有监督学习算法在材料科学中的应用

Application of Supervised Learning Algorithms in Materials Science

【目的】本文希望对近年来机器学习在材料学研究中的应用做一概略的介绍,为相关的研究提供一定的参考。【文献范围】本文主要参考引述了近几年来材料数据库相关文献,以及使用机器学习算法进行材料性能预测、发现新材料的研究论文。【方法】本文介绍了有监督机器学习的处理流程,并介绍了多种有监督机器学习算法在材料科学中的应用现状。【结果】机器学习算法,帮助总结了材料性能与材料的组成元素、晶格结构等的规律,对发现新材料具有重要的意义,而机器学习力场方法则展现出处理复杂的相变、界面等问题的潜力【局限】鉴于目前掌握的研究水平,主要重点介绍的是有监督机器学习方法在材料性能预测等几个领域的应用,对于无监督学习以及其他材料研究领域的引述尚缺乏。【结论】这是一个新兴的领域,未来将成为材料科学的一个重要组成部分。

[Objective]This article aims to provide a brief overview of the applications of machine learning in materials research in recent years,offering a reference for related studies.[Literature Scope]Therefore,this article mainly references recent literature and materials databases and research papers utilizing machine learning algorithms for material property prediction and new material discovery.[Methods]The article introduces the workflow of supervised machine learning and presents the current applications of various supervised machine learning algorithms in materials science.[Results]Machine learning algorithms help to identify patterns between material properties and factors such as composition elements and crystal structures,making them significant in the discovery of new materials.Additionally,force field methods using machine learning demonstrate potential in addressing complex phenomena like phase transitions and interfaces.[Limitations]Due to the limitations of the author’s expertise,the focus of the article is primarily on the application of supervised machine learning methods in material property prediction and a few other areas.Citations regarding to unsupervised learning and other research fields in materials science are currently inadequate.[Conclusions]This is an emerging field that is expected to become an important component of materials science in the future.