Check out our recent piece talking about the 'execution gap' between genAI modeling and real-world applications in chemistry and materials science. We also provide thoughts on paths forward to balance development with risk and mitigation strategies.

Zeolites, porous materials with diverse applications, often require intricate synthesis processes. Elton Pan Olivetti Group @ MIT and colleagues MIT ChemE Dept and ITQ (UPV-CSIC) developed an open-source dataset of 23,000+ synthesis routes. Read more in ACS Central Science: buff.ly/43bGWUD

🚨Our work on 'Capturing short-range order in high-entropy alloys with machine learning potentials” is out on arXiv! Congratulations Yifan for the awesome work! arxiv.org/abs/2401.06622 🚨

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Professors Bilge Yildiz and Rafael Gomez-Bombarelli are using AI to design new compounds or alloys whose surfaces can be used as catalysts in chemical reactions. Their research was published in Nature Computational Science. buff.ly/48EyfUt Nature Computational Science

I'm happy to share that our paper 'Autonomous, multiproperty-driven molecular discovery: From predictions to measurements and back' was published yesterday in Science Magazine: science.org/doi/10.1126/sc…

I'll be at #NeurIPS2023 next week, presenting an Oral on our recent work introducing a classic psychophysics method to the study of deep net robustness! If you're also interested in NeuroAI, robustness and alignment, DM me and we can find a time to chat! neurips.cc/virtual/2023/o…

Had a blast last week presenting our work at #HEA2023 ! I will also be presenting at Materials Research Society (MRS) #F23MRS next week, would love to chat if anyone is around!

Thrilled to share my recently published work describing another curious case of inter-domain horizontal gene transfer (HGT), this time involving nematodes! HGT is just 😍🤯🔥 Many thanks to Matt Daugherty and a wonderful collaboration with Dengke Ma!

embopress.org/doi/full/10.15…

It was wonderful to meet all 300+ of you at #MoML2023 ! 😃 So exciting to see the budding potential of early-stage molecular ML research & predict what the rapidly approaching future of molecular ML could look like in just a few years. See you all next year! 👋

DMSE PhD candidate Soojung Yang was among 13 newly selected Takeda Fellows for the 2023-24 academic year. Yang's research applies methods in geometric deep learning and generative modeling to better understand protein dynamics. buff.ly/47iBkJg

Our work on “Quantifying chemical short-range order in metallic alloys” is out on arXiv! arxiv.org/abs/2311.01545…

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New school year, new group photo! Check us out in front of the Stata Center. 😁

The first publication of my PhD and my first 10-rated review!
Thrilled to share our NeurIPS Conference'23 Oral finding large differences in frequency tuning between humans and neural networks, which strongly explain shape bias and robustness!
arxiv.org/pdf/2309.13190…
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Congratulations to Simon Axelrod for recently defending his dissertation on accelerating drug discovery with quantum chemistry, machine learning, and molecular dynamics! 🎉🥂 We are proud of you Dr.Axelrod! 👏

#ACSFall2023 was a great conference. I saw a lot of amazing work and had the honour to meet great chemists.

I had a good time with Akshay Subramanian, Jiayu Peng, Lucia, and Kevin Greenman from RGB Lab @ MIT .

Thanks to Sam Blau for taking us to his favorite burrito place in SF (and this beautiful park — just look at the view)!

With Evan Walter Clark Spotte-Smith (they/them) Johannes Dietschreit Akshay Subramanian and Lucia from RGB Lab @ MIT.

Excited to be presenting about our recent work at #ACSFall2023 on Monday at 3:15pm. Look forward to seeing you there! RGB Lab @ MIT

Happy for the opportunity to present my latest work done in RGB Lab @ MIT at #ACSFall2023 .

First paper of my PhD out in Digital Discovery ! We show the utility of mining domain-focused chemistry datasets for generative modelling. doi.org/10.1039/D3DD00… RGB Lab @ MIT

Professor Rafael Gomez-Bombarelli won the inaugural Common Ground Award for Excellence in Teaching, which is given by MIT Schwarzman College of Computing to faculty who have made exceptional contributions to teaching classes with substantial computing content.