Sharing data is critically important for accelerating discoveries & reproducibility, especially in materials science and chemistry. @DataFacility makes it easy to publish large datasets (>TB, millions of files) as citable research objects and we've been busy. We wanted to share just a few enhancements with you!

First, we have redesigned the front page and created a data publication guide to help our users.

#OpenData #MaterialsScience We also rewrote the search functionality to make it easier to find the data already published.

🚀How can we use LLMs to accelerate scientific discovery? Let's find out! This year, hundreds of people from across the globe worked together in a hackathon to BUILD groundbreaking prototypes — showing the path to breakthroughs in next generation batteries, sustainability, advanced computing, and more.

In this 💪megathread💪, we highlight the 34 incredible prototypes, built in only a day, and their potential impacts across areas of:

- Extracting and Organizing Knowledge
- Improving LLM Property Prediction
- Creation of Novel Human/Computer Interfaces
- Automating tasks and Improving Efficiency Automation
- Reducing Information Friction
- Empowering Learners
- Evaluating LLM Capabilities with Benchmarks

Let’s go! 📚It’s important that researchers have shared term definitions when discussing complex topics. Glossagen built a tool that enables automated creation of glossaries for knowledge graphs from research papers. @RadicalAI_inc 2nd prize winner

Team: Plessers, Dieter (KU Leuven), Lederbauer, Magdalena (ETH Zurich, EPFL), Schwaller, Philippe (EPFL)

Link:

https://x.com/maglederb/status/1788682695897240063

We are less than a week from the virtual hackathon for #LLM applications in materials science and chemistry. Here is some inspiration from other's applications to drive you!

🗓️ Register and start teaming today: eventbrite.com/e/llm-march-ma…

#hackathon #openscience #ML #AI @kmjablonka using fine tuned GPT-3 and GPT-4 (in-line prompting) as a model surrogate for various chemical modeling problems.

Have questions for Kevin? He will be giving a demo during the March 29th live session, and is a co-organizer.

https://twitter.com/kmjablonka/status/1635794978936066049?s=20

Great story from @Mechanophore and colleagues @UofIllinois. From skeptic to believer: the power of models. Let's take a look at some of these journeys and what we can learn.

✨What is your story?✨

#MaterialsDataMonth #academictwitter @OpenAcademics
doi.org/10.1016/j.tet.… @Mechanophore's story - seeing models make intriguing predictions into reaction behavior. "A key turning point for me was seeing how FEA of Frontal Ring-Opening Metathesis Polymerizations simulates autonomous curing processes by balancing reaction enthalpy with heat transport"

Did you know you can create a Google Scholar profile for your research group?

While GS is mostly used to track individual metrics, this process allows you to track and highlight team and project metrics.

Here's 5 steps to get to this 👇 #AcademicChatter #AcademicTwitter 1. Create a group Google Account
2. Navigate to Google Scholar (scholar.google.com) and click "My Profile"
3. Fill in your details

It's been a wild week already in #ML/#AI for science. Advancements in using diffusion models for protein folding, using learned potentials to discover new catalyst materials, a proposed battery data genome to speed energy storage material discovery, and so much more! 🧵 (1/8) @KevinKaichuang et al use diffusion models to generate novel foldable protein structures. (2/8)

https://twitter.com/KevinKaichuang/status/1576917631659307009?s=20&t=4A9MlA4Ge_ozKCSwNZDccw