A data-driven route to more sustainable materials, processes and polymers

How can we best incorporate recycled feedstock into our material? Can we make plastics more sustainable? How do we modify processes to comply with regulations protecting the environment or human health? These are common challenges for scientists and engineers. They must use all available data to respond, optimising materials and processes, while minimising costly trial-and-error experiments. But this data is often sparse or noisy, limiting the value of conventional data analysis or machine learning methods. In this webinar, we will show how Alchemite™ deep learning software solves these problems, with examples of metals, formulated products, and a feature demonstration based on polymers.