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Data · dataset · 2024

Database of Ternary Amorphous Alloys Based on Machine Learning

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We develop a machine learning workflow to predict the critical casting diameter, glass transition temperature, and Young’s modulus for 45 ternary reported amorphous alloy systems.

Description

The predicted results have been organized into a database, enabling direct retrieval of predicted values based on compositional information.

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Where it is published

Catalogue records · 1

Topics

Inferred from text
Machine learning 74%
Provenance · 1 source records, 12 field assertions
SourceKeyLast seenRaw
ScienceDB10.57760/sciencedb.1309612 d agoJSON v1
FieldAssertionExtractorEvidence
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