Rational design of materials with high-throughput computing and machine learning has made outstanding progress in the last decade. This talk presents a few state-of-the-art design examples where machine learning was helpful to obtain high-performing materials and structures. Then, these examples are used to motivate new methodological developments for inverse design that show substantial improvements when compared to conventional topology optimization.