The Materials Research Data Alliance meeting is next week! Great panelists and opportunity for connection. Registration is free, and content is available online worldwide. Sign up to hear from key speakers: James Warren (NIST), John Schlueter (NSF), Andrew Schwartz (DOE), and Sean Donegan (AFRL)

If your team works on ML for materials/molecular discovery and needs someone who has built physics-informed networks for real problems, check out Zhihao Feng. Genuine problem-solver, strong collaborator, adaptable. Here's an example of his work. Link: www.linkedin.com/posts/fengzh...

What are the biggest software-solvable problems holding you back in your research? e.g., - There's no Python package to convert formats between X and Y - Running tasks on HPC systems requires expertise I don't have - Formatting or tracking the references for my paper is annoying

The 4th Materials Research Data Alliance meeting is set for Feb 17-19 on topics in materials + data + AI. This is a great opportunity to hear from speakers from academia, national labs, and industry. Register now (free - online): marda-alliance.org/2026-marda-a...

DOE announces $320M in new investments in the AI4Science! You can read more about the Genesis Mission, the American Science Cloud, the Transformational AI Models Consortium (ModCon), along with 51 AI in science application projects here and in the attached image. 🔗 energy.gov/articles/ene...

Amazing week of results from AI4Science with NeurIPS and MRS. But, for those who couldn't make it, post a link to your research here we can boost you too!

I saved this image from Stable Diffusion in Dec 2022 because it was amazing a model could output such detail. 😅

TorchSim continues its growth aiming to be the high-performance engine for MLIP-powered atomistic simulation. With this release (4.1), there improvements across the entire stack with new features, bug fixes, and improved documentation.

So your data are available upon reasonable request? Well, we are making some reasonable requests - at scale. :) 1. Search literature (currently stubbed) 2. Enumerate papers, extract contacts 3. Send email w/ data drop location 4. Parse data Does anyone want to help productionize this?

Yesterday I did a glow-up of the entire Materials Data Facility front end using Gemini 3 in less than 2 hours. A few things I learned followed by few snapshots of the updates. What did I learn? 1. Gemini 3 feels clearly ahead in terms of front-end design. Less AI slop, and more focus.

Northern Lights over the western Chicago suburbs at 9:15. Clearly visible by eye, lasted about 10 mins. 🤩

Did a quick experiment last night to see how far kids run on Halloween by giving my son a phone. The answer was 5.3 miles.

🔥We're excited to announce a major milestone for the machine-learned interatomic potential (MLIP) ecosystem: TorchSim is moving to community ownership and governance through a partnership with Radical AI and the open-source community! TorchSim is an atomistic simulation engine built for the AI era.

Generative AI is changing how we discover materials, but without direction, it can quickly lose its way. Marcus Schwarting led an effort to show that using active learning as a guide helps to prioritize the best candidates in scientific discovery workflows for MOFs.

Are you looking for ~120~ examples of how LLMs can be used to advance research in materials science & chemistry with open code? We've made a quick search interface for you to explore projects from the recent hackathon. See the projects here and come join our community: llmhackathon.github.io

🔥 Today we announce the Meta OMol25 Electronic Structures Dataset - 500 TB of molecular data in collaboration with the AI at Meta team. Access Details: github.com/facebookrese...

Going to be honest. Apple’s Liquid Glass and ios26 styling feels cheap and cluttered. Also why would I want to see so much visually distorted content (under glass)? Some interesting concepts, but needs a lot of work.

Over 1100 people around the world, virtual and in-person, decided to take a chance together, to imagine, and build - seeking ways to speed discovery and understanding in materials and chemistry with AI. We'll share our findings soon, but from what I've heard already prepare to be amazed.

My feed has gotten stale. Who is posting top tier science content?

Imagine researchers being able to: + Move data effortlessly across systems + Launch simulations on Exascale HPC systems + Run AI models via Garden & Galaxy All via user or agent intent/language to make the next breakthroughs in energy, materials, and chemistry.

A conversation with Magdalena Lederbauer, prize winner from our LLM hackathon last year, and rising star in AI for science. She discusses GlossaGen, a project to automate glossary building and connecting terms into networks across papers, her background and story. Video: youtu.be/9Gr4VrEKD4Q

🚀 We asked: What if the time to set up and run the best Machine-Learned Interatomic Potentials (MLIPs) took seconds, not days? Today, we release the MLIP Garden v0.1. What you can do now: - Experiment ~instantly - Scale deployments on experimental NSF and Dept of Energy systems

If you are looking for a great place to meet collaborators across scientific domains and grow your career in AI, quantum, manufacturing, and more, consider applying for an Argonne Fellowship! A similar program helped me transition to data and AI research. Link: www.anl.gov/hr/ldrd-name...

