When should I consider machine learning software for my R&D project?

Whenever you have data that you might be able to learn from! If your data describes the inputs to a system and the resulting outputs, machine learning can build a model that gives you valuable insights. For example, it can tell you which inputs are most important in determining which outputs. It can predict outputs for a new set of inputs, enabling virtual experiments that provide a low effort way to test hypotheses. And it can suggest which inputs might achieve target outputs, helping you to design experiments and generate product ideas. Examples of the systems studied include formulations, materials, chemicals, manufacturing processes, and much more.

Can you share examples of machine learning being used in R&D?

Yes, check out machine learning case studies from organiziations like FUCHS Group (chemicals), Yili (food formulations), Voestalpine (materials), CPI (life sciences), NASA (aerospace), and AMRC (manufacturing).

What's the best machine learning approach for R&D?

Three key factors you need to consider are: (1) Can the approach generate a useful model even if data has gaps (is sparse) or is noisy - as much R&D data is? (2) Does it have excellent capabilities to understand and use the uncertainty in its model? (3) Is it easy-to-use for the specific R&D tasks I have in mind (like DOE or formulations)? Alchemite machine learning meets these criteria.

Can I trust the results I get from machine learning?

The results from machine learning will, of course, depend on the data input to the model. The more data and the better the quality of that data, the more reliable the machine learning predictions will be. What is very important is that the ML method you use is able to give you a good estimate of the uncertainty in its predictions, so you know how much reliance to place on these results and can see how they improve as more data is added. Alchemite uses high quality uncertainty quantificaton methods based on nonparametric probability distributions.

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Our customers say...

We could quickly drop a number of the ingredients, speeding up development

Yili optimised development of dairy product formulations.

We identified a route requiring five times fewer experiments

Johnson Matthey found improved formulation designs for catalysts

In total it gives us nearly 40% cost savings in manufacturing and testing

Voestalpine gained insight into AM alloy development

What is nice is that it uses both chemical and experimental information

Genentech: learn from drug discovery datasets to inform R&D decisions

We improved performance by 50% – and we were able to get there faster

FUCHS applied Alchemite™ to lubricant formulation development

Alchemite™ reduced the formulation timescale by
months

Domino Printing Sciences reduced timescales in development of new inks

It expands the bandwidth of our data scientists to apply materials informatics

NASA: supporting material and component design

The project provided us with insights that help us to improve steel properties

OCAS (ArcelorMittal): insights from process, property, and microstructure data

We can rapidly optimise process parameters for new powders and machines

Additive Manufacturing DOE with AMRC, Boeing, Constellium, and GE Additive

Design of experiments made easy

Faster, fewer, better experiments

See how Alchemite™ for DOE cuts 50-80% from experimental workloads, while finding solutions that traditional DOE methods would miss.

Accelerate formulation innovation

Sharpen your competitive edge

Try Alchemite™ for Formulations and experience the fast track to winning formulations, materials, and chemicals.

Breakthrough insights

Apply the power of machine learning

Use Alchemite™ for R&D Insights to unlock the hidden value in your data and empower decision making.

Transform your R&D with Alchemite™

See for yourself how Alchemite™ accelerates innovation while saving time and cost.

Frequently asked questions

Whenever you have data that you might be able to learn from! If your data describes the inputs to a system and the resulting outputs, machine learning can build a model that gives you valuable insights. For example, it can tell you which inputs are most important in determining which outputs. It can predict outputs for a new set of inputs, enabling virtual experiments that provide a low effort way to test hypotheses. And it can suggest which inputs might achieve target outputs, helping you to design experiments and generate product ideas. Examples of the systems studied include formulations, materials, chemicals, manufacturing processes, and much more.
Three key factors you need to consider are: (1) Can the approach generate a useful model even if data has gaps (is sparse) or is noisy – as much R&D data is? (2) Does it have excellent capabilities to understand and use the uncertainty in its model? (3) Is it easy-to-use for the specific R&D tasks I have in mind (like DOE or formulations)? Alchemite machine learning meets these criteria.
Alchemite™ Suite
Technology
The results from machine learning will, of course, depend on the data input to the model. The more data and the better the quality of that data, the more reliable the machine learning predictions will be. What is very important is that the ML method you use is able to give you a good estimate of the uncertainty in its predictions, so you know how much reliance to place on these results and can see how they improve as more data is added. Alchemite uses high quality uncertainty quantificaton methods based on nonparametric probability distributions.