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.
Webinar - A data-driven route to more sustainable materials, polymers, and chemical processes
5th May 20201
How can we best incorporate recycled feedstock into our material? Can we identify more sustainable alternatives to current materials? How do we modify processes to comply with regulations, reducing the risk of harm to the environment or human health? These are increasingly common challenges for polymer scientists. They want to use all available data as they respond, minimising the need for costly trial-and-error experiments. But this data is often sparse or noisy, limiting the value of conventional data analysis or machine learning methods. See how the Alchemite™ deep learning software solves these problems, with case study demonstrations touching on examples including polymer blending and biopolymers.
Example - 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, 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