Optimizing 3D structure of H2O molecule using DDPG

Soo Kyung Kim, Peggy Li, Joanne Taery Kim, Piyush Karande, Yong Han

Keywords: Material Design, Reinforcement Learning, Molecular structure

Abstract: Conventional methods to predict 3D structure of molecule are based on iterative stochastic optimization techniques based on energy calculation using physics-based electronic structure modeling such as DFT or MD. Therefore, computing cost of physics-based modeling is significantly depended by the number of iterations to calculate energy until the total energy of structure is converged. As the cost-efficient alternatives, we propose a novel RL-based algorithm to optimize 3D structure of single H2O molecule based on DDPG (Deep Deterministic Policy Gradient) method. To demonstrate the efficiency of our model, we predicted 3D structure of H2O molecule and compared with results from the conventional DFT calculation. Our experiments show that our model succeed to predict 3D structure of H2O molecule which is identical with the results from DFT calculation.