Abstract: Atmospheric fine particulate matter (PM2.5) is known to adversely impact human health. The dominant fraction of PM2.5 is organic, and these organic aerosols can be formed in the atmosphere through oxidative processing leading to secondary OA (SOA). It is now widely recognized that the multiphase chemistry of water-soluble isoprene oxidation products constitutes a substantial SOA formation pathway. Global models have predicted that the Amazon is a global hotspot in the production of multiphase isoprene SOA. Accurate SOA PM2.5 predictions are a known challenge for regional scale air quality models (AQMs) like the Community Multiscale Air Quality Model CMAQ due to the complex interactions with isoprene and the physicochemical properties of aerosols. Recent chamber experiments have systematically examined these properties and their impacts on SOA production that include various atmospheric conditions, types of pre-existing SOA coatings, and type of coating and thickness. Their results show how these properties can significantly reduce isoprene-derived SOA. We have leveraged this new data to constraint critical parameters needed to simulate isoprene derived SOA in a regional scale model. This work evaluated current isoprene-derived SOA CMAQ algorithms in box models of experimental data, and then implemented those improvements in a full regional scale model focused on the Amazon.