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NASA paper applies Alchemite™ to design of materials
A new paper from the NASA Glenn Research Center applies Alchemite™ machine learning to the design of shape memory alloys and heat exchangers. Machine learning can enable inverse design, in which scientists start from the desired properties to determine optimal process parameters, achieving superior results with less experimental time than alternative approaches.
Goodfellow 'Materials Inside' podcast on AI for materials
The latest episode of the Goodfellow ‘Materials Inside’ podcast features Intellegens Chief Scientific Officer, Dr Gareth Conduit, and Dr Phillip Woolston, who is part of the team applying machine learning technology to ink formulation at Domino Printing Sciences. Gareth outlines the work of Intellegens and our specialisation in extracting value from sparse, noisy, experimental data. Phillip explains how machine learning is being applied to streamline formulation development processes at Domino. The discussion covers what we mean by ‘artificial intelligence’ and ‘machine learning’ in the context of materials, and some of the challenges and trends in the area.
Recorded webinar: Optimising experimental workflows
This month’s webinar focused on the development of new and improved chemicals and formulated products, walking through a complete workflow for building a machine learning model, then improving it through focused acquisition of new data. Such an approach typically results in 50-80% fewer experiments than conventional methods, and has been proven in applications including specialty chemicals, food and beverage, paints, dyes, fragrances, cosmetics, and plastics.
Alchemite™ update improves formulation design capabilities
Our development team have been busy with the continuous improvement of the Alchemite™ software, taking inspiration from customer feedback. A recent enhancement was requested by formulation design customers aware that the lowest cost solution is often to apply or adapt something previously made. It’s now quick and easy to search over the training dataset for formulations with a high probability of meeting design targets, while using the power of machine learning to fill in missing data when assessing historic samples. Other recent additions include improved performance when predicting categorical outputs and a more sophisticated assessment of model quality.
Blog: Who gets the key to the machine learning treasure trove?
On this month’s blog, we discuss the issue of widening access to machine learning in the chemicals, materials, and manufacturing community. Is ‘democratisation’ the right thing to do, what are the challenges, and how are they overcome?
White paper - 7 examples of AI in materials & chemicals
If you haven’t yet taken a look at our recently updated white paper ‘7 examples of how materials and chemicals companies innovate with AI’, why not download it now? Get insights into alloy design, life sciences, plastics, design of experiments, formulation design, additive manufacturing, and batteries.
We'll be at ACHEMA 2022!
We’re looking forward to joining ACHEMA, the major international event for the chemical process industries, for the first time next month. We’ll be in the Digital Hub (Hall 11.0, stand F58). If you’re going, why not contact us in advance to fix up a meeting?
Join our team
We’re continuing to grow the Intellegens team both at our Cambridge headquarters and in the US. If you have relevent experience in data science, machine learning, software development, materials or chemical R&D, or related commercial activities – get in touch!