Machine learning reveals orbital interaction in materials

We propose a novel representation of materials named an ‘orbital-field matrix (OFM)’, which is based on the distribution of valence shell electrons. We demonstrate that this new representationcan be highly useful in mining material data. Experimental investigation shows that the formationenergies of crystalline materials, atomization energies of molecular materials, and local magneticmoments of the constituent atoms in bimetal alloys of a lanthanide metal and transition-metal can be predicted with high accuracy using the OFM. Knowledge regarding the role of the coordinationnumbers of the transition-metal and lanthanide elements in determining the local magnetic moments of the transition-metal sites can be acquired directly from decision tree regressionanalyses using the OFM.