Autonomous molecular design by Monte-Carlo tree search and rapid evaluations using molecular dynamics simulations

Functional materials, especially those that largely differ from known materials, are not easily discoverable because both human experts and supervised machine learning need prior knowledge and datasets. An autonomous system can evaluate various properties a priori, and thereby explore unknown extrapolation spaces in high-throughput simulations. However, high-throughput evaluations of molecular dynamics simulations are unrealistically demanding. Here, we show an autonomous search system for organic molecules implemented by a reinforcement learning algorithm, and apply it to molecular dynamics simulations of viscosity. The evaluation is dramatically accelerated (by three orders of magnitude) using a femto-second stress-tensor correlation, which underlies the glass-transition model. We experimentally examine one of 55,000 lubricant oil molecules found by the system. This study indicates that merging simulations and physical models can open a path for simulation-driven approaches to materials informatics. Identifying novel functional materials either manually or by using algorithm-based analytics is a complex and resource demanding task. Here, the authors present an autonomous search system which combines Monte Carlo tree search and molecular dynamic simulations in order to successfully identify organic molecules based on their viscosity for use as industrial lubricants.