01768nas a2200205   4500008004100000245007600041210006900117260000900186300001100195490000600206520109400212653002101306653002701327653002501354100001701379700001901396700001801415700002101433856010801454       2023                        eng d00aMeGen-generation of gallium metal clusters using reinforcement learning0 aMeGengeneration of gallium metal clusters using reinforcement le  cJUN   a0250320 v43 a<p>
	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. 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 density-functional theory (DFT)-based approaches.</p>
10agallium clusters10areinforcement learning10astructure generation1 aModee, Rohit1 aVerma, Ashwini1 aJoshi, Kavita1 aPriyakumar, Deva  uhttps://library.ncl.res.in/content/megen-generation-gallium-metal-clusters-using-reinforcement-learning