A unique combination
Value from sparse, noisy data
A self-consistent, iterative algorithm imputes sparse data. This enables Alchemite™ to solve real-world problems that are not accessible to traditional machine learning.
Optimise against multiple targets
Alchemite™ solves high-dimensional problems with multiple targets (for example, a formulation design) that were previously intractable, even when the training data is sparse and noisy
Accurate uncertainty quantification
Alchemite™ offers advanced uncertainty quantification based on nonparametric probability distributions for many properties simultaneously. You can more reliably identify the experimental route with the highest probability of success, focusing your time and effort based on a rational business case.
Scale up to large problems
Computations have a light memory and CPU footprint, so not only is Alchemite™ lightning fast, it can handle huge databases (where necessary).
Alchemite™ generates a model from the available data that you can then refine and apply. There is minimal need for data cleaning and model setup, removing the high dependence on starting assumptions that is typical of many methods.
A global view
Alchemite™ works across all available information to extract useful knowledge. This supports a global view (e.g., considering both ingredients and processing for a formulation), finding relationships that humans may miss.