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Our latest preprint on how to model organic reactions in explicit solvents with machine learning potentials is now out in chemRxiv! chemrxiv.org/engage/chemrxi…. #compchem work led by Hanwen and Veronika Juraskova Oxford Chemistry TMCS CDT

I really enjoyed yesterday's paper from Fernanda Duarte Duarte Group, so I wrote a post trying to explain why I liked it. TLDR - proper handling of solvation remains probably the biggest open challenge in computational organic chemistry; now ML can help. corinwagen.github.io/public/blog/20…

Congratulations to all poster prize winners! Great science on display by all participants. We are particularly proud of our very own Zhang Hanwen, who presented her work on modelling solvent effects with MLPs doi.org/10.26434/chemr… TMCS CDT

PhD funding available for UK Students. Interested in machine learning (ML) for molecular modelling? This PhD project aims to develop machine learning inter-atomic potentials (MLIPs) for molecular materials and organic-inorganic interfaces. Please RT 🤗 jobs.ac.uk/job/DBM614/phd…

Congratulations, Ally, on passing your viva👨‍🎓🍾! Can't wait to celebrate with you in January. Big thanks to Syma Khalid & Scott for examining! Ally's DPhil blended biomodelling, supramolecular chemistry & ML in collaboration with Langton Research Group Qing Hagan Bayley group SBM CDT

Such a privilege to be part of a community that builds great science tools and has instantly 60 people testing it on 30+ scientific questions! 🤟🤟

Machine learning meets explicit solvents. Just read an innovative study by Zhang, Juraskova, and Duarte, published three days ago, which integrates advanced machine learning techniques with explicit solvent modeling. They developed reactive machine learning potentials that

After a long journey, extremely happy to see Zhang Hanwen and Veronika Juraskova's work on MLPs in Nature Communications! 🎉 Check this blog summarising how this training approach facilitates modelling chemical reactions in solution communities.springernature.com/posts/modeling… Oxford Chemistry #compchem

Active Learning Meets Metadynamics: Automated Workflow for Reactive Machine Learning Potentials 1. The paper introduces an automated workflow combining active learning (AL) and well-tempered metadynamics (WTMetaD) to enhance machine learning potentials (MLPs) in reaction

Happy to share our latest preprint on reactive MLPs! Oxford Chemistry #compchem Our strategy combines WTMetaD + (inherited bias) AL (WTMetaD/WTMetaD-IB AL) to train MLPs efficiently without prior knowledge of TSs or costly AIMD calculations. Feedback and comments are welcome!

1/3 Today, an anecdote shared by an invited speaker at #NeurIPS2024 left many Chinese scholars, myself included, feeling uncomfortable. As a community, I believe we should take a moment to reflect on why such remarks in public discourse can be offensive and harmful.

We are recruiting a PhD student in machine learning for photocatalysis! In this project, we will collaborate with the group of Frank Glorius Glorius Group to develop predictive tools for energy-transfer-catalyzed photocycloadditions. Reposts appreciated! jobs.ethz.ch/job/view/JOPG_…