首页|Imperial College London Reports Findings in Machine Learning (Na Vacancy-Driven Phase Transformation and Fast Ion Conduction in W-Doped Na3SbS4 from Machine Lea rning Force Fields)
Imperial College London Reports Findings in Machine Learning (Na Vacancy-Driven Phase Transformation and Fast Ion Conduction in W-Doped Na3SbS4 from Machine Lea rning Force Fields)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting out of London, United Kingdom , by NewsRx editors, research stated, "Solid-state sodium batteries require effe ctive electrolytes that conduct at room temperature. The NaPnCh (Pn = P, Sb; Ch = S, Se) family has been studied for their high Na ion conductivity." Our news journalists obtained a quote from the research from Imperial College Lo ndon, "The population of Na vacancies, which mediate ion diffusion in these mate rials, can be enhanced through aliovalent doping on the pnictogen site. To probe the microscopic role of extrinsic doping and its impact on diffusion and phase stability, we trained a machine learning force field for Na W Sb S based on an e quivariant graph neural network. Analysis of large-scale molecular dynamics traj ectories shows that an increased Na vacancy population stabilizes the global cub ic phase at lower temperatures with enhanced Na ion diffusion and that the expli cit role of the substitutional W dopants is limited. In the global cubic phase, we observe large and long-lived deviations of atoms from the averaged symmetry, echoing recent experimental suggestions."
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