The heptazine-based polymer melon (also known as graphitic carbon nitride, g-C3N4) is a promising photocatalyst for hydrogen evolution. Nonetheless, attempts to improve its inherently low activity are rarely based on rational approaches because of a lack of fundamental understanding of its mechanistic operation. Here we employ molecular heptazine-based model catalysts to identify the cyanamide moiety as a photocatalytically relevant ‘defect'. We exploit this knowledge for the rational design of a carbon nitride polymer populated with cyanamide groups, yielding a material with 12 and 16 times the hydrogen evolution rate and apparent quantum efficiency (400 nm), respectively, compared with the unmodified melon. Computational modelling and material characterization suggest that this moiety improves coordination (and, in turn, charge transfer kinetics) to the platinum co-catalyst and enhances the separation of the photogenerated charge carriers. The demonstrated knowledge transfer for rational catalyst design presented here provides the conceptual framework for engineering high-performance heptazine-based photocatalysts.

Materials discovery has become significantly facilitated and accelerated by high-throughput ab-initio computations. This ability to rapidly design interesting novel compounds has displaced the materials innovation bottleneck to the development of synthesis routes for the desired material. As there is no a fundamental theory for materials synthesis, one might attempt a data-driven approach for predicting inorganic materials synthesis, but this is impeded by the lack of a comprehensive database containing synthesis processes. To overcome this limitation, we have generated a dataset of “codified recipes” for solid-state synthesis automatically extracted from scientific publications. The dataset consists of 19,488 synthesis entries retrieved from 53,538 solid-state synthesis paragraphs by using text mining and natural language processing approaches. Every entry contains information about target material, starting compounds, operations used and their conditions, as well as the balanced chemical equation of the synthesis reaction. The dataset is publicly available and can be used for data mining of various aspects of inorganic materials synthesis.

Scheme 1.Structures of "graphitic carbon nitrides." Shown are the 1D polymer melon (left), the fully condensed 2D counterpart (middle), and the 2D network PHI. KSCN HClScheme 2. Simplified reaction scheme of the compound synthesized in this work, showing melon and its conversion to NCN-CN x by a postsynthetic reaction using KSCN melt, and its acid-induced hydrolysis to urea-CN x .

Digitizing large collections of scientific literature can enable new informatics approaches for scientific analysis and meta-analysis. However, most content in the scientific literature is locked-up in written natural language, which is difficult to parse into databases using explicitly hard-coded classification rules. In this work, we demonstrate a semi-supervised machine-learning method to classify inorganic materials synthesis procedures from written natural language. Without any human input, latent Dirichlet allocation can cluster keywords into topics corresponding to specific experimental materials synthesis steps, such as "grinding" and "heating", "dissolving" and "centrifuging", etc. Guided by a modest amount of annotation, a random forest classifier can then associate these steps with different categories of materials synthesis, such as solid-state or hydrothermal synthesis. Finally, we show that a Markov chain representation of the order of experimental steps accurately reconstructs a flowchart of possible synthesis procedures. Our machine-learning approach enables a scalable approach to unlock the large amount of inorganic materials synthesis information from the literature and to process it into a standardized, machine-readable database.

Collecting and analyzing the vast amount of information available in the solid-state chemistry literature may accelerate our understanding of materials synthesis. However, one major problem is the difficulty of identifying which materials from a synthesis paragraph are precursors or are target materials. In this study, we developed a two-step Chemical Named Entity Recognition (CNER) model to identify precursors and targets, based on information from the context around material entities. Using the extracted data, we conducted a meta-analysis to study the similarities and differences between precursors in the context of solid-state synthesis. To quantify precursor similarity, we built a substitution model to calculate the viability of substituting one precursor with another while retaining the target. From a hierarchical clustering of the precursors, we demonstrate that "chemical similarity" of precursors can be extracted from text data. Quantifying the similarity of precursors helps provide a foundation for suggesting candidate reactants in a predictive synthesis model.

We quantify the thermodynamic equilibrium conditions that govern the formation of crystalline heptazinebased carbon nitride materials, currently of enormous interest for photocatalytic applications including solar hydrogen evolution. Key phases studied include the monomeric phase melem, the 1D polymer melon, and the hypothetical hydrogenfree 2D graphitic carbon nitride phase "g-C 3 N 4 ". Our study is based on density functional theory including van der Waals dispersion terms with different experimental conditions represented by the chemical potential of NH 3 . Graphitic carbon nitride is the subject of a vast number of studies, but its existence is still controversial. We show that typical conditions found in experiments pertain to the polymer melon (2D planes of 1D hydrogen-bonded polymer strands). In contrast, equilibrium synthesis of heptazine (h)-based g-h-C 3 N 4 below its experimentally known decomposition temperature requires much less likely conditions, equivalent to low NH 3 partial pressures around 1 Pa at 500 °C and around 10 3 Pa even at 700 °C. A recently reported synthesis of triazine (t)-based g-t-C 3 N 4 in a salt melt is interpreted as a consequence of the altered local chemical environment of the C 3 N 4 nanocrystallites.

As graphene has become one of the most important materials, there is renewed interest in other similar structures. One example is silicene, the silicon analogue of graphene. It shares some of the remarkable graphene properties, such as the Dirac cone, but presents some distinct ones, such as a pronounced structural buckling. We have investigated, through density functional based tight-binding (DFTB), as well as reactive molecular dynamics (using ReaxFF), the mechanical properties of suspended single-layer silicene. We calculated the elastic constants, analyzed the fracture patterns and edge reconstructions. We also addressed the stress distributions, unbuckling mechanisms and the fracture dependence on the temperature. We analysed the differences due to distinct edge morphologies, namely zigzag and armchair.

Carbon nitride-based nanostructures have attracted special attention (from theory and experiments) due to their remarkable electromechanical properties. In this work we have investigated the mechanical properties of some graphene-like carbon nitride membranes through fully atomistic reactive molecular dynamics simulations. We have analyzed three different structures of these CN families, the so-called graphene-based g-CN, triazine-based g-C 3 N 4 and * To whom correspondence should be addressed †1 1 heptazine-based g-C 3 N 4 . The stretching dynamics of these membranes was studied for deformations along their two main axes and at three different temperatures: 10K, 300K and 600K.We show that g − CN membranes have the lowest ultimate fracture strain value, followed by heptazine-based and triazine-based ones, respectively. This behavior can be explained in terms of their differences in terms of density values, topologies and types of chemical bonds. The dependency of the fracture patterns on the stretching directions is also discussed.