Materials design and discovery can be represented as selecting the optimal structure from a space of candidates that optimizes a target property. The selection is an iterative process, where selected candidate in one iteration is evaluted and fedback to the process for a more informed selection in the next iteration. Since the number of candidates can be exponentially proportional to the structure determination variables, the efficiency to obtain the optimal structure is a critical issue. We use Monte Carlo tree search (MCTS) approach in combination with expansion policy neural network to accelerate this process. MCTS has no tuning parameters and works autonomously in various problems. Additionally, it does not require pre- availalbe training data and it is highly scalble making it good fit for large spaces problems such as the chemical and mateirals space.