Multi-reward Reinforcement Learning Based Bond-Order Potential to Study Strain-Assisted Phase Transitions in Phosphorene

We introduce a multi-reward reinforcement learning (RL) approach to train a flexible bond-order potential (BOP) for 2D phosphorene based on ab initio training data sets. Our approach is based on a continuous action space Monte Carlo tree search algorithm that is general and scalable and presents an efficient multiobjective optimization scheme for high-dimensional materials design problems. As a proof-of-concept, we deploy this scheme to parametrize multiple structural and dynamical properties of 2D phosphorene polymorphs. Our RL-trained BOP model adequately captures the structure, energetics, transformation barriers, equation of state, elastic constants, and phonon dispersions of various 2D P polymorphs. We use this model to probe the impact of temperature and strain rate on the phase transition from black (alpha-P) to blue phosphorene (beta-P) through molecular dynamics simulations. A decrease in critical strain for this phase transition with increase in temperature is observed, and the underlying atomistic mechanisms are discussed.

Automated summary

In this study, a multi-reward reinforcement learning approach is introduced for training a flexible bond-order potential for 2D phosphorene. The approach combines a continuous action space Monte Carlo tree search algorithm with an efficient multiobjective optimization scheme, successfully capturing various properties of 2D phosphorene polymorphs. Molecular dynamics simulations reveal the impact of temperature and strain rate on a phase transition, with a decrease in critical strain observed with increasing temperature. The underlying atomistic mechanisms are discussed.