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Fermi Energy - Geometry - Deep Learning

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Use this model created via deep learning for fast and accurate predictions of Fermi energy using stoichiometry and unit cell parameters.

Load example values into the form.

Try it Yourself

Enter the corresponding values to try the approximation for yourself.

Specify the full stoichiometry
Unit cell A side length (Angstroms)
Unit cell B side length
Unit cell C side length
Unit cell α angle
Unit cell β angle
Unit cell γ angle

** Use of this web page reqiures correct Citing and attribution in any or all work and/or papers produced from results generated by this service.


You can access the single-attribute band gap predictor here:

URL format: /api/v{Version}/MachineLearning/FermiEnergy/Geometry


	"stoichiometry": "Cr4Cu1In1Se8",
	"geometryA": 7.642811,
	"geometryB": 7.643063,
	"geometryC": 7.643013,
	"geometryAlpha": 59.99344,
	"geometryBeta": 59.99952,
	"geometryGamma": 60.00009

JSON - response

    "fermiEnergy": 3.541726248357564780714516288,
	"geometryA": 7.642811,
	"geometryB": 7.643063,
	"geometryC": 7.643013,
	"geometryAlpha": 59.99344,
	"geometryBeta": 59.99952,
	"geometryGamma": 60.00009,
	"stoichiometry": "Cr4Cu1In1Se8"

XML - response

<FermiEnergyGeometryModel xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema">


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