  Modification of physical properties of materials and design of materials with
on-demand characteristics is at the heart of modern technology. Rare
application relies on pure materials--most devices and technologies require
careful design of materials properties through alloying, creating
heterostructures of composites or controllable introduction of defects. At the
same time, such designer materials are notoriously difficult for modelling.
Thus, it is very tempting to apply machine learning methods for such systems.
Unfortunately, there is only a handful of machine learning-friendly material
databases available these days. We develop a platform for easy implementation
of machine learning techniques to materials design and populate it with
datasets on pristine and defected materials. Here we describe datasets of
defects in represented 2D materials such as MoS2, WSe2, hBN, GaSe, InSe, and
black phosphorous, calculated using DFT. Our study provides a data-driven
physical understanding of complex behaviors of defect properties in 2D
materials, holding promise for a guide to the development of efficient machine
learning models. In addition, with the increasing enrollment of datasets, our
database could provide a platform for designing of materials with predetermined
properties.
