Andre Niyongabo Rubungo

New tool narrows the search for ideal metal organic frameworks Princeton researchers have developed a new tool to speed the discovery of advanced materials known as metal organic frameworks (MOFs).

A new machine learning tool rapidly predicts the stability and synthesizability of metal organic frameworks, streamlining the search for materials suited to applications like carbon capture and energy storage. doi.org/hbh4wk

Highly Accurate and Fast Prediction of MOF Free Energy via Machine Learning http://dx.doi.org/10.1021/jacs.5c13960

Highly Accurate and Fast Prediction of MOF Free Energy via Machine Learning A major bottleneck in translating computational screening of metal–organic frameworks (MOFs) to laboratory synthesis is the uncertain synthetic accessibility of computer-generated MOF designs. While M...

This seems cool pubs.acs.org/doi/10.1021/...

🚨 #Benchmark paper alert! 🚨

@andreniyongabo.bsky.social, Kangming Li, @adjiboussodieng.bsky.social and @jae3goals.bsky.social pave the way for #benchmarking #LLMs for materials property prediction with their LLM4Mat-Bench #MachineLearningScienceandTechnology
Read the paper here: bit.ly/43nxfnW

I'm excited to share that I’m starting a new position as a Research Scientist Intern at IBM Research AI, where I will spend the summer working on Large-scale Language Modeling! Looking forward to this exciting journey 🙏

#NewPaper We revisit Shannon's mutual information, highlight its shortcomings, and propose a novel alternative to it, called the Vendi Information Gain (VIG), using the Vendi Score. #InformationTheory #Diversity
By: Quan Nguyen & @adjiboussodieng.bsky.social
Link to paper: arxiv.org/abs/2505.09007

Thank you so much🙏 @piermorgante.bsky.social

I couldn't think of a greater way to start the week: "LLM4Mat-Bench: Benchmarking Large Language Models for Materials Property Prediction" (@adjiboussodieng.bsky.social and @jae3goals.bsky.social) is out in #MachineLearningScienceandTechnology 🤩 #EditorsLife
iopscience.iop.org/article/10.1...

Probing out-of-distribution generalization in machine learning for materials - Communications Materials State-of-the-art machine learning models are often tested on their ability to generalize materials deemed ’dissimilar’ to training data, but such definitions frequently rely on heuristics. Here, an an...

Our paper probing out-of-distribution generalization in machine learning for materials discovery is now published at Communications Materials (@NaturePortfolio) #MatSci #AI4Science #Chemsky #Vertaix

📓Link: nature.com/articles/s43...

Thank you for stopping by our poster and for the great chat! @kylecranmer.bsky.social

AI4Mat-NeurIPS 2024 About the Workshop

One of the awesome things about the AI4Mat workshop at NeurIPS 2024 (sites.google.com/view/ai4mat) is that it brings together people doing things like graph neural networks and large language models with others into electron microscopy and materials synthesis #ALDep

Our
@andreniyongabo.bsky.social will be @neuripsconf.bsky.social presenting LLM4Mat-Bench at the AI4Mat Workshop ✨ LLM4Mat-Bench is the largest text benchmark for training LLMs for materials property prediction. #NLP4Science

🖇️Paper: arxiv.org/abs/2411.00177
💻Code: github.com/vertaix/LLM4...

Thanks!

We are hiring (resharing appreciated)!

Given recent successful grant applications (I got my SNSF Starting Grant 🚀), we are extending the LIAC team with multiple openings (PhD/postdoc) for 2025.

Apply now (deadline: December 20th) by filling in this form: forms.fillout.com/t/eq5ADAw3kkus.
#ChemSky

Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Here, we present the outcomes from the second Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry, which engaged participants across global hybrid locations, resul...

Exciting news! 🔥🤩
The second LLM Hackathon for Applications in Materials Science and Chemistry has resulted in a preprint! 🔥🚀

Check out the paper here arxiv.org/abs/2411.15221 and a short version of all the projects in this Twitter thread twitter.com/BenBlaiszik/...

#AI #AI4Materials #AI4Chem

andreniyongabo.bsky.social

Reposting two starter packs with researchers working on AI4Science for those who are newly joining 🦋.

So many wonderful folks on these lists, help me add more by sending names (including your own 😊).

Starter Pack 2: go.bsky.app/GnFTUM6
Starter Pack 1: go.bsky.app/JeFdryY

Meet my sister, the winner of UESTC's Most Outstanding PhD Student Award! Her PhD focuses on fuzzy-inspired frameworks for medical diagnosis and signal processing, tackling challenging Neuroscience problems. She’s now seeking a post-doc position in a related field!
scholar.google.com/citations?us...

Sergei Kalinin on LinkedIn: GitHub - SergeiVKalinin/MSE_Fall2024 🎓 Reflecting on "Introduction to Machine Learning for Materials Science"🎓 As the semester comes to a close, it’s a great time to look back on the second…

Intro to Machine Learning for Materials Science: from ontologies and NLP to causality and decision making

www.linkedin.com/posts/sergei...

A very cool article about our ChemBench benchmark (arxiv.org/abs/2404.01475).

Have you been wondering how your favorite LLM, e.g. Llama, Mistral, or Gemma performs on materials property prediction? We have just released LLM4Mat-Bench, the largest benchmark for materials property prediction with LLMs! #ChemSky

Paper: arxiv.org/abs/2411.00177
Code: github.com/vertaix/LLM4...

GitHub - IBM/materials: Foundation Model for Materials - FM4M Foundation Model for Materials - FM4M. Contribute to IBM/materials development by creating an account on GitHub.

If you're interested in foundation models for materials and molecules, check out our repo: github.com/IBM/materials

We have three models released based on SMILES, SELFIES and molecular graphs.

More to come shortly - we aim to have a unified collection of state-of art models across all modalities.