Tessera

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

FACCTs@faccts.de · last yr.

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!

“Built with the high-performance quantum chemistry program package ORCA (Version 6.0.1), OMol25 contains simulations of large atomic systems that, until now, have been out of reach.” - Meta #ORCAqc #ORCA6 #CompChem #QuantumChem #ML #Meta ai.meta.com/blog/meta-fa... arxiv.org/abs/2505.08762

Berkeley Lab @berkeleylab.lbl.gov · last yr.

🚨 Just dropped: Open Molecules 2025 — a record-breaking dataset co-led by Berkeley Lab + Meta FAIR. 100M+ DFT snapshots. Built to train #AI for real-world chemistry 🧪. Could reshape discovery in batteries, drug discovery & much more! @cs.lbl.gov ⬇️

Computational Chemistry Unlocked: A Record-Breaking Dataset to Train AI Models has Launched - Berkeley Lab

Scientists will finally be able to simulate the chemistry that drives our bodies, our environment, and our technologies.

newscenter.lbl.gov

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

Andrew S. Rosen @andrewrosen.bsky.social · last yr.

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...

IIDAI Seminar, 5/1/2025, Samuel M. Blau (Berkeley Lab)

YouTube video by Coordinated Science Laboratory

youtube.com

🧠 New postdoctoral researcher position at Princeton for those interested in data science and machine learning! Specify my group if you are interested in working together. Deadline is May 31. Details: puwebp.princeton.edu/AcadHire/app...

puwebp.princeton.edu

Gabe Gomes @gabegomes.bsky.social · last yr.

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

the @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.

Aditi Krishnapriyan@ask1729.bsky.social · last yr.

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!

1/ Machine learning force fields are hot right now 🔥: models are getting bigger + being trained on more data. But how do we balance size, speed, and specificity? We introduce a method for doing model distillation on large-scale MLFFs into fast, specialized MLFFs! More details below: #ICLR2025

John Parkhill @johnparkhill.bsky.social · 2y ago

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)

ChemRxiv Bot @chemrxivbot.bsky.social · 2y ago

@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

Inverse Design of Complex Nanoparticle Heterostructures via Deep Learning on Heterogeneous Graphs Authors: Eric Sivonxay, Lucas Attia, Evan Walter Clark Spotte-Smith, Benjamin Lengeling, Xiaojing Xia, Daniel Barter, Emory Chan, Samuel Blau DOI: 10.26434/chemrxiv-2024-1dw4q

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

Bingqing Cheng @chengbingqing.bsky.social · 2y ago

Long-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!

Learning charges and long-range interactions from energies and forces

Accurate modeling of long-range forces is critical in atomistic simulations, as they play a central role in determining the properties of materials and chemical systems. However, standard machine lear...

arxiv.org

This week, "RNMC: kinetic Monte Carlo implementations for complex reaction networks" was published in @joss-openjournals.bsky.social. Work on RNMC started back in 2021, when brilliant mathematician Daniel Barter suggested using stochastic methods to study #reactionnetworks. #CompChem #ChemSky 🧪 1/6

RNMC: kinetic Monte Carlo implementations for complex reaction networks

Zichi et al., (2024). RNMC: kinetic Monte Carlo implementations for complex reaction networks. Journal of Open Source Software, 9(104), 7244, https://doi.org/10.21105/joss.07244

joss.theoj.org

Kjell Jorner @valencekjell.com · 2y ago

I've been slacking on research updates here! A few weeks ago, a preprint for "HEPOM: Using Graph Neural Networks for the accelerated predictions of Hydrolysis Free Energies in different pH conditions" dropped on @chemrxiv.bsky.social. 1/5

HEPOM: Using Graph Neural Networks for the accelerated predictions of Hydrolysis Free Energies in different pH conditions.

Hydrolysis is a fundamental family of chemical reactions where water facilitates the cleavage of bonds. The process is ubiquitous in biological and chemical systems, owing to water's remarkable versat...

chemrxiv.org

Another 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...

PhD position in digital ligand engineering for nanocatalysis

jobs.ethz.ch

Sam Blau@samblau.bsky.social · 2y ago

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