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arXiv 2302.09184cond-mat.mtrl-scics.LG
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基于构筑模块定性表示的金属有机框架高性能快速设计

Rapid Design of Top-Performing Metal-Organic Frameworks with Qualitative Representations of Building Blocks

Northwestern University（西北大学）

Yigitcan Comlek, Thang Duc Pham, Randall Snurr, Wei Chen

备注：
35 pages total. First 29 pages belong to the main manuscript and the remaining 6 six are for the supplementary information, 13 figures total. 9 figures are on the main manuscript and 4 figures are in the supplementary information. 1 table in the supplementary information
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AI总结：

本研究集成潜变量高斯过程与多目标批量贝叶斯优化，利用构筑模块定性表示，仅搜索约1%的设计空间即识别出帕累托前沿全部MOFs及97%以上的前50高性能设计，实现高效自主的MOF设计。

摘要：

数据驱动的材料设计常常面临系统需要或具有定性（分类）信息的挑战。金属有机框架（MOFs）就是此类材料系统的典型例子。通过不同的构筑模块来表示MOFs，使得设计者难以将定性信息纳入设计优化中。此外，大量潜在的构筑模块导致了组合爆炸问题，数百万种可能的MOFs需要通过耗时的基于物理的方法进行探索。在本工作中，我们集成了潜变量高斯过程（LVGP）和多目标批量贝叶斯优化（MOBBO），以自适应、自主且高效的方式识别高性能MOFs，无需任何人工干预。我们的方法具有三个主要优势：（i）不需要特定的物理描述符，仅使用构成MOFs的构筑模块通过定性表示进行全局优化；（ii）该方法与应用和性质无关；（iii）潜变量方法提供了具有物理依据的定性构筑模块的可解释模型。为了展示我们方法的有效性，我们考虑了一个包含超过47,000个MOF候选物的设计空间。通过仅搜索约1%的设计空间，LVGP-MOBBO能够识别出帕累托前沿上的所有MOFs，以及针对CO₂工作容量和CO₂/N₂选择性性质的前50个高性能设计中的超过97%。最后，我们将我们的方法与随机森林算法进行了比较，并展示了其效率、可解释性和鲁棒性。

英文摘要：

Data-driven materials design often encounters challenges where systems require or possess qualitative (categorical) information. Metal-organic frameworks (MOFs) are an example of such material systems. The representation of MOFs through different building blocks makes it a challenge for designers to incorporate qualitative information into design optimization. Furthermore, the large number of potential building blocks leads to a combinatorial challenge, with millions of possible MOFs that could be explored through time consuming physics-based approaches. In this work, we integrated Latent Variable Gaussian Process (LVGP) and Multi-Objective Batch-Bayesian Optimization (MOBBO) to identify top-performing MOFs adaptively, autonomously, and efficiently without any human intervention. Our approach provides three main advantages: (i) no specific physical descriptors are required and only building blocks that construct the MOFs are used in global optimization through qualitative representations, (ii) the method is application and property independent, and (iii) the latent variable approach provides an interpretable model of qualitative building blocks with physical justification. To demonstrate the effectiveness of our method, we considered a design space with more than 47,000 MOF candidates. By searching only ~1% of the design space, LVGP-MOBBO was able to identify all MOFs on the Pareto front and more than 97% of the 50 top-performing designs for the CO$_2$ working capacity and CO$_2$/N$_2$ selectivity properties. Finally, we compared our approach with the Random Forest algorithm and demonstrated its efficiency, interpretability, and robustness.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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