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UnknownDrawing a materials map with an autoencoder for lithium ionic conductors.
Efforts to optimize known materials and enhance their performance are ongoing, driven by the advancements resulting from the discovery of novel functional materials. Traditionally, the search for and optimization of functional materials has relied on the experience and intuition of specialized resea...
S-EPMC10556005 | biostudies-literature
UnknownComputational investigation of the Mg-ion conductivity and phase stability of MgZr4(PO4)6.
Solid electrolyte materials exhibiting high Mg-ion conductivity are required to develop Mg-ion batteries. In this study, we focused on a Mg-ion-conducting solid phosphate based electrolyte, MgZr4(PO4)6 (MZP), and evaluated the ionic conductivity of NASICON-type and β...
S-EPMC9063713 | biostudies-literature
UnknownDeep learning based emulator for predicting voltage behaviour in lithium ion batteries.
This study presents a data-driven battery emulator using long short-term memory deep learning models to predict the charge-discharge behaviour of lithium-ion batteries (LIBs). This study aimed to reduce the economic costs and time associated with the fabrication of large-scale automotive prototype b...
S-EPMC11582594 | biostudies-literature
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