Alchemite™ for R&D Insights
Maximise value from your data
Unlock the insights hidden in your data through the power of machine learning. In discovery, development, manufacturing, and beyond, Alchemite™ makes it quick and easy to learn from any dataset, creating models that you can apply to fill in missing information, find critical relationships, and guide decision making.
Our customers say...
Predictive power
In just a few button-clicks, Alchemite™ learns from your data and enables you to make predictions. Fill in gaps and find outliers. Predict likely outcomes for new scenarios. Identify optimal solutions for your system. Alchemite™ is a tool that gets your data working for you, fast.
Vital insights
The Alchemite™ ‘explainable AI’ tools enable you to gain deep insights, understanding what the machine learning has learned from your R&D data. See which inputs to a system are most important in determining the key outputs. Understand how a process will behave as inputs change. Prioritise data acquisition efforts to focus on the information that will generate most value.
Proven technology
Alchemite™ is a proven advanced machine learning method that works for real-world datasets, even where there are substantial gaps in the data – a scenario where conventional machine learning methods fail. Successful applications include chemicals, materials science, life sciences, manufacturing processes, foods, scientific databases, clinical studies, battery research, and more.
Recommended Solution
Individuals can pick the right Alchemite app for their R&D challenge. R&D organisations can match the right app to the right team member. Projects and results can be shared across all users, creating an integrated solution and enabling collaboration across your team.
Alchemite™ Explorer
Formulation scientists, materials researchers, and chemists can apply machine learning models to test hypotheses and explore relationships in their data.
Alchemite™ Innovator
Research scientists can combine predictive tools with a quick and easy method to design experimental programmes, for a complete ML project toolset.
Alchemite™ Architect
System architects and data scientists can access the full power of the Alchemite ML method and integrate it into lab systems, analysis tools, and workflows.
Further resources
Find out more about the Alchemite™ method and its applications
Mind the gap – working with real experimental data](https://intellegens.com/mind-the-gap-working-with-real-experimental-data/)
7 Examples of How Materials & Chemicals Companies Innovate with AI](https://intellegens.com/materials-chemicals-companies-innovate-with-ai/)
Five ways machine learning can power life science data analysis](https://intellegens.com/five-ways-machine-learning-can-power-life-science-data-analysis/)
Alchemite™ for R&D Insights - frequently asked questions...
How can machine learning help me get more insight from my R&D data?
Machine learning models capture the complex relationships in your data. You can then analyze the outputs of the models to help you understand these relationships and use them predictively to test hypotheses or identify which inputs are most likely to generate a desired output. You extract maximum value from your data and use this data to drive your decision-making. This makes ML very useful for any of a broad range of problems including optimising chemicals, formulations, materials, or manufacturing and other processes.
Are machine learning models black boxes?
In one sense, yes. You cannot inspect the ‘equation’ underlying the machine learning model in the way that you can with other analytical techniques. However, two things can ensure that ML models do not function as black boxes. The first is an accurate estimation of the uncertainty in their predictions – this helps you to understand what reliance to place on ML results and to see how new data impacts that reliability. The second is a suite of ‘Explainable AI’ analytics. For example, importance charts apply the model to make predictions and then show you in detail which inputs to a system drive which of the outputs. This allows you to understand both your data and the relationships that have been captured in the model. The advantage of ML is that it learns from data without needing a predefined analytical model and that it can capture very complex combinations of relationships.
What insights can I get from Alchemite machine learning?
Key types of insight include: (1) Which inputs to your system influence which outputs, and how. (2) What outputs a new set of inputs are likely to return (virtual experimentation). (3) Which inputs are most likely to return the outputs I want (optimization). (4) What new data I should add to best improve the quality of my model (experimental design).
Can I trust the data insights I get from machine learning?
The quality of these insights will, of course, depend on the data input to the model. Make sure that the 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 quantification methods based on nonparametric probability distributions.