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📢Fangning's work on size-transferable machine learning exciton model is now featured in the Supplementary Cover Art of The Journal of Physical Chemistry Letters ACS Publications ! The model can rapidly predict the excited state properties of nanosized aggregates! #MyACSCover

Excited to kick off our ACS Spring 2025 symposium "ML for Molecular Simulation & Design" tomorrow at 8 am! Join us for 1.5 days of invited talks featuring top researchers from universities, national labs, and industry. acs.digitellinc.com/live/34/page/1…. See you there!

Thanks to all who participated in the 2nd meeting of #Scialog: Automating Chemical Laboratories! Cosponsored by RCSA, Beckman Foundation & Frederick Gardner Cottrell Foundation, with Walder Foundation support.

In correlated materials, strong quantum fluctuations hamper conventional spectral diagnosis of phase transitions, as the usual “energy gap” is no longer a reliable marker. We employ ML to parse single-shot photoemission spectra and pinpoint long-range superconducting order.🧵2/6

Huge congratulations to Ariel Gale on successfully defending her PhD dissertation! So proud of this milestone and excited for what’s next!

Prof. Mingda Li MIT Nuclear Science and Engineering just published a preview in Newton to introduce our recent paper in Newton (doi.org/10.1016/j.newt…) about using domain adversarial neural networks (DANN) to detect phase transition in superconducting material. cell.com/newton/fulltex…

Now in Digital Discovery, Xu Chen introduces the ACES-GNN framework, designed to simultaneously improve predictive accuracy and interpretability by integrating explanation supervision for activity cliffs (ACs) into GNN training. pubs.rsc.org/en/content/art…