Exploiting Machine Learning in Multiscale Modelling of Materials
Exploiting Machine Learning in Multiscale Modelling of Materials
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DOI：
10.1007/s40033-022-00424-z
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发表时间：
2022-11
影响因子：
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通讯作者：
G. Anand;Swarnava Ghosh;Liwei Zhang;Angesh Anupam;Colin L. Freeman;C. Ortner;M. Eisenbach;J. Kermode
中科院分区：
文献类型：
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作者：
G. Anand;Swarnava Ghosh;Liwei Zhang;Angesh Anupam;Colin L. Freeman;C. Ortner;M. Eisenbach;J. Kermode
高效机器学习算法的最新发展引起了材料界的极大兴趣。材料科学与工程中固有的复杂性和多尺度问题带来了巨大的挑战。目前材料科学中的机器学习研究有一个明显的空白，高效的算法正在作为一个单独的努力来开发，而这些方法正在被其他人作为“黑箱”模型来应用。本文旨在讨论与多尺度材料建模各个方面的机器学习算法的开发和应用相关的问题。作者提出了一个概述的机器学习的等变属性，机器学习辅助统计力学，将ofab initio方法在多尺度模型的材料加工和应用机器学习的不确定性量化。除了上述，贝叶斯方法的多尺度建模的适用性进行了讨论。还讨论了与多尺度材料建模相关的关键问题。
Recent developments in efficient machine learning algorithms have spurred significant interest in the materials community. The inherently complex and multiscale problems in Materials Science and Engineering pose a formidable challenge. The present scenario of machine learning research in Materials Science has a clear lacunae, where efficient algorithms are being developed as a separate endeavour, while such methods are being applied as ‘black-box’ models by others. The present article aims to discuss pertinent issues related to the development and application of machine learning algorithms for various aspects of multiscale materials modelling. The authors present an overview of machine learning of equivariant properties, machine learning-aided statistical mechanics, the incorporation ofab initioapproaches in multiscale models of materials processing and application of machine learning in uncertainty quantification. In addition to the above, the applicability of Bayesian approach for multiscale modelling will be discussed. Critical issues related to the multiscale materials modelling are also discussed.