MeGen - generation of gallium metal clusters using reinforcement learning

Abstract

The generation of low-energy 3D structures of metal clusters depends on the efficiency of the search algorithm and the accuracy of inter-atomic interaction description. In this work, we formulate the search algorithm as a Reinforcement Learning (RL) problem. Concisely, we propose a novel actor-critic architecture that generates low-lying isomers of metal clusters at a fraction of computational cost than conventional methods. Our RL-based search algorithm uses a previously developed DART model as a reward function to describe the inter-atomic interactions to validate predicted structures. Using the DART model as a reward function incentivizes the RL model to generate low-energy structures and helps generate valid structures. We demonstrate the advantages of our approach over conventional methods for scanning local minima on potential energy surface (PES). Our approach not only generates isomer of gallium clusters at a minimal computational cost but also predicts isomer families that were not discovered through previous DFT-based approaches.

Deep reinforcement learning in chemistry: A review

“…Modee et al 48 state that generation of low‐energy 3D structures of metal clusters depends on the efficiency of the search algorithm and the accuracy of inter‐atomic interaction description. They formulate the search algorithm as a Reinforcement Learning (RL) problem.…”

MolOpt: Autonomous Molecular Geometry Optimization Using Multiagent Reinforcement Learning

“…Once trained NNPs can successfully circumvent the need to solve the electronic Schrödinger equation explicitly as it has learned the mapping f ( Z i , r i ) → E , where Z i are the nuclear charges and r i are the atomic positions. − Machine learning (ML) methods in general have been successful in improving computational chemistry algorithms leading to accelerated property prediction and chemical space exploration . Recently, much emphasis has been on developing efficient ML-based search algorithms to explore chemical space, − but the same is not the case for conformational space. There are very few attempts to develop an efficient ML-based search algorithm that can explore the conformational space, i.e., probe the potential energy surface (PES). − These ML-based search algorithms have applications in 3D structure generation , and molecular geometry optimization (MGO).…”