Research [ASAP] Size-Transferable Prediction of Excited State Properties for Molecular Assemblies with a Machine Learning Exciton Model LikeLiked Date: March 3, 2025 Less than 1 MinRead Views: 34 The Journal of Physical Chemistry Letters DOI: 10.1021/acs.jpclett.4c03548 Source: http://dx.doi.org/10.1021/acs.jpclett.4c03548 Tags:Materials Industry FacebookTwitterLinkedinWhatsAppTelegramEmail ALT-Lab-Ad-1ALT-Lab-Ad-2ALT-Lab-Ad-3ALT-Lab-Ad-4ALT-Lab-Ad-5ALT-Lab-Ad-6ALT-Lab-Ad-7ALT-Lab-Ad-8ALT-Lab-Ad-9ALT-Lab-Ad-10ALT-Lab-Ad-11ALT-Lab-Ad-12ALT-Lab-Ad-13 Recent Articles A physical, low-environmental-impact, and easily applicable technology for enhancing the performance of recycled concrete: Multiscale insights Research April 25, 2026 Influence of the cathodic binder on CO2 electroreduction to formic acid in a three-compartment electrolyzer Research April 24, 2026 The impact and application of lignin fillers in triboelectric nanogenerators: from performance optimization to sustainable energy technology Research April 24, 2026 Copper stress responses in Scenedesmus obliquus–Bacillus subtilis Consortia: Machine Learning-Based prediction of copper removal Research April 24, 2026 Hope College is Now an Accreditated Level 2 Arboretum Climate & Resource April 24, 2026 Renewables Overtake Coal for the First Time as World’s Largest Electricity Source in 2025 Climate & Resource April 24, 2026 I Am Artemis: Peter Rossoni Research April 24, 2026 Verra’s VM0051 Gains CORSIA Eligibility, Boosting Rice Carbon Credit Demand Carbon Markets April 24, 2026 Summer tire range for EMEA markets launched by Cooper Tires Clean Tech April 24, 2026 Packaging innovations: CVS Health swaps foam, PPG coats pet food cans Packaging April 24, 2026 Load more
