NASA – material and component design | Alchemite™ gives accurate predictions Johnson Matthey – automotive catalysts | Reduced the amount of experiment required Domino Printing Sciences – ink formulation | Maximised value from lab resources AMRC and Boeing - process parameters for AM | Reduce experimentation Optibrium and AstraZeneca - drug discovery | Successfully predicts pharmacokinetics Rolls-Royce - aerospace alloys | New alloy design satisfying 11 property criteria Welding Alloys Group – hardfacing materials | Improved cost, performance, eco impact Optibrium and Genentech - drug discovery | Accurate models despite sparse data GKN Aerospace - alloy design | Virtual experiments deliver results in seconds
Alchemite™ machine learning identifies potential antimalarial drug compound With Optibrium and Open Source Malaria | Now published in the Journal of Medicinal Chemistry Second Focus Group brings together growing Alchemite™ community Nov 2021. The Second Alchemite™ Focus Group shared experiences of applying machine learning. Imputation of sensory properties using deep learning With Optibrium and IFF | Now published in the Journal of Computer-Aided Molecular Design
Transforming materials and process development with machine learning A recorded webinar featuring Dr Richard Padbury from materials technology experts Lucideon. Alchemite™ deep learning – solving complex problems with real-world data Intellegens white paper. An overview of the Alchemite™ deep learning technology. Alchemite™ powered deep learning with "small data" Intellegens white paper. Strategies for applying machine learning when limited data is available.
