The discovery of superior new materials and their integrated use in devices and chemical reactors can be vastly accelerated by combining active learning, atomistic simulations, and multi-scale modeling.<br/>We will highlight several advances in the Amsterdam Modeling Suite enabling faster and improved property predictions for OLED and battery materials: workflows, polarizable force fields, QM/MM with polarizable embedding, qsGW, BSE, automated reaction pathway predictions, force field parametrization tools, GFN-FF, charge mobility, and electrolyte solubility predictions.<br/>We will also discuss how integration with active learning AI techniques, through a collaboration with Kebotix, accelerates the computational screening by more than on order of magnitude. The integration with meso- and macroscale methods enables atomistic-to-device level simulations for OLEDs, through a collaboration with Simbeyond, and for atomistic-to-reactor-scale modeling in the context of the EU ReaxPro consortium.

Computational design of materials and processes: AI-accelerated atomistic & multi-scale modeling

Fall 2022 abstracts, posters, and presentations will launch in mid-September.

ACS Fall 2022

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.