Repositorio UFRO · Omeka S

Supervised machine learning-based multi-class phase prediction in high-entropy alloys using robust databases

Primer Autor	Onate, Angelo
Co-autores	Sanhueza, Juan Pablo Zegpi, Diabb Tuninetti, Victor Ramirez, Jesus Medina, Carlos Melendrez, Manuel Rojas, David
Título	Supervised machine learning-based multi-class phase prediction in high-entropy alloys using robust databases
Editorial	ELSEVIER SCIENCE SA
Revista	JOURNAL OF ALLOYS AND COMPOUNDS
Lenguaje	en
Resumen	This work evaluated the phase prediction capability of high entropy alloys using four supervised machine learning models K-Nearest Neighbors (KNN), Multinomial Regression, Extreme Gradient Boosting (XGBoost), and Random Forest. The study addresses the challenge of predicting multicomponent alloys by considering the overlapping of multicategorical stability parameters. Eight prediction classes (FCC, BCC, FCC+BCC, FCC+Im, BCC+Im, FCC+BCC+Im, Im and AM) were used. Finally, the predicted results were compared with those of two new alloys fabricated by induction melting in a controlled atmosphere using X-ray diffraction (XRD). The results indicate that with a robust database, appropriate data treatment, and training, satisfactory and competitive prediction indicators can be obtained with traditional machine learning predictions based on four prediction classes: Solid Solution (SS), Solid Solution with Intermetallic (SS+Im), intermetallic (Im), and amorphous (AM). The best predictive model obtained from the four evaluated models was Random Forest, with an accuracy of 72.8% and ROC AUC of 93.1%.
Fecha Publicación	2023
Tipo de Recurso	artículo original
doi	10.1016/j.jallcom.2023.171224
Formato Recurso	PDF
Palabras Claves	Phase prediction High entropy alloys Machine Learning Intermetallics prediction
Ubicación del archivo	http://dx.doi.org/10.1016/j.jallcom.2023.171224
Categoría OCDE	Química Ciencia de Materiales Metalurgia e Ingeniería Metalúrgica
Materias	Predicción de fase Aleaciones de alta entropía Aprendizaje automático Predicción de intermetálicos
Identificador del recurso (Mandatado-único)	artículo original
Versión del recurso (Recomendado-único)	versión publicada
Derechos de acceso	metadata
Access Rights	metadata
Id de Web of Science	WOS:001036419600001
ISSN	0925-8388
Tipo de ruta	hibrida
Categoría WOS	Química Ciencia de Materiales Metalurgia e Ingeniería Metalúrgica
Referencia del Financiador (Mandatado si es aplicable-repetible)	ANID-FONDECYT 1221600 UdeC 220.098.005-INV WBI/AGCID RI02 (DIE23-0001) UFRO DI22-0067 ANID FONDECYT 1221600