首页|Data on Machine Learning Discussed by Researchers at University of Science and T echnology of China (Size dependent lithium-ion conductivity of solid electrolyte s in machine learning molecular dynamics simulations)
Data on Machine Learning Discussed by Researchers at University of Science and T echnology of China (Size dependent lithium-ion conductivity of solid electrolyte s in machine learning molecular dynamics simulations)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New study results on artificial intelligence have been published. According to news reporting originating from Anhui, People's Re public of China, by NewsRx correspondents, research stated, "Solidstate electro lytes are key ingredients in next-generation devices for energy storage and rele ase." Funders for this research include National Natural Science Foundation of China; Chinese Academy of Sciences. The news correspondents obtained a quote from the research from University of Sc ience and Technology of China: "Machine learning molecular dynamics (MLMD) has s hown great promise in studying the diffusivity of mobile ions in solid-state ele ctrolytes, with much higher efficiency than conventional ab initio molecular dyn amics (AIMD). In this work, we combine an efficient embedded atom neural network (EANN) approach and an uncertainty-driven active learning algorithm that optima lly selects data points from high-temperature AIMD trajectories to construct ML potentials for solid-state electrolytes and validate this strategy in a benchmar k system, Li3YCl6, for which several controversy theoretical results exist. Thro ugh systematic MLMD simulations, we find that a typically used small supercell i n AIMD simulations fails to predict the supersonic transition at a critical temp erature, leading to a significant overestimation of the Li+ conductivity in Li3Y Cl6 at room temperature. Fortunately, thanks to the scalability of the EANN pote ntial, extended MLMD simulations in a sufficiently large cell does yield a notab le change of temperature-dependence in conductivity at 420 K and a much lower ro om-temperature conductivity in excellent with experiment. Interestingly, our res ults are all based on a semi-local PBE density functional, which was argued unab le to predict the superionic transition. We analyze possible reasons of the seem ingly inconsistent MLMD results reported in literature with different ML potenti als."
University of Science and Technology of ChinaAnhuiPeople's Republic of ChinaAsiaCyborgsElectrolytesEmerging TechnologiesInorganic ChemicalsMachine LearningMolecular DynamicsPhysics
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