McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, Texas, USA; email:
Department of Physics, University of Texas at Austin, Austin, Texas, USA.
Annu Rev Chem Biomol Eng. 2022 Jun 10;13:235-254. doi: 10.1146/annurev-chembioeng-092220-024340. Epub 2022 Mar 17.
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.
设计功能材料需要在多维空间中深入搜索系统参数，以获得理想的材料性能。对于传统的参数扫描或反复试验采样不切实际的情况，将设计作为约束优化问题的反演方法提供了一种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性能进行耗时且资源密集型的表征，从而形成设计瓶颈。结合机器学习的方法可以帮助解决这一限制，并加速具有目标性能的材料的发现。在本文中，我们回顾了如何利用机器学习来降低维度，从而有效地探索设计空间，加速性能评估，并生成具有最优性能的非常规材料结构。我们还讨论了有前途的未来方向，包括将机器学习集成到设计算法的多个阶段，以及对机器学习模型的解释，以了解设计参数与材料性能的关系。
