Machine learning approach reveals exciton states in large molecular assemblies Organic molecular assemblies composed of aromatic building blocks have attracted wide interest because of their tunable optical behaviors in fields like photovoltaics, bio-inspired light harvesting,

Data scarcity often limits ML applications in experimental science. We tackle this with a domain-adversarial framework that fuses abundant simulation data with scarce experimental data to detect thermodynamic phase transitions from photoemission spectroscopy. 🧵1/6

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…

(Reposting due to wrong link posted in previous post) Our recent work about utilizing domain adversarial neural network trained on simulated data to analyze experimental spectra is now featured in the cover image of Newton, volume 1, issue 3! 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…