Automated Modelling of Material Chemistry Applying High-Throughput Formulation and Machine Learning

Abstract

High-throughput technology has been evolved from simple pipetting tasks for example in pharmaceutical research [1] to more complex workflows for material sample preparation and characterization [2]. This accelerated the research and development of new materials by automated repetitive cycles and allowed a deep analysis of the multi-dimensional experimental space based on variation of the raw material synthesis and formulation.