Machine Learning-Assisted Design of Material Properties.

IF 7.6 2区 工程技术 Q1 CHEMISTRY, APPLIED
Sanket Kadulkar, Z. Sherman, V. Ganesan, Thomas M Truskett
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引用次数: 11

Abstract

Designing functional materials requires a deep search through multidimensional spaces for system parameters that yield desirable material properties. For cases where conventional parameter sweeps or trial-and-error sampling are impractical, inverse methods that frame design as a constrained optimization problem present an attractive alternative. However, even efficient algorithms require time- and resource-intensive characterization of material properties many times during optimization, imposing a design bottleneck. Approaches that incorporate machine learning can help address this limitation and accelerate the discovery of materials with targeted properties. In this article, we review how to leverage machine learning to reduce dimensionality in order to effectively explore design space, accelerate property evaluation, and generate unconventional material structures with optimal properties. We also discuss promising future directions, including integration of machine learning into multiple stages of a design algorithm and interpretation of machine learning models to understand how design parameters relate to material properties. Expected final online publication date for the Annual Review of Chemical and Biomolecular Engineering, Volume 13 is October 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
机器学习辅助材料特性设计。
设计功能材料需要在多维空间中深入搜索产生所需材料特性的系统参数。对于传统的参数扫描或试错采样不切实际的情况,将设计框定为约束优化问题的逆方法提供了一个有吸引力的替代方案。然而,即使是高效的算法也需要在优化过程中多次对材料特性进行时间和资源密集型表征,从而造成设计瓶颈。结合机器学习的方法可以帮助解决这一限制,并加速发现具有目标特性的材料。在这篇文章中,我们回顾了如何利用机器学习来降低维度,以便有效地探索设计空间,加速性能评估,并生成具有最佳性能的非常规材料结构。我们还讨论了有前景的未来方向,包括将机器学习集成到设计算法的多个阶段,以及解释机器学习模型,以了解设计参数如何与材料特性相关。《化学与生物分子工程年刊》第13卷预计最终在线出版日期为2022年10月。请参阅http://www.annualreviews.org/page/journal/pubdates用于修订估算。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Annual review of chemical and biomolecular engineering
Annual review of chemical and biomolecular engineering CHEMISTRY, APPLIED-ENGINEERING, CHEMICAL
CiteScore
16.00
自引率
0.00%
发文量
25
期刊介绍: The Annual Review of Chemical and Biomolecular Engineering aims to provide a perspective on the broad field of chemical (and related) engineering. The journal draws from disciplines as diverse as biology, physics, and engineering, with development of chemical products and processes as the unifying theme.
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