Energy-GNoME

Energy-GNoME ID: OSWf48e92c0a7bd458e95876228acb2323d | UUID: f48e92c0-a7bd-458e-9587-6228acb2323d
ID	OSWf48e92c0a7bd458e95876228acb2323d
UUID	f48e92c0-a7bd-458e-9587-6228acb2323d
Label	Energy-GNoME
Machine compatible name	EnergyGnome
Types/Categories	Dataset
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Details
Types/Categories<br>Definition: Entity, Item, Data, Dataset: Dataset Description<br>Definition: Entity: The Energy-GNoME database was developed to identify and predict materials suitable for energy applications, such as thermoelectrics, cathodes, and perovskites. The process combines machine learning (ML) techniques with an iterative active learning approach, enabling continuous integration and refinement. Label(s)At least one label is required.<br>Definition: Entity: Energy-GNoME URL / WebsitesLanding page(s) that documents the dataset<br>Definition: Dataset: https://paolodeangelis.github.io/Energy-GNoME/

Description

The Energy-GNoME database was developed to identify and predict materials suitable for energy applications, such as thermoelectrics, cathodes, and perovskites. The process combines machine learning (ML) techniques with an iterative active learning approach, enabling continuous integration and refinement.

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jsondata

type	"Category:OSWfe72974590fd4e8ba94cd4e8366375e8"
url	"https://paolodeangelis.github.io/Energy-GNoME/"
themes	Empty array
uuid	"f48e92c0-a7bd-458e-9587-6228acb2323d"
label	text "Energy-GNoME" lang "en"
description	text "The Energy-GNoME database was developed to identify and predict materials suitable for energy applications, such as thermoelectrics, cathodes, and perovskites. The process combines machine learning (ML) techniques with an iterative active learning approach, enabling continuous integration and refinement." lang "en"
name	"EnergyGnome"