Inorganic-organic hybrid materials such as organically templated metal oxides, metal-organic frameworks (MOFs) and organohalide perovskites have been studied for decades, and hydrothermal and (non-aqueous) solvothermal syntheses have produced thousands of new materials that collectively contain nearly all the metals in the periodic table. Nevertheless, the formation of these compounds is not fully understood, and development of new compounds relies primarily on exploratory syntheses. Simulation- and data-driven approaches (promoted by efforts such as the Materials Genome Initiative) provide an alternative to experimental trial-and-error. Three major strategies are: simulation-based predictions of physical properties (for example, charge mobility, photovoltaic properties, gas adsorption capacity or lithium-ion intercalation) to identify promising target candidates for synthetic efforts; determination of the structure-property relationship from large bodies of experimental data, enabled by integration with high-throughput synthesis and measurement tools; and clustering on the basis of similar crystallographic structure (for example, zeolite structure classification or gas adsorption properties). Here we demonstrate an alternative approach that uses machine-learning algorithms trained on reaction data to predict reaction outcomes for the crystallization of templated vanadium selenites. We used information on 'dark' reactions--failed or unsuccessful hydrothermal syntheses--collected from archived laboratory notebooks from our laboratory, and added physicochemical property descriptions to the raw notebook information using cheminformatics techniques. We used the resulting data to train a machine-learning model to predict reaction success. When carrying out hydrothermal synthesis experiments using previously untested, commercially available organic building blocks, our machine-learning model outperformed traditional human strategies, and successfully predicted conditions for new organically templated inorganic product formation with a success rate of 89 per cent. Inverting the machine-learning model reveals new hypotheses regarding the conditions for successful product formation.

For organic semiconductors to find ubiquitous electronics applications, the development of new materials with high mobility and air stability is critical. Despite the versatility of carbon, exploratory chemical synthesis in the vast chemical space can be hindered by synthetic and characterization difficulties. Here we show that in silico screening of novel derivatives of the dinaphtho[2,3-b:2′,3′-f]thieno[3,2-b]thiophene semiconductor with high hole mobility and air stability can lead to the discovery of a new high-performance semiconductor. On the basis of estimates from the Marcus theory of charge transfer rates, we identified a novel compound expected to demonstrate a theoretic twofold improvement in mobility over the parent molecule. Synthetic and electrical characterization of the compound is reported with single-crystal field-effect transistors, showing a remarkable saturation and linear mobility of 12.3 and 16 cm2 V−1 s−1, respectively. This is one of the very few organic semiconductors with mobility greater than 10 cm2 V−1 s−1 reported to date.

Although ZnO and ZnS are abundant, stable, environmentally benign, their band gap energies (3.44 eV, 3.72 eV) are too large for optimal photovoltaic efficiency. By using band-corrected pseudopotential density-functional theory calculations, we study how the band gap, optical absorption, and carrier localization can be controlled by forming quantum-well like and nanowire-based heterostructures of ZnO/ZnS and ZnO/ZnTe. In the case of ZnO/ZnS core/shell nanowires, which can be synthesized using existing methods, we obtain a band gap of 2.07 eV, which corresponds to a Shockley-Quiesser efficiency limit of 23%. Based on these nanowire results, we propose that ZnO/ZnS core/shell nanowires can be used as photovoltaic devices with organic polymer semiconductors as p-channel contacts.

Graphene has been demonstrated to be impermeable to gases but can be made selectively permeable by introduction of pores. The permeability of a recently synthesized porous graphene structure to He, Ne, and CH4 is studied using MP2/cc-pVTZ potential energy surfaces. The role of quantum and classical transmission effects as a function of temperature are investigated. At room temperature, there is a 20 and 16% increase in transmission due to quantum tunneling for 3He and 4He, respectively, over the purely classical result. The large differences in classical barrier heights for transmission through this membrane (0.523, 1.245, and 4.832 eV for He, Ne, and CH4, respectively) allow for highly selective separation. This is proposed as an economical means of separating He from the other noble gases and alkanes present in natural gas.

The rapid increase in both the quantity and complexity of data that are being generated daily in the field of environmental science and engineering (ESE) demands accompanied advancement in data analytics. Advanced data analysis approaches, such as machine learning (ML), have become indispensable tools for revealing hidden patterns or deducing correlations for which conventional analytical methods face limitations or challenges. However, ML concepts and practices have not been widely utilized by researchers in ESE. This feature explores the potential of ML to revolutionize data analysis and modeling in the ESE field, and covers the essential knowledge needed for such applications. First, we use five examples to illustrate how ML addresses complex ESE problems. We then summarize four major types of applications of ML in ESE: making predictions; extracting feature importance; detecting anomalies; and discovering new materials or chemicals. Next, we introduce the essential knowledge required and current shortcomings in ML applications in ESE, with a focus on three important but often overlooked components when applying ML: correct model development, proper model interpretation, and sound applicability analysis. Finally, we discuss challenges and future opportunities in the application of ML tools in ESE to highlight the potential of ML in this field.