编辑概述: 机器学习-加速 MOFs设计与开发

随着机器学习（ML）的发展，材料系统的设计和发展经历了一个加速过程。然而，将ML应用于材料系统设计的主要挑战之一，在于找到合适的设计表示。大多数材料设计应用程序是利用定量（或数值）设计变量来表示材料系统。在很多情况下，这些定量描述符（特征）需要专业知识或数据分析，才能找到最合适的描述符。另一方面，尽管大多数定性（或分类）变量（如化学元素、化学成分）比定量变量更容易获得，但在自动材料设计中直接将定性变量作为设计变量的一部分是一个挑战。金属有机框架（MOFs）就是这类材料系统的一个例子。MOFs是一类多孔结晶材料，广泛用于气体储存、气体分离和催化。由于其高度可调性，MOFs被视为解决不同应用问题的潜在方案，例如二氧化碳（CO₂）的捕集和分离。然而，由于MOF构建块及其组合方式的多样性，候选材料数量级过高。因此，实验所需的时间和资源太高，人们已经开始使用机器学习来加速材料系统的设计和开发。但现有的方法通常依赖于大量的数据集和高维物理描述符来表示材料设计空间。这些机器学习模型既耗时，泛化性又不强，通常不能迁移到不同的设计目标上。在本工作中，来自美国西北大学机械系的Yigitcan Comlek等人，提出了一套潜在变量高斯过程多目标批量贝叶斯优化（LVGP-MOBBO）框架，以直接从构建材料的构建块中快速设计优越的MOFs。他们使用了已有的定性MOFs建筑块信息，构建了一个可解释的LVGP模型，在MOBBO的辅助下，自适应地引导CO₂捕获和分离性能较好的MOFs。通过整合批量贝叶斯优化，无描述符的LVGP也可以有效地扩展到具有大量级别的应用。通过LVGP预测具有看不见构建块的MOFs的特性是一个很有前途的研究领域。该框架的一个有趣的应用是将涉及到通过自主实验研究进行材料设计和开发。由于在LVGP-MOBBO中没有人为干预，而且实验输入可以是定性和定量的，在这里提出的方法可以帮助研究人员有效地指导实验。

Editorial Summary: Machine learning accelerates the design and development of MOFs

With recent advances in machine learning (ML), material system design and development has undergone rapid acceleration. However, one of the major challenges in applying ML to material system design lies in finding the appropriate design representations. Most material design applications take advantage of quantitative (or numerical) design variables to represent material systems. In most cases, these quantitative descriptors (features) require either expert knowledge or data analysis to find the most appropriate ones. On the other hand, although most qualitative (or categorical) variables (e.g., chemical elements, chemical compositions) are more accessible than quantitative variables, it is challenging to directly include qualitative variables as a part of the design variables in automated materials design. Metal-organic frameworks (MOFs) are an example of such materials systems.    MOFs are a class of porous crystalline materials that have been used extensively for gas storage, gas separation, and catalysis. Because of their highly tunable nature, MOFs have been looked at as a potential solution for different applications such as CO₂ capture and separation. However, the versatility and different possible combinations of the MOF building blocks lead to millions of candidates. Due to the high experimental cost, both in time and resources, machine learning has been used to accelerate material system design and development. However, the existing approaches usually rely on large data sets and high-dimensional physical descriptors to represent the material design space. These processes can be both time consuming and property specific, meaning that the ML models and descriptors are often not transferable to different design objectives. In this work, Yigitcan Comlek et al. from the Department of Mechanical Engineering, Northwestern University, presented a Latent Variable Gaussian Process Multi-Objective Batch Bayesian Optimization (LVGP-MOBBO) framework to perform rapid design of superior MOFs directly from the building blocks that construct the material. They took advantage of the readily available qualitative building block information that is used to construct the MOFs and built an interpretable LVGP surrogate model that cooperates with MOBBO to adaptively lead towards promising MOF candidates for CO₂ capture and separation. With the integration of batch BO, descriptor-free LVGP can be effectively extended to applications with substantial number of levels. To predict the properties of MOFs with unseen building blocks through LVGP is a promising area of research. The interesting application of this framework would involve performing materials design and development through autonomous experimentation studies. As there is no human intervention in LVGP-MOBBO, and the experimental inputs can be both qualitative and quantitative, the method presented in this work can help researchers guide their experiments efficiently.