首页|Findings from Polish Academy of Sciences Provide New Insights into Machine Learn ing (Machine Learning-based Predictions of Power Factor for Half-heusler Phases)
Findings from Polish Academy of Sciences Provide New Insights into Machine Learn ing (Machine Learning-based Predictions of Power Factor for Half-heusler Phases)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in Machine Learning. According to news reporting from Wroclaw, Poland, by NewsRx jo urnalists, research stated, "A support vector regression model for predictions o f the thermoelectric power factor of half-Heusler phases was implemented based o n elemental features of ions. The training subset was composed of 53 hH phases w ith 18 valence electrons."Financial support for this research came from Wroclaw Center for Networking and Supercomputing. The news correspondents obtained a quote from the research from the Polish Acade my of Sciences, "The target values were calculated within the density functional theory and Boltzmann equation. The best predictors out of over 2000 combination s regarded for the p-type power factor at room temperature are: electronegativit y, the first ionization energy, and the valence electron count of constituent io ns. The final results of support vector regression for 70 hH phases are compared with data available in the literature, revealing good ability to determine favo rable thermoelectric materials, i.e., VRhGe, TaRhGe, VRuSb, NbRuAs, NbRuBi, LuNi As, LuNiBi, TaFeBi, YNiAs, YNiBi, TaRuSb and NbFeSb."
WroclawPolandEuropeCyborgsEmergi ng TechnologiesMachine LearningSupport Vector RegressionPolish Academy of Sciences
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