Abstract
Designing novel materials and analyzing their properties is a computation intensive process. Increasingly modern machine learning techniques are being exploited in contemporary research to expedite and advance materials studies. One powerful tool available to researchers is the body of scientific knowledge that aids in selecting design models, algorithms, meta-data, and visualization tools to process and analyze experimental and empirical data for an iterative design process, potentially involving a human in the loop, In this preliminary research paper, our goal is to introduce a new machine learning platform, called MatFlow, for automated and knowledge driven design of novel materials and their usage. We outline its architecture and illustrate its functionality with an application in Transition Metal Dichalcogenide (TMD) Heterostructures design of electronic and energy devices.