Use Efficient Data Collection Tools
Increase the performance of LED materials by leveraging data sets from literature and theoretical repositories. Receive curated sets, insert them into initial machine learning (ML) recommendation models, fabricate LED materials based on the experimental recommendations, and finally, characterize them. Use an ML feedback loop that allows for an efficient and supervised improvement of LED materials.
Connect, Standardize and Visualize Complex LED Data
Connect fabrication, testing, and characterization instruments of LED materials used in academic laboratories, industrial laboratories, and production lines to the online platform. Tag, parse, standardize, and visualize complex data sets from the instruments and securely access them from any device.
Use Custom Apps (Analysis Tools)
Use customized LED measurement apps that meet your research or company’s unique needs and save valuable R&D time. Together with MZ's scientific team, design and manage apps that perform advanced calculations, modeling, and analyses on large and sophisticated LED data sets.
Accelerate LED Research Using Machine Learning
Benefit from a unique and innovative approach to discovering and improving LED materials. It relies on a flow that connects four critical research aspects: Material properties (density, molecular weight, thermal conductivity, electron mobility, etc.), preparation properties (temperature, pressure, etc.), structure properties (crystal size, lattice constants), and function properties (lifetime, emission bands, color points, efficiency, etc.). Find connections between these aspects and make predictions.