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Four fluorescent tricyclic basic dyes with two hollow organic capsules namely cucurbit[n]urils (CB[n]), n = 7 and 8, compose the chemical tongue that is examined for -amino acids recognition. This array is able to identify and discriminate 18 -amino acids up to 10-4 M without the need of enzyme activation. The paper shows the importance of the classification technique employed in order to reach the highest recognition rate at this concentration.

γ‐Hydroxybutyric acid (GHB), a colourless, odourless and tasteless chemical, has become one of the most dangerous illicit drugs of abuse today. At low doses, this drug is a central nervous system depressant that reduces anxiety and produces euphoria and relaxation, sedating the recipient. There is an urgent need for simple, easy‐to‐use sensors for GHB in solution. Here, we present a colorimetric sensor array based on supramolecular host–guest complexes of fluorescent dyes with organic capsules (cucurbiturils) for the detection of GHB.

Finding a clear route to new structures: The design of an adaptable time warping (ATW) methodology (see figure) for automatically, quickly, and reliably deciphering X‐ray diffraction patterns is described.

This study shows how chemistry knowledge and reasoning are taken into account for building a new methodology that aims at automatically grouping data having a chronological structure. We consider combinatorial catalytic experiments where the evolution of a reaction (e.g., conversion) over time is expected to be analyzed. The mathematical tool has been developed to compare and group curves taking into account their shape. The strategy, which consists on combining a hierarchical clustering with the k-means algorithm, is described and compared with both algorithms used separately. The hybridization is shown to be of great interest. Then, a second application mode of the proposed methodology is presented. Once meaningful clusters according to chemists preferences and goals are successfully achieved, the induced model may be used in order to automatically classify new experimental results. The grouping of the new catalysts tested for the Heck coupling reaction between styrene and iodobenzene verified the set of criteria “defined” during the initial clustering step, and facilitated a quick identification of the catalytic behaviors following users preferences.

This work deals with data analysis techniques and high-throughput tools for synthesis and characterization of solid materials. In previous studies, it was found that the final properties of materials could be successfully modeled using learning systems. Machine learning algorithms such as neural networks, support vector machines, and regression trees are non-parametric strategies. They are compared to traditional parametric statistical approaches. We review a wide range of statistical methodologies, and all the methods are evaluated using experimental data derived from an exploration-optimization of the material ITQ-21. The results are judged on the numerical prediction of phase's crystallinity. We discuss the theoretical aspects of such statistical techniques, which make them an attractive method when compared to other learning strategies for modeling the properties of the solids. Advantages and drawbacks are highlighted. We show that such approaches, by offering broad solutions, can reach high-level performances while offering ease of use, comprehensibility, and control. Finally, we shed light on both the interpretation and stability of results, which remain the main drawbacks of the majority of machine learning methodologies when trying to retrieve knowledge from the data treatment.

This work shows the application of support vector machines (SVM) for modelling and prediction of zeolite synthesis, when using the gel molar ratios as model input (synthesis descriptors). Experimental data includes the synthesis results of a multi-level factorial experimental design of the system TEA:SiO2:Na2O:Al2O3:H2O. The few parameters of the SVM model were studied and the fitting performance is compared with the ones obtained with other machine learning models such as neural networks and classification trees. SVM models show very good prediction performances and generalization capacity in zeolite synthesis prediction. They may overcome overfitting problems observed sometimes for neural networks. It is also studied the influence of the type of material descriptors used as model output.

This work shows a methodology for the synthesis of self-assembled organic−inorganic materials which integrates high-throughput tools for the synthesis and characterization of solid materials and data-mining techniques in materials science. This is illustrated by a detailed exploration of the hydrothermal synthesis in the system SiO2:GeO2:Al2O3:F-:H2O:N(16) methylsparteinium. Data analysis and dimensional reduction were conducted by using principal components analysis and clustering algorithms, allowing the definition of a new and suitable structural vector which summarizes the X-ray diffraction characterization data as well as an improvement of data visualization and interpretation. Different modeling techniques were applied for the prediction of the properties of the materials considering the synthesis descriptors as input of the model. Furthermore, different “material property” descriptors were considered as outcome of the model, that is, the crystallinity of the formed phases, structural principal components computed by principal component analysis, or clustering results. It was found that the final properties of the materials could be successfully modeled using artificial neural networks and decision trees.

One of the main problems in high-throughput research for materials is still the design of experiments. At early stages of discovery programs, purely exploratory methodologies coupled with fast screening tools should be employed. This should lead to opportunities to find unexpected catalytic results and identify the “groups” of catalyst outputs, providing well-defined boundaries for future optimizations. However, very few new papers deal with strategies that guide exploratory studies. Mostly, traditional designs, homogeneous covering, or simple random samplings are exploited. Typical catalytic output distributions exhibit unbalanced datasets for which an efficient learning is hardly carried out, and interesting but rare classes are usually unrecognized. Here is suggested a new iterative algorithm for the characterization of the search space structure, working independently of learning processes. It enhances recognition rates by transferring catalysts to be screened from “performance-stable” space zones to “unsteady” ones which necessitate more experiments to be well-modeled. The evaluation of new algorithm attempts through benchmarks is compulsory due to the lack of past proofs about their efficiency. The method is detailed and thoroughly tested with mathematical functions exhibiting different levels of complexity. The strategy is not only empirically evaluated, the effect or efficiency of sampling on future Machine Learning performances is also quantified. The minimum sample size required by the algorithm for being statistically discriminated from simple random sampling is investigated.

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γ‐Hydroxybutyric acid (GHB)…︁…︁ is a euphoriant and aphrodisiac drug that can cause death. In their Full Paper on p. 4489 ff., H. Garcia et al. describe the development of a chemical tongue based on the concept of host–guest complexes between tricyclic basic dyes and cucurbiturils. This sensor array can instantaneously discriminate GHB from precursors and related carboxylates.