Are you developing new polymeric materials, or aiming to improve the performance of existing plastics or elastomers? Perhaps you are trying to incorporate more recycled material, to eliminate additives that have become obsolete due to new regulations, or simply to improve performance or lower cost. In each of these examples, you will need to optimise the impact of multiple parameters. And you will be doing this based on data from experiment, simulation, or production that you are constantly seeking to understand and improve. The Alchemite software can help. Find out how you guide experimental programs and get vital insights into what is driving the performance of your materials, ensuring quality and enabling effective innovation.
Recorded webinar - Machine learning for polymers R&D
Save time and cost optimising chemistry, blending, and formulation
Optimising the chemistry, blending, and formulation of polymers can have profound impacts on the performance, economics, and sustainability of an array of products and industrial processes. Yet such vital development often relies on trial-and-error, intuition, and costly, time consuming experimental programs. Find out how innovative machine learning can enable a data-driven approach, focusing experiment more effectively and providing scientists and chemical engineers with quality insights to guide development of product concepts and to enable more efficient, reliable processes.
White paper including example application
This white paper discusses some polymer R&D challenges and how machine learning can help. It outlines some of the difficulties of using machine learning for polymers and shows how these are overcome by the Alchemite™ software, with project application examples.
Publication - applying machine learning to lubricants
Lubricants are commercially-important, yet understanding of how to improve key properties is still relatively poor. In an Alchemite study with scientists from BP, the relatively sparse experimental data was combined with results from molecular dynamics simulations. Alchemite was able to exploit property-property correlations in this data to predict the physical properties of known and new alkanes.
Alchemite for plastics and elastomers
With the Alchemite software, polymer scientists, chemical engineers, and data scientists can apply powerful deep learning methods to get more from their data. You could use Alchemite to:
- Gap-fill and validate sparse, noisy data from suppliers, experiment, simulation, and production
- Auto-generate models that identify key ingredient-property-process relationships
- Quantify uncertainty to support a rational business case for key decisions
- Design experimental programs to support formulation or re-formulation of polymers with the fewest experiments
- Identify new or improved polymer blends
- Optimise process parameters to improve quality and performance