Machine learning speeds up prediction of materials' spectral properties

Many techniques in computational materials science require scientists to identify the right set of parameters that capture the physics of the specific material they are studying. Calculating these parameters from scratch is sometimes possible but costs a lot of time and computational power. Consequently, scientists are always eager to find more efficient ways to estimate them without doing the full calculation.