02619nas a2200193   4500008004100000022001400041245016300055210006900218260000900287300001600296490000700312520188200319100001702201700002002218700001902238700001802257700002102275856012902296       2021                        eng d  a1463-907600aDART: deep learning enabled topological interaction model for energy prediction of metal clusters and its application in identifying unique low energy isomers0 aDART deep learning enabled topological interaction model for ene  cOCT   a21995-220030 v233 aRecently, machine learning (ML) has proven to yield fast and accurate predictions of chemical properties to accelerate the discovery of novel molecules and materials. The majority of the work is on organic molecules, and much more work needs to be done for inorganic molecules, especially clusters. In the present work, we introduce a simple topological atomic descriptor called TAD, which encodes chemical environment information of each atom in the cluster. TAD is a simple and interpretable descriptor where each value represents the atom count in three shells. We also introduce the DART deep learning enabled topological interaction model, which uses TAD as a feature vector to predict energies of metal clusters, in our case gallium clusters with sizes ranging from 31 to 70 atoms. The DART model is designed based on the principle that the energy is a function of atomic interactions and allows us to model these complex atomic interactions to predict the energy. We further introduce a new dataset called GNC_31-70, which comprises structures and DFT optimized energies of gallium clusters with sizes ranging from 31 to 70 atoms. We show how DART can be used to accelerate the process of identification of low energy structures without geometry optimization. Albeit using a topological descriptor, DART achieves a mean absolute error (MAE) of 3.59 kcal mol(-1) (0.15 eV) on the test set. We also show that our model can distinguish core and surface atoms in the Ga-70 cluster, which the model has never encountered earlier. Finally, we demonstrate the transferability of the DART model by predicting energies for about 6k unseen configurations picked up from molecular dynamics (MD) data for three cluster sizes (46, 57, and 60) within seconds. The DART model was able to reduce the load on DFT optimizations while identifying unique low energy structures from MD data.1 aModee, Rohit1 aAgarwal, Sheena1 aVerma, Ashwini1 aJoshi, Kavita1 aPriyakumar, Deva  uhttps://library.ncl.res.in/content/dart-deep-learning-enabled-topological-interaction-model-energy-prediction-metal-clusters01768nas 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