https://www.biorxiv.org/content/10.1101/2024.02.05.578959v1

babe wake up leetcode for ML just dropped

https://www.deep-ml.com/

(жду когда добавят побольше задачек ?)

https://github.com/lamalab-org/MatText

GitHub

GitHub - lamalab-org/MatText: Text-based modeling of materials.

Text-based modeling of materials. Contribute to lamalab-org/MatText development by creating an account on GitHub.

ONLINE BOOK
—-
https://github.com/weisscharlesj/SciCompforChemists?tab=readme-ov-file

GitHub

GitHub - weisscharlesj/SciCompforChemists: Scientific Computing for Chemists text for teaching basic computing skills to chemistry…

Scientific Computing for Chemists text for teaching basic computing skills to chemistry students using Python, Jupyter notebooks, and the SciPy stack. This text makes use of a variety of packages i...

[Verse]
In a world of circuits and screens
We push the boundaries of what's real
Computational chemistry, the ultimate dream
Bringing science to a whole new deal (woah-oh-oh)

[Verse 2]
Robots in lab coats, precision at hand
AI algorithms, guiding our command
Data mining, simulations so grand
Unlocking mysteries, we'll understand (yeah-yeah)

[Chorus]
Welcome to the future, where molecules collide
In virtual realms, where discoveries reside
No beakers, no test tubes, just code and design
In the realm of computation, we redefine

Ученые на месте?

Нашел интересный проект с ИИшкой для науки

https://www.air4.science/

https://github.com/divelab/AIRS

Внутри подразделы с реализациями работ по следующим направлениям

- OpenQM: AI for Quantum Mechanics
- OpenDFT: AI for Density Functional Theory
- OpenMol: AI for Small Molecules
- OpenProt: AI for Protein Science
- OpenMat: AI for Materials Science
- OpenMI: AI for Molecular Interactions
- OpenPDE: AI for Partial Differential Equations

Интересующимся рекомендую посмотреть, мб что полезное увидите

Навальный был крутым и сильным, и люди которые его поддерживают - крутые и сильные.

​​Graph & Geometric ML in 2024: Where We Are and What’s Next

? Two new blog posts - a comprehensive review of Graph and Geometric ML in 2023 with predictions for 2024. Together with Michael Bronstein, we asked 30 academic and industrial experts about the most important things happened in their areas and open challenges to be solved.

1️⃣ Part I: https://towardsdatascience.com/graph-geometric-ml-in-2024-where-we-are-and-whats-next-part-i-theory-architectures-3af5d38376e1

2️⃣ Part II: https://medium.com/towards-data-science/graph-geometric-ml-in-2024-where-we-are-and-whats-next-part-ii-applications-1ed786f7bf63

Part I covers: theory of GNNs, new and exotic message passing, going beyong graphs (with Topology, Geometric Algebras, and PDEs), robustness, graph transformers, new datasets, community events, and, of course, top memes of 2023 (that’s what you are here for, right).

Part II covers applications in structural biology, materials science, Molecular Dynamics and ML potentials, geometric generative models on manifolds, Very Large Graphs, algorithmic reasoning, knowledge graph reasoning, LLMs + Graphs, cool GNN applications, and The Geometric Wall Street Bulletin ?

New things this year:

- the industrial perspective on important problems in structural biology that are often overlooked by researchers;
- The Geometric Wall Street Bulletin prepared with Nathan Benaich, the author of the State of AI report

It was a huge community effort and we are very grateful to all our experts for their availability around winter holidays. Here is the slide with all the contributors, the best “thank you” would be to follow all of them on Twitter!