The scientific process of discovering new knowledge is often characterized as search through a space of candidate hypotheses.
Machine learning can accelerate the search by properly modeling the data and determining which candidate on which to apply an experiment.
In many cases, experiments can be substituted by first principles calculations. Automated search algorithms such as Bayesian optimization and Monte Carlo tree search can be used to increase efficiency in designing molecules and materials. I report successful case studies in discovery of low LTC compounds, grain boundary optimization, automated design of Si-Ge superlattices and RNA inverse folding.