Case Study with Matmatch
“We were most impressed by the results we were able to achieve using the Intellegens Alchemite tool to improve the Matmatch material data set. The outcome exceeded our expectations and we succeeded in predicting and filling significantly more properties than expected across different material categories despite starting with a very sparse dataset. The Intellegens team was a pleasure to work with and we look forward to continued cooperation.”
– Melissa Albeck, CEO at Matmatch.
Key outcomes
- Alchemite™ delivered significant value and potential for additional revenue to Matmatch through data checking, estimation, and gap filling.
- Alchemite™ could populate sparse and missing data and successfully model complex, multi-dimensional data.
- Data scientists from Matmatch found it easy to access the full power of Alchemite™ and to integrate it well with their system.
Summary
Selecting materials with the right properties for a project is critical. Matmatch makes this process easier by providing comprehensive databases of materials and their properties, with data sourced from materials suppliers. In practice, however, not all characteristics are known for each material, which is why Intellegens’ machine learning tools can add value. Alchemite™ can help to fill the gaps, detect outliers, support material selection, and even identify the gaps in the property space that might be targets for new materials development.
This case study describes the ability of Alchemite™ to populate missing material property data and add significant value to Matmatch’s services. It also shows that Intellegens’ machine learning tools were easily integrated with Matmatch’s systems and that their data scientists could effortlessly build complex models of multi-dimensional data with Alchemite™ software.