With less than a month to go until the LLM Hackathon for Applications in Materials & Chemistry, registration is open for the London site. @imperialcollegeldn.bsky.social 📝 Register: llmhackathon.github.io/sites/london/

BLASTNet is a much friendlier dataset than it sounds, and is especially useful for those working in space propulsion, chemical reaction optimization, fusion physics, aerospace applications, weather modeling, and more. The team is seeking to collect the highest quality datasets, and needs your help.

Does anyone have a science application ready that could benefit from running an MLIP like MACE, MatterSim, or SevenNet at scale? Have a fun collaboration opportunity with free large compute access for the right applications. Reply or DM if interested!

Today we are opening the teaming board for the LLM Hackathon for Applications in Materials and Chemistry (Sept 11-12). This is your opportunity to rally a team to help solve big problems! Register here for the link: llmhackathon.github.io

You can grow fresh peaches in your yard as far north as Chicago. Quite the unlock.

We're about a month and a half away from the 3rd LLM Hackathon for Applications in Materials and Chemistry (Sept 11-12). I hope you will join us! Here are a few reasons to register now. 🤗 Join the preeminent community for LLM and multimodal model applications in materials science and chemistry.

Training data volumes have exploded, but what if we only get one shot to teach a model? Evans et al show a breakthrough: a "distortable canvas" model that mimics human cognition, achieving 80% MNIST accuracy with just 1 example per class. No pretraining needed.

The first (of many, I hope!) challenges articles is finally out! Really excited to be able to provide this service to the #machinelearning community. If you're organizing #hackathons, #MLcompetitions, or similar, please reach out! @iopp-mlresearch.bsky.social #MachineLearningSeries

🚀 Announcing the third LLM Hackathon for Applications in Materials and Chemistry (Sept 11-12)! Last year, 34 teams submitted inspiring and open examples. This year, we are expecting amazing applications with higher powered models and agentic frameworks. Your imagination is the only limit.

A new preprint introduces a Socratic Method Agent that guides LLMs through structured questioning, i.e., definition, analogy, hypothesis elimination, and more following lessons learned from thousands of years of philosophy and reasoning achieving SOTA on the ARC Challenge. Continued below w links:

Son proudly and triumphantly: “We really can accomplish anything” Me: 🥺🥲🫡 Our accomplishment: Getting a Nintendo Switch 2

Even more open materials and chemistry data for everyone! Think of all the models you could train using these datasets. June updates: 26 high-quality datasets spanning polymer science, drug discovery, spectroscopy, MOF databases, + foundation model training datasets. 🔗: github.com/blaiszik/awe...

Are you interested in learning about how AI agents could be used in various research tasks? In this talk, Sam Schmidgall discusses "Autonomous Research Assistants". The talk is very accessible for those looking to get started. 📽️ Video: www.youtube.com/watch?v=5lp-...

Someone is trying to drop me a subtle hint… #Switch2

America and the world's brightest X-ray "microscope" - now 500x brighter. It can: 🔋 watch a battery charge atom by atom, 🛫 help us understand fracture in fighter jet alloys, and 🧬 speed up cancer-drug discovery. Welcome to the new Dept. of Energy Advanced Photon Source at Argonne! 🥳🔬⚡️

Building foundation models for chemistry and materials or just interested in open data and open science? This collection is a treasure trove: 🤖 LLM training sets 🔬 Computational datasets ⚗️ Experimental data 📚 Literature datasets Link in next:

Ok, Claude 4 is 🔥. I built a text-based RPG with my kids, in 1 hour, where players literally reshape reality using AI! The game lets the player speak to create their adventure (rooms, NPCs, and items) in real-time. Magic and adventure meets AI ✨ @anthropic.com

Perfect encapsulation of academic family life crossover. 😆

The world is hungry for materials data, and today we unveil our revamped Materials Data Facility experience. - Simple publication for datasets from kB to TB - Foundry-ML integration for structured datasets - Reimagined search design

The modern software development conundrum. Building today is much easier than 3 months ago, but much harder than it will be 3 months from now.

Chemeleon, was just published this week. This new generative model from @aronwalsh.github.io's team enables generation of materials given textual inputs.🦎 🌱 With Garden, you can test Chemeleon today in just a few minutes of effort! Try Chemeleon on Garden now: thegardens.ai#/garden/10.2...

How can the community create trillion token datasets from literature for training scientific LLMs? Meet AdaParse: An adaptive PDF parsing engine that delivers 17x throughput while maintaining accuracy by intelligently matching documents with parsers and optimizing computational resources.

Everyone in the past was neurotypical, which is how we got things like this single-author, eight-volume encyclopedia of ferns

It’s amazing when you have a meeting and the other person flat out gets and contributes to your vision. Just a huge energy boost. 🤩

These "weeds" are out of control!

Sometimes I just sit and wonder what Nobel-level discoveries or trillion-dollar technologies are waiting to be pieced together from the vast expanse of ArXiv. They are certainly there...

Very cool idea! You can quickly check how NIH research affects real conditions and get links to the studies, publications, and projects.