Recently, 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...

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.

<div><div><div><p>Recently, 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 size ranging from 31 to 70 atoms. DART model is designed based on the principle that 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 identification of ground-state structures without geometry optimization. Albeit using topological descriptor, DART achieves MAE of 3.59 kcal/mol (0.15 eV) on testset. 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 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.</p></div></div></div>

Introduction: Corona virus disease- 2019 is caused by severe acute respiratory syndrome- 2 (SARS-CoV-2), also known as Novel corona virus (2019-nCoV).The disease presentation ranges from asymptomatic to severe acute respiratory failure requiring intensive care support. There are multiple drugs, therapies, and vaccine trials underway. One such therapy is convalescent plasma. Passive immunity delivered as anti-corona virus antibodies from convalescent human plasma has promise of emerging as a therapeutic option in the treatment of SARS-CoV-2. Objective: The objective of the study is to determine blood group distribution among covid -19 convalescent plasma donors in the Department of blood bank, Rajendra Institute of Medical Sciences, Ranchi, Jharkhand. Materials and Methods:Aretrospective study was conducted at Department of blood st st bank, Rajendra Institute of Medical Sciences, Ranchi, Jharkhand over a period of 3 months from 01 August to 31 October 2020 who have donated at least one unit of convalescent plasma. Results: This study recorded greater number of male donors with B Positive blood group. The convalescent plasma donors were mostly middle aged between 30 to 50 years of age.