首页|Researchers from Council of Scientific and Industrial Research (CSIR) National Chemical Laboratory Report Findings in Machine Learning (Solid State Hydrogen Storage: Decoding the Path Through Machine Learning)
Researchers from Council of Scientific and Industrial Research (CSIR) National Chemical Laboratory Report Findings in Machine Learning (Solid State Hydrogen Storage: Decoding the Path Through Machine Learning)
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NETL
NSTL
Elsevier
Investigators publish new report on Machine Learning. According to news reporting from Pune, India, by NewsRx journalists, research stated, "We present a machine learning (ML) framework HEART (HydrogEn storAge propeRty predicTor) for identifying suitable families of metal alloys for hydrogen storage under ambient conditions. Our framework includes two ML models that predict the hydrogen storage capacity (HYST) and the enthalpy of hydride formation (THOR) of multi-component metal alloys." Financial supporters for this research include Council of Scientific & Industrial Research (CSIR) - India, Department of Science & Technology (India).
PuneIndiaAsiaAlloysCyborgsElementsEmerging TechnologiesGasesHydrogenInorganic ChemicalsMachine LearningCouncil of Scientific and Industrial Research (CSIR) National Chemical Laboratory
NETL
NSTL
Elsevier
