RGB Lab @ MIT

Official Twitter for the Gómez-Bombarelli group @MIT_DMSE | We use atomistic simulations and ML for accelerated materials design | Managed by group members

[Akshay Subramanian @AkshaySubraman9]

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: https://t.co/wFbxV7RQb4
@RGBLabMIT

[Nature Computational Science @NatComputSci]

📢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. https://t.co/oatO2rL6yj

🔓https://t.co/pLPLicmyti

[Sathya Edamadaka @sathyaedamadaka]

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!
🔗https://t.co/Htj360egSZ
📅 https://t.co/38bmrkCM28

[Sathya Edamadaka @sathyaedamadaka]

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.
🖇️: https://t.co/H8ZRlpvJUj
🧵(1/5)

[RGB Lab @ MIT @RGBLabMIT]

OK now we are out go follow us here; https://t.co/Olfd0dElaM

[RGB Lab @ MIT @RGBLabMIT]

Our work on "End-To-End Learning of Classical Interatomic Potentials for Benchmarking Anion Polarization Effects in Lithium Polymer Electrolytes" is out now in Chemistry of Materials! https://t.co/JSDwQwv8VZ

[RGB Lab @ MIT @RGBLabMIT]

We also find that previously-parameterized classical potentials model two separate anion polarization states that drastically influence resulting lithium solvation and transference.

[RGB Lab @ MIT @RGBLabMIT]

We're out.
So long, and thanks for all the fish

Group's Bluesky
https://t.co/Olfd0dElaM
Rafa's LinkedIn https://t.co/9IlIjUEoct

[RGB Lab @ MIT @RGBLabMIT]

Slightly less surprising than you'd think. We ran into the same wall three years ago https://t.co/L6PHaQcM47

[Biology+AI Daily @BiologyAIDaily]

The surprising ineffectiveness of molecular dynamics coordinates for predicting bioactivity with machine learning

1. The study challenges the assumption that molecular dynamics (MD)-derived coordinates are superior for machine learning-based bioactivity predictions, revealing that they often underperform compared to minimum-energy conformations.

2. Using over 2600 protein-ligand complexes, the authors systematically compared MD-derived and minimum-energy coordinates, employing three descriptor sets and machine learning algorithms like Random Forest, XGBoost, and Support Vector Regression.

3. Surprisingly, MD-derived conformations failed to consistently outperform minimum-energy structures, even though they provide dynamic representations of molecular interactions.

4. In certain cases, ensemble averaging of MD-generated snapshots improved predictive performance slightly, but the benefits were not proportional to the computational costs.

5. Extended Connectivity Fingerprints (ECFPs), a 2D molecular representation, outperformed MD-based models in many cases, questioning the utility of complex 3D data for predicting bioactivity.

6. The findings highlight a critical need for better 3D and dynamic molecular representations. The study suggests exploring geometric deep learning or incorporating protein information to improve machine learning models.

7. The authors propose a tiered approach: using fast, simpler methods like ECFPs for initial screening, followed by MD-based predictions for refining top candidates.

8. The study serves as a wake-up call for molecular machine learning, emphasizing the importance of balancing data complexity, computational cost, and predictive accuracy.

@fra_grisoni @DerekvTilborg @Rza_ozcelik
📜Paper: https://t.co/6b6GxztgQ9

#MachineLearning #DrugDiscovery #MolecularDynamics #Bioinformatics #AI

[Akshay Subramanian @AkshaySubraman9]

📢New preprint out! We constrain the molecular generation space to follow the "symmetry" of patented molecules that are likely to be synthesizable. Achieved with "symmetry-aware" fragment decomposition, and a constrained Monte Carlo Tree Search generator. https://t.co/NWidW2Wx9y

[Nofit @NofitSegal]

Zero-shot extrapolation for out-of-distribution (OOD) chemical property prediction is an important step towards high-performance materials discovery. Check out our spotlight at the #NeurIPS AI for Accelerated Materials Design Workshop! https://t.co/wHxezk4zD7

[RGB Lab @ MIT @RGBLabMIT]

I will always upvote bogus enthalpy-entropy compensation.
My thesis advisor, J Casado, loved this paper. I remember doing the calculations in undergrad kinetics class some 20 years ago

[Andrew S. Rosen @Andrew_S_Rosen]

Instead of the typical (somewhat technical) lecture, we discussed a couple of papers, including a favorite J. Chem. Ed. activity of mine: "Chemistry from Telephone Numbers: The False Isokinetic Relationship." It's a lot of fun. Do check it out. 2/3

https://t.co/uvFEdlESXK

[gecniv 📷 | Vince Guillerm 👨‍🔬 @gecniv]

MERGED NETS 💎📄
I can’t describe 350+ new nets in 280 signs, so just watch the animation & read our new paper in @ScienceMagazine 👨‍🎨💎👨‍🔬 with @Eddaoudi_FMD3.
In short, we merge nets together and get new topologies perfect for designing mix-ligand #MOFs.

https://t.co/xnARltj9dO

[DMSE at MIT @mit_dmse]

Applying to DMSE? The DMSE Application Assistance Program (DAAP) offers support for students from underrepresented groups in science and engineering. You’ll be paired with a grad student mentor to guide you through the application process. Apply by Nov 1. https://t.co/4H3Eq1gHxz

[DMSE at MIT @mit_dmse]

In tomorrow’s Wulff Lecture, DMSE’s Professor Antoine Allanore will explore greener iron and steel production processes, highlighting innovations that use electricity instead of carbon. October 23 | 4 pm | 6-120 https://t.co/Ngj6F32juC

[Sulin Liu @su_lin_liu]

Code💻: https://t.co/o2VV1pjJMV

HuggingFace paper page 📰: https://t.co/55fsBHOAgu

Shout out again to my amazing collaborators 🙌🙌🙌 and @thjashin for insightful discussion ❤️❤️❤️! (11/11)

[Sulin Liu @su_lin_liu]

Discrete generative models use denoisers for generation, but they can slip up. What if generation *isn’t only* about denoising?🤔

Introducing DDPD: Discrete Diffusion with Planned Denoising🤗🧵(1/11)

w/ @junonam_ @AndrewC_ML @HannesStaerk @xuyilun2 Tommi Jaakkola @RGBLabMIT

[Luigi Bonati @LuigiBonati]

Final group photo of the @cecamEvents workshop on "Advances in catalytic reactivity simulations under operando conditions"!
Thanks to all participants for your valuable contributions and stimulating discussions
and to @IITalk @fondazione_fair for support & CASALE SA for sponsor

[Jiayu Peng @peng_jiayu]

Please help repost and spread the word. 🙏 My research group at @UBuffalo's Materials Design & Innovation (@UBengineering & @UBCAS) is recruiting multiple graduate students! https://t.co/IPnthYoS1F

If you are interested in combining 🤖 data science and machine learning with ⚛️ materials physics and surface chemistry to design materials and interfaces for⚡ decarbonization and 🧪 sustainability, please visit our website and reach out to me by email (see the link above for more details).

#sciencetwitter #research #chemistry #materials #machinelearning #artificalintelligence