LiveDesign^®

Key property predictions with automated workflows and high-throughput computation.

Schrödinger's platform is designed to enable design of high-quality, next-generation organic electronic materials. Our physics-based models and machine-learning algorithms are intended to help tackle major challenges in designing new materials for display, energy, and wearable devices. Schrödinger’s platform predicts with a high degree of accuracy key properties of molecular materials used in today’s organic light-emitting diodes (OLED), organic photovoltaics (OPV), and printed/flexible electronics. Our approach enables discovery of novel molecules more rapidly, at lower cost, with a higher likelihood of success compared to methods based on trial-and-error or traditional molecular modeling techniques.

Enhanced in silico design of catalysts and reactive precursors for the creation of materials.

Our platform is designed to enable the simulation, optimization, and discovery of effective, efficient, and selective catalysts and reactive systems. We empower in silico design of catalysts and reactive precursors with enhanced or differentiated reactivity for the creation of materials. You can also use our platform to elucidate the details of a reaction coordinate to understand observed activity, selectivity, and specificity. Our tools include differentiated model builders, an efficient DFT engine, Jaguar, automated DFT-based reactivity workflows, and analysis tools.

Model builders, adaptable workflows and analysis tools for discovery of novel polymers and fluid materials.

Chemical reactivity, physical morphology, and polymer physics drive the behavior of polymers and soft materials. Our materials science platform offers differentiated model builders, an extremely efficient MD engine, automated thermophysical and mechanical response workflows, a chemically adaptable cross-linking workflow, and analysis tools for the simulation, optimization, and discovery of novel polymers and fluid materials.

Empowered atomistic modeling enabling accurate predictions.

Our platform empowers atomistic modeling of materials for batteries, fuel cells, and hydrogen storage materials. Our tools, based on the principles of quantum mechanics, are capable of predicting critical properties with a high degree of accuracy, including the ion mobility, intercalation potentials, and load capacity of battery electrodes, additives chemistry at the electrode-electrolyte interfaces, electro-catalytic activity, and degradation processes. Molecular dynamics simulations allow for analysis of elastic and thermophysical properties as well as ionic mobility in organic and solid electrolytes. Enhanced structure building and enumeration capabilities as well as high-performance algorithms enable in silico discovery and optimization of key device components and their interfaces.