Sam Blau
Research scientist & computational chemist at Berkeley Lab using HT DFT workflows, machine learning, and reaction networks to model complex reactivity.
- Out in @natcomputsci.nature.com: A roadmap for inverse design of #nanomaterials heterostructures via HT data gen -> representation dev -> heteroGNN training -> gradient-based global opt! w/ @emorychannano.bsky.social @ewcspottesmith.bsky.social www.nature.com/articles/s43... Free link rdcu.be/eTH72
- Emory and I also wrote a higher level summary of the work available here: rdcu.be/eUVIw
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- I'm hiring postdocs @berkeleylab.lbl.gov to drive cutting-edge research involving MLIPs, high-throughput workflows, chemical reaction networks, generative models, and open-source software dev. Full position description + application here: forms.gle/zePBZDmciXez... #Chempostdoc #AI4Science
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- I'm presenting OMol25 tomorrow 7/29 at 9 AM PST as part of a talk series at Google. Learn how we built the dataset + how MLIPs trained on OMol are revolutionizing comp chem! Meet: lnkd.in/g4AAWkcK YouTube Stream: lnkd.in/ggmtMtTR Join group: lnkd.in/g5ciuNuX
- OMol25 was calculated with ORCA. I want to acknowledge the work of the ORCA team to improve the quality of the gradient + the robustness of SCF convergence for complicated systems as part of the OMol effort - it was much appreciated and critical to ensuring that we're releasing high quality data!
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- The Open Molecules 2025 dataset is out! With >100M gold-standard ωB97M-V/def2-TZVPD calcs of biomolecules, electrolytes, metal complexes, and small molecules, OMol is by far the largest, most diverse, and highest quality molecular DFT dataset for training MLIPs ever made 1/N
- OMol covers 83 elements, a wide range of intra and intermolecular interactions, explicit solvation, reactive structures, conformers, charges -10 to 10, 0-10 unpaired electrons, and 2-350 atoms per snapshot. It required >6B CPU hrs, 10x more than any prev MLIP training dataset 2/N
- OMol was constructed via an unprecedented diversity of methods: MD, ML-MD, RPMD, rattling, Architector, rxn path interpolation, AFIR, optimization, and scaled separation. We also recalculated some previous datasets and did additional sampling/structure generation atop others 3/N
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View full threadWe can't wait to see what the community does with OMol! Don't hesitate to reach out with feedback on the data, models, or paper - we aren't going to submit to a journal until the leaderboard goes up, which means we have time to incorporate community feedback (within reason) 10/10
- It was a pleasure to give an IIDAI seminar on nanoparticle ML for gradient-based heterostructure optimization (w/ @emorychannano.bsky.social ) and neural network path opt for finding reaction transition states on MLIPs (w/ @thglab.bsky.social) - find the talk here: www.youtube.com/watch?v=-4jB...
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- Final day to submit abstracts for ACS Fall 2025! Reminder that @ewcspottesmith.bsky.social , Brett Savoie (Notre Dame), and I are organizing a symposium on "Chemical Reaction Networks, Retrosynthesis, and Reaction Prediction". Will be a mix of invited and contributed talks - please submit! #CompChem
- Reposted by Sam Blauthe @gpggrp.bsky.social is at the ACS Spring 2025! come check out the works of Daniil Boiko and Rob MacKnight at the "ML + AI in Organic Chemistry" Symposium (Hall B-1, Room 4) today! extreme scaling of experimental chemical reactions via MS and an OS for autonomous comp chem!
- Looking forward to speaking at ACS on Sunday at 5:30! Come learn about "Popcornn" - a new method for double-ended transition state optimization atop machine learned interatomic potentials that is substantially better than NEB or GSM.
- Fantastic new work from Aditi & co that shows how to leverage the expressivity + accuracy of massive pre-trained MLIPs to distill smaller, much faster models that are still extremely accurate to drive downstream simulations - no need to compromise on speed vs accuracy!
- Applications closing in one week! If you’re interested in a prestigious postdoc at the intersection of AI/ML and nuclear nonproliferation, don’t hesitate to apply - come work with me on fascinating f-block chemistry and computational/ML methods! (Must be a US citizen)
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- Reposted by Sam Blau@samblau.bsky.social, Brett Savoie (Notre Dame), and I are organizing a symposium for @amerchemsociety.bsky.social Fall 2025 called "Chemical Reaction Networks, Retrosynthesis, and Reaction Prediction" under @acscomp.bsky.social. #reactionnetwork #CRN #retrosynthesis 🧪 ⚗️ #CompChem
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- Very proud of this work, going all the way from implementing the kMC in C++ to building datasets w/ high-throughput workflows to designing the novel graph representation to training the custom hetero-GNN w/ on-the-fly augmentation to inverse design of novel nanoparticles with GNN-based optimization!
- Preprint! "Inverse Design of Complex Nanoparticle Heterostructures via Deep Learning on Heterogeneous Graphs" is now on ChemRxiv. In this work, we consider the problem of applying deep learning to heterogeneous nanostructures. (1/10) #ChemSky 🧪 #CompChem #physics #optics #nanoscience
- Reposted by Sam BlauLong-range machine learning potentials strike again! 🚀 We benchmarked the Latent Ewald Summation method on diverse systems—molecules, solutions, interfaces. Learning just from energy & forces, it delivers the most accurate potential energy surfaces, physical charges, dipoles, and quadrupoles!
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- Reposted by Sam BlauAnother chance to join our group! We are recruiting a PhD student in digital ligand engineering for nanocatalysis. Reposts and spreading the word to interested people in your network appreciated! jobs.ethz.ch/job/view/JOP...
- Example nanoparticle heterostructure optimization, driven by gradients of UV emission with respect to layer thicknesses and dopant concentrations from our hetero-GNN (not accessible from kMC) and sub-second inference (vs days from kMC) #F24MRS
- Excited to speak at #F24MRS Thurs 1:30 - 1st talk of my career w/o any DFT connection - we design a hetero-GNN for learning core-shell nanoparticle properties, train on first ever large-scale NP kMC dataset, and use autodiff to optimize -> discover far OOD heterostructures with >6x enhanced emission
- Excited to speak at #F24MRS Thurs 1:30 - 1st talk of my career w/o any DFT connection - we design a hetero-GNN for learning core-shell nanoparticle properties, train on first ever large-scale NP kMC dataset, and use autodiff to optimize -> discover far OOD heterostructures with >6x enhanced emission
- Reposted by Sam BlauWe are hiring (resharing appreciated)! Given recent successful grant applications (I got my SNSF Starting Grant 🚀), we are extending the LIAC team with multiple openings (PhD/postdoc) for 2025. Apply now (deadline: December 20th) by filling in this form: forms.fillout.com/t/eq5ADAw3kkus. #ChemSky
