As materials researchers increasingly embrace machine-learning (ML) methods, it is natural
to wonder what lessons can be learned from other fields undergoing similar developments …

Organic molecules and polymers have a broad range of applications in biomedical,
chemical, and materials science fields. Traditional design approaches for organic molecules …

We have devised and implemented a novel computational strategy for de novo design of
molecules with desired properties termed ReLeaSE (Reinforcement Learning for Structural …

The game of Go has long been viewed as the most challenging of classic games for artificial
intelligence owing to its enormous search space and the difficulty of evaluating board …

Monte-Carlo Tree Search (MCTS) is a new best-first search guided by the results of Monte-
Carlo simulations. In this article, we introduce two progressive strategies for MCTS, called …

The game of checkers has roughly 500 billion billion possible positions (5× 1020). The task
of solving the game, determining the final result in a game with no mistakes made by either …

The ever-increasing demand for novel polymers with superior properties requires a deeper
understanding and exploration of the chemical space. Recently, data-driven approaches to …

Board games have long fascinated as mirrors of intelligence, skill, cunning, and wisdom.
While board games have been the topic of many scientific studies, and have been studied …

Monte Carlo tree search (MCTS) is an AI technique that has been successfully applied to
many deterministic games of perfect information. This paper investigates the application of …

Research in molecular sciences witnessed the rise and fall of Artificial Intelligence
(AI)/Machine Learning (ML) methods, especially artificial neural networks, few decades ago …