@efssh

Computational Chemistry and Machine Learning | previously: @SchwallerGroup @EPFL | @BASF | @TheorHetCatICIQ | scientific Illustrations when time allows

 Lucerne, Switzerland
scholar.google.com/citations?user=qcIwmagAAAAJ&hl=en
Joined February 2019
  • Tweets145
  • Following316
  • Followers316
24Media
Edvin Fako retweeted
Kostas Vogiatzis@VogLabFeb 23
We are very excited to share our new perspective article on AI for chemical reactivity and catalysis, available now in JACS! This is the outcome of a wonderful discussion with experts in this field #Compchem #MachineLearning #RealTimeChem #Cheminformatics
J. Am. Chem. Soc.@J_A_C_SFeb 21
Boosting Computational Catalysis and Chemical Reactivity with Artificial Intelligence | Journal of the American Chemical Society @EPFL @ETH @ETH_DCHAB @UTKnoxvilleNews @UTKnoxville pubs.acs.org/doi/10.1021/jac…

Boosting Computational Catalysis and Chemical Reactivity with Artificial Intelligence

Abstract. Artificial intelligence (AI) and machine learning (ML) are rapidly reshaping the landscape of computational chemistry, offering new opportunities

pubs.acs.org
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Edvin Fako retweeted
J. Am. Chem. Soc.@J_A_C_SFeb 21
Boosting Computational Catalysis and Chemical Reactivity with Artificial Intelligence | Journal of the American Chemical Society @EPFL @ETH @ETH_DCHAB @UTKnoxvilleNews @UTKnoxville pubs.acs.org/doi/10.1021/jac…

Boosting Computational Catalysis and Chemical Reactivity with Artificial Intelligence

Abstract. Artificial intelligence (AI) and machine learning (ML) are rapidly reshaping the landscape of computational chemistry, offering new opportunities

pubs.acs.org
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Thrilled to share our new work MatInvent, a general and efficient reinforcement learning workflow that optimizes diffusion models for goal-directed crystal generation. Thanks to @JeffGuo__ , @efssh , @pschwllr , @SchwallerGroup , @NCCR_Catalysis . arxiv.org/abs/2511.03112

Accelerating inverse materials design using generative diffusion...

Diffusion models promise to accelerate material design by directly generating novel structures with desired properties, but existing approaches typically require expensive and substantial labeled...

arxiv.org
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Exciting postdoc opportunity in the @SchwallerGroup at EPFL! We're hiring a postdoc to advance ML-driven synthesis planning after Zlatko Joncev’s successful exit to co-found B-12 (YC '25) 🚀 Work on: - LLMs for strategic synthesis planning - Chemical reasoning at scale - Building the next-gen framework for retrosynthesis Our recent preprint shows that LLMs can guide synthesis planning with natural-language strategies — combining AI reasoning with traditional chemical tools (arxiv.org/abs/2503.08537). Join us at the intersection of chemistry & AI. Up to 2 years. Based in Lausanne 🇨🇭 Apply: forms.fillout.com/t/nnxVE3Rc… #ChemTwitter #MachineLearning #SynthesisPlanning #PostdocPosition
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Which foundation MLIP is best suited for your application? The MLIPX framework can help you answering this question. Checkout the Paper iopscience.iop.org/article/1… and github.com/basf/mlipx #MLIPX

MLIPX: machine-learned interatomic potential eXploration

MLIPX: machine-learned interatomic potential eXploration, Zills, Fabian, Agarwal, Sheena, Goncalves, Tiago J, Gupta, Srishti, Fako, Edvin, Han, Shuang, Britta Mueller, Imke, Holm, Christian, De,...

