Abstract:
The success of machine learning approaches to high throughput screening for
drug discovery in the pharmaceutical industry has inspired similar schemes
for materials discovery [8, 13]. One of the challenges in adopting a datadriven
approach to materials discovery is the lack of large experimental data,
especially in the space of inorganic materials. To overcome this problem,
hybrid approaches have been developed in which properties calculated from
crystal structure by ab initio density functional theory are combined with
experimental data [18]. These hybrid approaches have the best of both worlds
in that erroneous DFT property calculations and limited experimental data
compensate for each other.
