Whatever your product – plastics, pharmaceuticals, paints, inks, cosmetics, foodstuffs, personal care products – formulation is difficult. You must bring together the right combination of ingredients and processing conditions to deliver a quality product, repeatably, while controlling cost and minimising environmental impact.
Alchemite applies powerful machine learning to guide your testing program and identify formulation improvements that deliver better product performance, reduce costs, and meet regulatory constraints. It gets more value from your existing data, even where that data is sparse and noisy, and helps you to find the most efficient routes to improve the quality of this data and your understanding of it.
View recorded case study webinar:
Case studies and publications
Formulation design at Domino Printing Sciences
Domino Printing Sciences applied Alchemite to help guide testing and find optimal formulations for their inks. This case study shows how to reduce time-to-market, identify new candidate formulations, and enable reformulation in response to market, environmental, or regulatory drivers.
“We were impressed with the ability of Alchemite™ to identify novel formulations quickly and accurately. This enabled us to make the most of limited lab resources and continue innovating during the COVID-19 lockdown.”
– Dr Andrew Clifton, Director of Marking Materials and Test Engineering Team at Domino
White paper: Machine learning for adaptive experimental design
Identifying the optimal composition and processing parameters to achieve commercial performance goals as quickly as possible is the key objective of formulation design projects. Machine learning identifies improved formulations up to 10 times quicker than traditional approaches, by focusing experimental effort directly on formulations that will lead to successful products in as few experimental cycles as possible.
Alchemite for Formulations
Alchemite is ideal for quick response to formulation or reformulation challenges.
- Gap-fill and validate sparse, noisy data from suppliers, experiment, simulation, and production
- Auto-generate models that identify key ingredient-process-property relationships
- Quantify uncertainty to support a rational business case for key decisions
- Design experimental programs to support formulation or re-formulation of products with the fewest experiments
- Identify new or improved formulations; 0ptimise process parameters to improve quality and performance
- Capture knowledge of formulation recipes as models for sharing and re-use