iopscience.iop.org
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Check out the fantastic entries, adsKRK and DynoAgent, from the @SchwallerGroup's LLMs for Materials and Chemistry Applications Hackathon team! From heterogeneous catalysis to proteins - MD simulations made simple and explainable 🚀 @efssh @XuanVuNguyen18 @6ojaHa Ryo, Salomé, Cassandra, and Jeremy. Proud of you! Thanks for the global organisation, @BenBlaiszik! #LLMHackathon #AIMat #AIChem
Xuan-Vu Nguyen@XuanVuNguyen1812 Sep 2025
You don’t like molecular dynamics? We get it. That’s why at this year’s LLM hackathon for Chemistry and Materials Science, we built not one, but ✨two✨ AI agents for molecular dynamics 👇
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🚨 PhD & Postdoc positions in Computational Chemistry 🚨 Funded by SNSF + ERC Starting Grant in my group @unifrChemistry (Fribourg 🇨🇭). Focus: ML × DFT (method development & applications). Please RT 🙏 📄 Info & apply: unifr.ch/chem/en/assets/publ…
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Thrilled to present our new work on Reinforcement Learning for 3D Crystal Diffusion Generation in #AI4Mat at #ICLR2025 ! We show that RL can optimize diffusion models for goal-directed crystal generation. Thanks to @pschwllr and @SchwallerGroup ! openreview.net/forum?id=Ovxf…
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Hello from Singapore 🇸🇬! Thrilled to be at #ICLR2025 presenting our work on fragment-based drug discovery 🧩. We go beyond virtual screening with a generative, structure-aware approach. 📃 openreview.net/forum?id=bZW1… 🔗 github.com/rneeser/LatentFra… A thread 🧵👇
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Finally, would I trust an LLM with my expensive experiment? Absolutely not. ❌ Would I trust a GP? Yes. ✅ LLM optimized through a GP? Absolutely YES. 💯💯💯 We offer a principled path to using LLMs for real-world optimization — under uncertainty, under low data, under control.
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Edvin Fako retweeted
ICIQ@ICIQchem26 Jul 2024
#ICIQPhD 🎓 Moltes felicitats, Dra. @ARuizFerrando from Prof. Núria López's group 📚 Today Andrea has defended her thesis on single-atom catalysis, revealing fundamental mechanisms and potential applications in catalysis and nanotechnology. About her 🔗iciq.org/moltes-felicitats-d…
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The first time I used this model I was pretty breath taken. The coolest things: with just a few lines, **anyone** can now run decent atomic scale simulations. Try it out yourself! Details in 🧵 paper: arxiv.org/abs/2401.00096
Ilyes Batatia@IlyesBatatia2 Jan 2024
Our large collaborative effort shows the impressive range of applicability of foundational ML potentials, developed using only open-source data and software. More than 30 applications, including MOFs, catalysis, water, and more simulated with one model. arxiv.org/abs/2401.00096
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Edvin Fako retweeted
Janosh@jrib_8 Jan 2024
Great news! Following hot on the heels of the MACE-MP-0 foundation model that hit arXiv (arxiv.org/abs/2401.00096) 10 days ago, the MACE package just landed on PyPI pypi.org/project/mace-torch. Just run `pip install mace-torch` to get started!

A foundation model for atomistic materials chemistry

Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much of our...

arxiv.org
Frank Noe@FrankNoeBerlin11 Nov 2023
It’s great to see that ML forcefields slowly start to be distributed in versions and packages. The field needs this sort of consolidation and adoption to move from experimenting to production.
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Our large collaborative effort shows the impressive range of applicability of foundational ML potentials, developed using only open-source data and software. More than 30 applications, including MOFs, catalysis, water, and more simulated with one model. arxiv.org/abs/2401.00096
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Pleased to see our viewpoint on single-atom catalysts @ACSCatalysis, highlighted with this beautiful cover designed and crafted by the great @efssh🎉👇 pubs.acs.org/doi/10.1021/acs…
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What would you do with a DFT-accurate(ish) #machinelearning potential that can handle 25 transition metals? Find out how we made one, and what we did with it, in the our latest (and last for 2022) #preprint on high-entropy alloys and alchemical compression arxiv.org/abs/2212.13254
A "data-driven periodic table" of the transition metals obtained by training a machine-learning potential. The figure shows the d-block transition metal symbols arranged in a half-cylinder pattern, with the period as the vertical axis and the groups along the circumference

ALT A "data-driven periodic table" of the transition metals obtained by training a machine-learning potential. The figure shows the d-block transition metal symbols arranged in a half-cylinder pattern, with the period as the vertical axis and the groups along the circumference

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So the post-doc position is now open for @oh2pera project. Simulations of perovskites. Please RT @GEFES_RSEF @Geen_Rseq @LatinXChem Deadline 30/11 iciq.org/jobs-grants/positio…
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I will be having a post-doc position in the @oh2pera project.
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I have an open #compchem postdoc position to simulate ionic liquids with molecular dynamics. RT appreciated! Check link for details: usc.es/ciqus/en/empleo/postd…
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What is the optimal nanostructure to propel catalytic reactions and what is the corresponding descriptor? See our recent study on metal-catalyzed acetylene hydrochlorination @NatureNano @TheorHetCatICIQ @efssh @SelinaKKaiser1 @catalysis_eth 👇 nature.com/articles/s41565-0…
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