Water Interface Water’s ability to autoionize into hydroxide and hydronium ions profoundly influences surface properties, rendering interfaces either basic or acidic. While it is well-established that protons show an affinity to the air–water interface, a critical knowledge gap exists in technologically relevant surfaces like the graphene–water interface. Here we use machine learning-based simulations with first-principles accuracy to unravel the behavior of hydroxide and hydronium ions at the graphene–water interface. Our findings reveal that protons accumulate at the graphene–water interface, with the hydronium ion predominantly residing in the first contact layer of water. In contrast, the hydroxide ion exhibits a bimodal distribution, found both near the surface and further away from it. Analysis of the underlying electronic structure reveals local polarization effects, resulting in counterintuitive charge rearrangement. Proton propensity to the graphene–water interface challenges the interpretation of surface experiments and is expected to have far-reaching consequences for ion conductivity, interfacial reactivity, and proton-mediated processes.

Excited to see our work “Protons Accumulate at the Graphene−Water Interface” now published in ACS Nano! 🎉

Using ML-driven MD simulations, we uncover why hydronium prefers the graphene–water interface while hydroxide does not. 💧⚡🔬

🔗 doi.org/10.1021/acsn...

29.04.2025 09:13 — 👍 1    🔁 2    💬 0    📌 1

FAST on the computers AND on the running track. A big well done to the group members who participated in the Cambridge half marathon on Sunday 🏃‍♂️🏃‍♀️

11.03.2025 09:44 — 👍 6    🔁 1    💬 0    📌 0

Xavier R. Advincula ‪University of Cambridge‬ - ‪‪Cited by 194‬‬ - ‪Confined Water‬ - ‪Hydrogen Bonding‬ - ‪Machine Learning Potentials‬ - ‪Nucleation‬

Check out some of Xavi's work here:
scholar.google.com/citations?us...

08.01.2025 14:24 — 👍 1    🔁 0    💬 0    📌 0

Niamh O'Neill ‪PhD student, University of Cambridge‬ - ‪‪Cited by 169‬‬ - ‪Machine Learning Potentials‬ - ‪Water‬ - ‪Electrolytes‬

Check out some of Niamh's work here:
scholar.google.com/citations?us...

08.01.2025 14:22 — 👍 2    🔁 0    💬 0    📌 0

Happy 2025!

Did you ever think whilst eating your Christmas dinner how salt dissolves? @niamhoneill.bsky.social did.

Name: Niamh
Likes: Running (jobs) 🏃‍♀️💻
Dislikes: Deionised Water 🧂❌
Most likely to: tell you all about Ireland ☘️

08.01.2025 14:19 — 👍 3    🔁 1    💬 1    📌 0

Fantastic Work!

08.01.2025 12:04 — 👍 0    🔁 0    💬 0    📌 0

For our first ‘Meet the FAST Group’ post …

Name: Xavi
Role: Junior PhD Student
Likes: Football and Nanoconfinement
Dislikes: PBE with no D3
Most likely to: debug your life

11.12.2024 15:39 — 👍 7    🔁 0    💬 1    📌 0

There are already many excellent expert reviews on MLPs, so why adding more to the mix?

We want to give a light intro into the field for new starters going from the historical developments to latest developments on going beyond locality to foundation models.

dx.doi.org/10.1088/1361-648X/ad9657

06.12.2024 16:33 — 👍 5    🔁 2    💬 1    📌 0

Toy model of atoms.

Hello Bluesky.

This is our new group account for the FAST group, hosted at the Cavendish Laboratory in Cambridge. We are fascinated by using computational tools to understand challenging materials and systems at the atomistic level.

Watch this space to learn more about our work 🚀

04.12.2024 17:32 — 👍 4    🔁 0    💬 1    📌 0

Nice work Christoph 👍

04.12.2024 14:25 — 👍 0    🔁 0    💬 0    📌 0