Register for our on demand webinar and see how we are going beyond understanding the relationships of a material’s atomic and molecular structure with its properties and behavior – but also quickly screen materials and chemicals for potential viability in a project.
Computational materials science methods have advanced in recent years to provide the ability to directly predict materials structure and properties for a range of materials including polymers, liquids, alloys, semiconductors, and catalysts. Using a range of simulation technologies, thermal, chemical, and mechanical properties can be predicted, and materials can be accurately screened in rapid fashion. Through the application of Machine Learning to drive materials model building and simulation, new chemicals and materials with specific properties can be identified to prioritize testing.
This on demand webinar will include examples of applying Machine Learning to select and virtually screen chemical additives and materials such as polymers and catalysts in order to accelerate materials selection and product innovation.
