  The representations of a compound, called "descriptors" or "features", play
an essential role in constructing a machine-learning model of its physical
properties. In this study, we adopt a procedure for generating a systematic set
of descriptors from simple elemental and structural representations. First it
is applied to a large dataset composed of the cohesive energy for about 18000
compounds computed by density functional theory (DFT) calculation. As a result,
we obtain a kernel ridge prediction model with a prediction error of 0.041
eV/atom, which is close to the "chemical accuracy" of 1 kcal/mol (0.043
eV/atom). The procedure is also applied to two smaller datasets, i.e., a
dataset of the lattice thermal conductivity (LTC) for 110 compounds computed by
DFT calculation and a dataset of the experimental melting temperature for 248
compounds. We examine the performance of the descriptor sets on the efficiency
of Bayesian optimization in addition to the accuracy of the kernel ridge
regression models. They exhibit good predictive performances.
