Machine Learning-Aided Design of Materials with Target Elastic Properties

A set of universal descriptors which
combines atomic properties
with crystal fingerprint are presented to build interpretable models
for elastic property prediction. Using the well-performed model, 100
materials with large predicted elastic moduli are screened out and
then validated by the first-principles calculations. When performing
projection analysis, we find that compounds with large and small elastic
moduli are clearly divided into two parts by the average value of
volume and atomization enthalpy (ΔHatomic), and the relation between them is given by two discriminant equations,
suggesting that compounds composed of elements with large ΔHatomic are potential large elastic moduli materials.
Following this rule, we design several new stable materials like ReTcB4 and ReB which have high elastic moduli. This method is valuable
for high-throughput screening and material design.