Prediction and Classification of Formation Energies of Binary Compounds by Machine Learning: An Approach without Crystal Structure Information

It is well believed
that machine learning models could help to
predict the formation energies of materials if all elemental and crystal
structural details are known. In this paper, it is shown that even
without detailed crystal structure information, the formation energies
of binary compounds in various prototypes at the ground states can
be reasonably evaluated using machine-learning feature abstraction
to screen out the important features. By combining with the “white-box”
sure independence screening and sparsifying operator (SISSO) approach,
an interpretable and accurate formation energy model is constructed.
The predicted formation energies of 183 experimental and 439 calculated
stable binary compounds (Ehull = 0) are
predicted using this model, and both show reasonable agreements with
experimental and Materials Project’s calculated values. The
descriptor set is capable of reflecting the formation energies of
binary compounds and is also consistent with the common understanding
that the formation energy is mainly determined by electronegativity,
electron affinity, bond energy, and other atomic properties. As crystal
structure parameters are not necessary prerequisites, it can be widely
applied to the formation energy prediction and classification of binary
compounds in large quantities.