Official Twitter for the Gómez-Bombarelli group @MIT_DMSE | We use atomistic simulations and ML for accelerated materials design | Managed by group members
Cambridge, MA
Joined October 2022
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The almanac of matter models is the future for tracking foundational models for materials science! Incredible work, @BenBlaiszik and team!
Why does this matter? We believe the Almanac is the foundation for a new kind of scientific instrument that will enable AI-guided ensembles of MLIPs (with improving quality weekly) to be pointed at the hardest problems.
Recent work from Sathya Edamadaka, Soojung Yang, Ju Li and @rgblabmit, which was a core motivator of the Almanac work, shows convergence in representation space between models. But, despite this convergence, no single model is yet enough for most research tasks. arxiv.org/abs/2512.03750
Demo coming soon: agent-driven MLIP committees orchestrated with Academy on @globus Compute - agents that query many potentials, quantify disagreement, and decide what to trust or what needs more detailed simulation.
RGB Lab @ MIT retweeted
Today, we excitedly announce Garden's Almanac of Matter Models!
The goal is simple: make it as easy to use the latest matter models as it is to use the latest LLM. We see this as the foundation of a new kind of scientific instrument - one where ensembles of MLIPs, not a single model, are pointed at a discovery problem.
garden-ai.github.io/almanac/
RGB Lab @ MIT retweeted
The Machine-Learned Interatomic Potential (MLIP) landscape is growing so rapidly. New foundation models for molecules, materials, and proteins seem to drop every week, each with its own architecture, training data, and software quirks. This fragmented, and sometimes downright tricky setup and maintenance slows, and often precludes deep questions that require many models from all but the most advanced users.
What if instead anyone could access and run them with very little hassle?
Fun surprise coming later in the week... :)
@RGBLabMIT @ianfoster
RGB Lab @ MIT retweeted
"Atomistic Language Models Understand and Generate Materials"
Most materials AI still treats crystals and language separately, either turning atoms into lossy text formats or making LLMs call atomistic tools.
This paper makes materials natively multimodal by connecting a 3D atom encoder, Qwen LLM, and diffusion crystal generator through continuous latent projectors.
This model can read atomic coordinates, predict properties, edit crystals from text, and generate stable new materials, with SoTA crystal structure prediction and strong de novo generation.
Our new work achieves native multimodality over language and atomistic structure. The paradigm unlocks crystal property prediction, generation, and optimization as instructed by text!
RGB Lab @ MIT retweeted
Thrilled to announce the final preprint of my PhD!
We introduce PackFlow, a flow matching method for generative molecular crystal structure prediction, and post-trained via reinforcement learning on MLIP energies and forces.
Paper: arxiv.org/abs/2602.20140
@RGBLabMIT
RGB Lab @ MIT retweeted
📢New Article alert! @elton_pan_, @OlivettiGroup, @RGBLabMIT, @mit_romangroup, and colleagues from @mit_dmse, @MITChemE and @ITQ_UPVCSIC develop DiffSyn, a generative approach for predicting materials synthesis recipes. nature.com/articles/s43588-0…
🔓rdcu.be/e1ViT
RGB Lab @ MIT retweeted
We'll be giving a public talk about this work tomorrow, January 28, at 11:30 AM ET (8:30 AM PT, 4:30 PM GMT)! It'll be hosted by LeMaterial, an initiative from @entalpic_ai and @huggingface. We'd love to see you there!
🔗meet.google.com/hcg-szpf-ibh
📅 shorturl.at/aL6FR
This paper shows that wildly different AI models for molecules, materials, and proteins are independently learning the same underlying representation of matter
suggesting we’re converging on a shared physics-grounded “latent reality” that proves scientific foundation models might actually be generalizable across domains
RGB Lab @ MIT retweeted
Scientific foundation models are converging to a universal representation of matter.
Come chat with us at #NeurIPS! We (@SoojungYang2 @RGBLabMIT) have an oral spotlight at the #NeurIPS #UniReps workshop and will also poster at #AI4Mat.
🖇️: arxiv.org/pdf/2512.03750
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