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Materials chemistry is focused on the design, preparation, and understanding of innovative materials. It is an emerging area of research where definitions are not well established. This document defines the area of materials chemistry for the benefit of chemistry communities and the general public worldwide interested in this discipline. This recommendation defines the term "materials chemistry" as the "scientific discipline that designs, synthesizes, and characterizes materials, with particular interest on processing and understanding of useful or potentially useful properties displayed by the materials designed and synthesized for specific applications." [ABSTRACT FROM AUTHOR]

Global factors are compromising the security of the food supply. Many of us in our community might be unsure about how to help with our seemingly inapplicable expertise. In this piece, I describe how materials chemistry and materials chemists will be essential in addressing this problem, but not only in the way we might think. There is a gigantic and largely unexplored opportunity for real societal impact if we accept a broader view of what academic materials chemistry can be. [ABSTRACT FROM AUTHOR]

The vast size of composition space poses a significant challenge for materials chemistry: exhaustive enumeration of potentially interesting compositions is typically infeasible, hindering assessment of important criteria ranging from novelty and stability to cost and performance. We report a tool, Comgen, for the efficient exploration of composition space, which makes use of logical methods from computer science used for proving theorems. We demonstrate how these techniques, which have not previously been applied to materials discovery, can enable reasoning about scientific domain knowledge provided by human experts. Comgen accepts a variety of user‐specified criteria, converts these into an abstract form, and utilises a powerful automated reasoning algorithm to identify compositions that satisfy these user requirements, or prove that the requirements cannot be simultaneously satisfied. In contrast to machine learning techniques, explicitly reasoning about domain knowledge, rather than making inferences from data, ensures that Comgen's outputs are fully interpretable and provably correct. Users interact with Comgen through a high‐level Python interface. We illustrate use of the tool with several case studies focused on the search for new ionic conductors. Further, we demonstrate the integration of Comgen into an end‐to‐end automated workflow to propose and evaluate candidate compositions quantitatively, prior to experimental investigation. This highlights the potential of automated formal reasoning in materials chemistry. [ABSTRACT FROM AUTHOR]

Metal–organic frameworks (MOFs) have emerged as promising materials for triboelectric nanogenerators (TENGs), but the effects of ligand choice on triboelectric charge remain underexplored. Hence, this paper demonstrates the effect of single, binary, and ternary ligands on TENG performance of cobalt/cerium‐based (Co─Ce) bimetallic MOFs utilizing 2‐methylimidazole (2Melm), terephthalic acid (BDC), and benzene tricarboxylic acid (BTC) as ligands. The detailed structural characterization revealed that varying ligand chemistries led to distinct MOF features affecting TENG performance. Single ligand bimetallic MOFs (designated as CoCe‐2MeIm, CoCe‐BDC, CoCe‐BTC) has lower performance than binary ligand (designated as CoCe‐2MeIm‐BDC, CoCe‐2MeIm‐BTC, CoCe‐BDC‐BTC) and ternary ligand MOFs (designated as CoCe‐2MeIm‐BDC‐BTC). Among all, the binary ligand MOF, CoCe‐2MeIm‐BTC, shows the best results (598 V, 26.7 µA) due to the combined effect of imidazole ring and (─COO─) groups. This is attributed to lone pairs on nitrogen atoms and a delocalized π‐electron system in imidazole system in this material. CoCe‐BTC has the lowest results (31 V, 3.2 µA) due to the bulkier nature of the electron‐withdrawing (─COO─) groups and their impact on the π‐electron system of the benzene ring. This study showcases the potential of ligand chemistry manipulation to control triboelectric charge and thereby enhance MOF‐based TENG performance. [ABSTRACT FROM AUTHOR]

The electrochemical series is a useful tool in electrochemistry, but its effectiveness in materials chemistry is limited by the fact that the standard electrochemical series is based on a relatively small set of reactions, many of which are measured in aqueous solutions. To address this problem, we have used machine learning to create an electrochemical series for inorganic materials from tens of thousands of entries in the Inorganic Crystal Structure Database. We demonstrate that this series is generally more consistent with oxidation states in solid-state materials than the series based on aqueous ions. The electrochemical series was constructed by developing and parameterizing a physical, human-interpretable model of oxidation states in materials. We show that this model enables the prediction of oxidation states from composition in a way that is more accurate than a state-of-the-art transformer-based neural network model. We present applications of our approach to structure prediction, materials discovery, and materials electrochemistry, and we discuss possible additional applications and areas for improvement. To facilitate the use of our approach, we introduce a freely available website and API. [ABSTRACT FROM AUTHOR]