  Polymer matrix composites exhibit remarkable lightweight and high strength
properties that make them attractive for aerospace applications. Constituents'
materials such as advanced polymers and fibers or fillers with their
hierarchical structure embed these exceptional properties to the composite
materials. This hierarchical structure in multiple length scales provides an
opportunity for designing the composite materials for optimized properties.
However, the high dimensional design space for the constituents' materials and
architectures choice of the composites makes it a challenging design problem.
To tackle this high dimensional design space, a systematic, efficient approach
named mechanistic data science framework is proposed in this work to identify
the governing mechanisms of materials systems from the limited available data
and create a composite knowledge database. Our composite knowledge database
comprises the knowledge of polymers at the nanoscale with nano reinforcement,
the unidirectional structure at the microscale, and woven structure at
mesoscale mechanisms that can be further used for the part scale composite
design and analysis. The mechanistic data science framework presented in this
work can be further extended to other materials systems that will provide the
materials designer with the necessary tools to evaluate a new materials system
design rapidly.
