2021
DOI: 10.26434/chemrxiv.13490925
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Failure-experiment-supported optimization of poorly reproducible synthetic conditions for novel lanthanide metal-organic frameworks with two-dimensional secondary building units

Abstract: A series of novel metal organic frameworks with lanthanide double-layer-based inorganic subnetworks (KGF-3) was synthesized assisted by machine learning. Pure KGF-3 was difficult to isolate in the initial screening experiments. The synthetic conditions were successfully optimized by extracting the dominant factors for KGF-3 synthesis using two machine-learning techniques. Cluster analysis was used to classify the obtained PXRD patterns of the products and to decide automatically whether the experiments were su… Show more

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“…We have recently reported am ethod allowing one to explore the synthetic conditions of novel MOFs based on two machine learning techniques,namely,i )the cluster analysis of powder X-ray diffraction (PXRD) patterns and ii)random forest and decision tree analysis. [11] Cluster analysis is an unsupervised learning technique automatically grouping the PXRD patterns of crystals obtained under various synthetic conditions based on the degree of similarity (correlation) between these patterns.R andom forest and decision tree analysis are supervised learning techniques that create ap rediction model using the experimental conditions as explanatory variables and the PXRD patterns classified by clustering analysis as objective variables.The above approach allowed us to extract chemical insights from failure experiments and optimize the conditions for the synthesis of novel MOFs.H erein, this method is applied to the syntheses of novel AgÀSC Ps (Figure 1), which have attracted much attention because of their unique optical properties and semiconducting nature. [4b, 12] Specifically,w ed escribe the syntheses and crystal structures of three novel topological isomers of CPs comprising Ag + and trithiocyanuric acid (H 3 ttc).…”
Section: Introductionmentioning
confidence: 99%
“…We have recently reported am ethod allowing one to explore the synthetic conditions of novel MOFs based on two machine learning techniques,namely,i )the cluster analysis of powder X-ray diffraction (PXRD) patterns and ii)random forest and decision tree analysis. [11] Cluster analysis is an unsupervised learning technique automatically grouping the PXRD patterns of crystals obtained under various synthetic conditions based on the degree of similarity (correlation) between these patterns.R andom forest and decision tree analysis are supervised learning techniques that create ap rediction model using the experimental conditions as explanatory variables and the PXRD patterns classified by clustering analysis as objective variables.The above approach allowed us to extract chemical insights from failure experiments and optimize the conditions for the synthesis of novel MOFs.H erein, this method is applied to the syntheses of novel AgÀSC Ps (Figure 1), which have attracted much attention because of their unique optical properties and semiconducting nature. [4b, 12] Specifically,w ed escribe the syntheses and crystal structures of three novel topological isomers of CPs comprising Ag + and trithiocyanuric acid (H 3 ttc).…”
Section: Introductionmentioning
confidence: 99%