This data repository provides the raw and standardized data for machine learning presented in the article entitled "Machine learning for hierarchical prediction of elastic properties in Fe-Cr-Al system" by the same authors. The data are generated by the cluster expansion method. The binary data, which are separated as the training set and test set, are used to construct the extremely randomized trees and deep neural networks models. And the ternary data of different temperatures are used for predictions
The ability to accurately predict the mechanical properties of metals is essential for their correct...
High strength alloys are materials with alloying additions designed to produce a specific combinatio...
Machine learning plays an important role in understanding and predicting the parameters of a microst...
This data repository provides the raw and standardized data for machine learning presented in the ar...
In this work we developed a microstructure database for a model alloy, i.e., Iron Chromium Alloy (Fe...
We apply machine learning algorithms to optimize thermodynamic and elastic properties of multicompon...
This paper presents the results obtained using Machine Learning (ML) algorithms to predict the mecha...
These data sets were used to develop machine-learning models to predict yield strength and hardness ...
Raw data and processed data used in the paper "A predicting model for properties of steel using the ...
This work aims to evaluate the predictive performance of various Machine Learning algorithms when a...
Data-driven algorithms for predicting mechanical properties with small datasets are evaluated in a c...
When considering data-driven modelling, uncertainties, errors and inconsistencies in the data can mo...
This study explores the use of machine learning (ML) as a data-driven approach to estimate hot ducti...
Machine learning approaches, enabled by the emergence of comprehensive databases of materials proper...
Machine learning has the potential to enhance damage detection and prediction in materials science. ...
The ability to accurately predict the mechanical properties of metals is essential for their correct...
High strength alloys are materials with alloying additions designed to produce a specific combinatio...
Machine learning plays an important role in understanding and predicting the parameters of a microst...
This data repository provides the raw and standardized data for machine learning presented in the ar...
In this work we developed a microstructure database for a model alloy, i.e., Iron Chromium Alloy (Fe...
We apply machine learning algorithms to optimize thermodynamic and elastic properties of multicompon...
This paper presents the results obtained using Machine Learning (ML) algorithms to predict the mecha...
These data sets were used to develop machine-learning models to predict yield strength and hardness ...
Raw data and processed data used in the paper "A predicting model for properties of steel using the ...
This work aims to evaluate the predictive performance of various Machine Learning algorithms when a...
Data-driven algorithms for predicting mechanical properties with small datasets are evaluated in a c...
When considering data-driven modelling, uncertainties, errors and inconsistencies in the data can mo...
This study explores the use of machine learning (ML) as a data-driven approach to estimate hot ducti...
Machine learning approaches, enabled by the emergence of comprehensive databases of materials proper...
Machine learning has the potential to enhance damage detection and prediction in materials science. ...
The ability to accurately predict the mechanical properties of metals is essential for their correct...
High strength alloys are materials with alloying additions designed to produce a specific combinatio...
Machine learning plays an important role in understanding and predicting the parameters of a microst...