Controlled synthesis across several length scales, ranging from discrete molecular building blocks to size-and morphology-controlled nanoparticles to 2D sheets and thin films and finally to 3D architectures, is an advanced and highly active research field within both the metal-organic framework (MOF) domain and the overall material science community. Along with synthetic progress, theoretical simulations of MOF structures and properties have shown tremendous progress in both accuracy and system size. Further advancements in the field of hierarchically structured MOF materials will allow the optimization of their performance; however, this optimization requires a deep understanding of the different synthesis and processing techniques and an enhanced implementation of material modeling. Such modeling approaches will allow us to select and synthesize the highest-performing structures in a targeted rational manner. Here, recent progress in the synthesis of hierarchically structured MOFs and multiscale modeling and associated simulation techniques is presented, along with a brief overview of the challenges and future perspectives associated with a simulation-based approach toward the development of advanced hierarchically structured MOF materials. Hierarchical Architecturesand signal transduction. These molecular components are then organized into subcellular and cellular compartments or domains. These subcellular and cellular compartments are then structured into different organs and organ systems, which ultimately, at the highest level, constitute the entire organism. These organisms are then able to self-replicate and are themselves part of more complex ecosystems.Hierarchically organized synthetic materials also contain structural elements at more than one length scale. This structural hierarchy can strongly influence bulk material properties. Understanding the effects of hierarchical structure is essential for guiding the synthesis of new materials with properties that are tailored to specific applications. [2] The individual building blocks in a material, which are often grouped into different subdomains and domains, are usually regarded as structural elements in hierarchical materials. However, other features of materials, such as porosity or chemical composition, can also be organized hierarchically, which is crucial for optimizing specific properties, such as diffusion within a material or directional energy transfer. [3] The hierarchical order of a material may be defined as the number (n) of levels of scale within a specific structure. [2] As in natural systems, such as proteins, one can categorize structures at different length scales starting from the smallest length scale as primary structures, secondary structures, tertiary structures, and so on. [4] Metal-organic frameworks (MOFs) are a class of functional crystalline materials that has received increasing attention over the

Despite rapid progress in the field of metal–organic frameworks (MOFs), the potential of using machine learning (ML) methods to predict MOF synthesis parameters is still untapped. Here, we show how ML can be used for rationalization and acceleration of the MOF discovery process by directly predicting the synthesis conditions of a MOF based on its crystal structure. Our approach is based on: i) establishing the first MOF synthesis database via automatic extraction of synthesis parameters from the literature, ii) training and optimizing ML models by employing the MOF database, and iii) predicting the synthesis conditions for new MOF structures. The ML models, even at an initial stage, exhibit a good prediction performance, outperforming human expert predictions, obtained through a synthesis survey. The automated synthesis prediction is available via a web‐tool on https://mof-synthesis.aimat.science.

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With the increasing amount of text data stored in relational databases, there is a demand for RDBMS to support keyword queries over text data. As a search result is often assembled from multiple relational tables, traditional IR-style ranking and query evaluation methods cannot be applied directly.In this paper, we study the effectiveness and the efficiency issues of answering top-k keyword query in relational database systems. We propose a new ranking formula by adapting existing IR techniques based on a natural notion of virtual document. Compared with previous approaches, our new ranking method is simple yet effective, and agrees with human perceptions. We also study efficient query processing methods for the new ranking method, and propose algorithms that have minimal accesses to the database. We have conducted extensive experiments on large-scale real databases using two popular RDBMSs. The experimental results demonstrate significant improvement to the alternative approaches in terms of retrieval effectiveness and efficiency.