Are you developing new chemical compounds, designing formulations, or aiming to improve or manage a chemical process?
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 can apply it to guide your experimental programs, getting better results in less time, and to get vital insights into what is driving the performance of your products or processes, ensuring quality and enabling effective innovation.
Case studies and resources
Designing an automotive catalyst at Johnson Matthey
The design of catalysts to reduce harmful emissions is currently an intensive process of expert-driven discovery, taking several years to develop a product. Machine learning can accelerate this timescale, leveraging historic experimental data from related products to guide which new formulations and experiments will enable a project to most directly reach its targets. Researchers from Johnson Matthey and Intellegens published the results of such a project in the Johnson Matthey Technology Review.
Chemical discovery - lubricants example
Lubricants are a commercially-important class of chemical which are predominantly mixtures of alkanes. 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.
Ink formulations - Domino Printing Sciences case study
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.
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 chemistry and chemical processes
With the Alchemite software, chemists, 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 structure-property-process relationships
- Quantify uncertainty to support a rational business case for key decisions
- Design experimental programs to achieve objectives with the fewest experiments
- Identify new candidate compounds that meet target properties
- Optimise process parameters to improve quality and performance