Materials Science

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.

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.

Atomic and coarse-grained models for real-life systems in food, packaging and cosmetic industries

As trends in the consumer packaged goods (CPG) industry continue to evolve, there is a need for rapid development of new and innovative products and the urge to understand and rationalize complex formulations. Molecular modeling and simulation provide new opportunities to accelerate R&D product development, rationalize product behavior, optimize manufacturing processes, and reduce costs by offering insights into the atomic-level properties that impact product performance.

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.

Controlled guidance and time-saving solutions using molecular simulation.

Most of today's high-tech devices, from solar panels to smartphone chips, depend for their operation on particular materials in precisely formed structures. Fabricating these structures at the micro- or even nano-scale is a huge challenge. Chemical reactions have to be controlled so as to add or remove material where it is needed, sometimes as precisely as one atomic layer at a time. Molecular simulation is designed to enable this control by guiding users to understand deposition and etch chemistry and achieve atom-by-atom control of chemical reactions at surfaces. Schrödinger leads the way by providing time-saving solutions for the simulation of surface reactivity and the design of novel chemicals.

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.

Enabled optimization and efficiency for complex and evolving structures.

Complex and evolving structures, often in fluid states, play a crucial role in many industrial processes across the pharmaceutical, consumer product, plastic, composite, and petrochemical industries. Our materials science platform delivers validated workflows and expert support to enable researchers to optimize the properties of their end products through a rigorous focus on selecting and combining the right ingredients in the right manner. Workflows are available to determine elastic constants (e.g., bulk modulus, shear modulus, etc.), glass transition temperatures (Tg), diffusion constants, melting points, and solubility parameters. Additionally, atomistic and coarse-grained models permit the characterization of molecular interactions and nanoscale structuring within otherwise disordered bulk systems.