基于Boosting的Sm-Co合金最大能量积预测模型

Q3 Materials Science
A. Trostianchyn, I. Izonin, Z. Duriagina, R. Tkachenko, V. Kulyk, B. Havrysh
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引用次数: 0

摘要

本文旨在基于机器学习方法集成的提升策略来确定Sm-Co合金的最大能量积预测任务。本文研究了一种基于集成的方法来解决Sm-Co合金的最大能量积预测任务。由于经典的机器学习方法在解决回归问题时有时不能提供可接受的精度,作者研究了boosting ML模型,即梯度boosting。建立一个基于几个弱子模型的助推模型,每个子模型都考虑了先前子模型的误差,大大提高了问题解决的准确性。使用作者收集的实际数据集来确认所获得的结果。这项工作证明了将机器学习的集成策略应用于材料科学应用问题的高效性。实验确定,与经典的机器学习方法相比,在机器学习的助推模型上形成的Sm-Co合金的最大能量乘积的预测任务的求解精度最高。与机器学习的单一算法相比,机器学习的助推策略需要更多的计算和时间资源来实现模型的学习过程。这项工作证明了使用机器学习有效解决Sm-Co合金最大能量积预测任务的可能性。所研究的用于解决该问题的机器学习的助推模型提供了高精度的预测,这揭示了它们在解决应用于计算材料科学的问题中的几个优点。此外,使用Orange建模环境为使用所研究的方法提供了一个简单直观的界面。所提出的预测方法显著降低了研究昂贵的稀土金属(REM)基铁磁材料的时间和资源成本。作者收集并形成了一组关于预测Sm-Co合金最大能量乘积的数据。我们使用机器学习工具来解决这项任务。结果表明,与经典的机器学习方法相比,基于boosting模型的预测精度最高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Boosting-based model for solving Sm-Co alloy’s maximum energy product prediction task
This paper aims to decide the Sm-Co alloy’s maximum energy product prediction task based on the boosting strategy of the ensemble of machine learning methods. This paper examines an ensemble-based approach to solving Sm-Co alloy’s maximum energy product prediction task. Because classical machine learning methods sometimes do not supply acceptable precision when solving the regression problem, the authors investigated the boosting ML model, namely Gradient Boosting. Building a boosting model based on several weak submodels, each of which considers the errors of the prior ones, provides substantial growth in the accuracy of the problem-solving. The obtained result is confirmed using an actual data set collected by the authors. This work demonstrates the high efficiency of applying the ensemble strategy of machine learning to the applied problem of materials science. The experiments determined the highest accuracy of solving the forecast task for the maximum energy product of Sm-Co alloy formed on the boosting model of machine learning in comparison with classical methods of machine learning. The boosting strategy of machine learning, in comparison with single algorithms of machine learning, requires much more computational and time resources to implement the learning process of the model. This work demonstrated the possibility of effectively solving Sm-Co alloy’s maximum energy product prediction task using machine learning. The studied boosting model of machine learning for solving the problem provides high accuracy of prediction, which reveals several advantages of their use in solving issues applied to computational material science. Furthermore, using the Orange modelling environment provides a simple and intuitive interface for using the researched methods. The proposed approach to the forecast significantly reduces the time and resource costs associated with studying expensive rare earth metals (REM)-based ferromagnetic materials. The authors have collected and formed a set of data on predicting the maximum energy product of the Sm-Co alloy. We used machine learning tools to solve the task. As a result, the most increased forecasting precision based on the boosting model is demonstrated compared to classical machine learning methods.
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来源期刊
Archives of materials science and engineering
Archives of materials science and engineering Materials Science-Materials Science (all)
CiteScore
2.90
自引率
0.00%
发文量
15
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