Phase-Property Diagrams for Multicomponent Oxide Systems toward Materials Libraries

Challenges and Strategies for Synthesizing High Performance Micro and Nanoscale High Entropy Oxide Materials

High-entropy oxide micro/nano materials (HEO MNMs) have shown broad application prospects and have become hot materials in recent years. This review comprehensively provides an overview of the latest developments and covers key aspects of HEO MNMs, by discussing design principles, computer-aided structural design, synthesis challenges and strategies, as well as application areas. The analysis of the synthesis process includes the role of high-throughput process in large-scale synthesis of HEOs MNMs, along with the effects of temperature elevation and undercooling on the formation of HEO MNMs. Additionally, the article summarizes the application of high-precision and in situ characterization devices in the field of HEO MNMs, offering robust support for related research. Finally, a brief introduction to the main applications of HEO MNMs is provided, emphasizing their key performances. This review offers valuable guidance for future research on HEO MNMs, outlining critical issues and challenges in the current field.

A High-Entropy Oxide as High-Activity Electrocatalyst for Water Oxidation

High-entropy materials are an emerging pathway in the development of high-activity (electro)catalysts because of the inherent tunability and coexistence of multiple potential active sites, which may lead to earth-abundant catalyst materials for energy-efficient electrochemical energy storage. In this report, we identify how the multication composition in high-entropy perovskite oxides (HEO) contributes to high catalytic activity for the oxygen evolution reaction (OER), i.e., the key kinetically limiting half-reaction in several electrochemical energy conversion technologies, including green hydrogen generation. We compare the activity of the (001) facet of LaCr_0.2Mn_0.2Fe_0.2Co_0.2Ni_0.2O_3-δ with the parent compounds (single B-site in the ABO₃ perovskite). While the single B-site perovskites roughly follow the expected volcano-type activity trends, the HEO clearly outperforms all of its parent compounds with 17 to 680 times higher currents at a fixed overpotential. As all samples were grown as an epitaxial layer, our results indicate an intrinsic composition-function relationship, avoiding the effects of complex geometries or unknown surface composition. In-depth X-ray photoemission studies reveal a synergistic effect of simultaneous oxidation and reduction of different transition metal cations during the adsorption of reaction intermediates. The surprisingly high OER activity demonstrates that HEOs are a highly attractive, earth-abundant material class for high-activity OER electrocatalysts, possibly allowing the activity to be fine-tuned beyond the scaling limits of mono- or bimetallic oxides.

High Entropy Approach to Engineer Strongly Correlated Functionalities in Manganites

Technologically relevant strongly correlated phenomena such as colossal magnetoresistance (CMR) and metal-insulator transitions (MIT) exhibited by perovskite manganites are driven and enhanced by the coexistence of multiple competing magneto-electronic phases. Such magneto-electronic inhomogeneity is governed by the intrinsic lattice-charge-spin-orbital correlations, which, in turn, are conventionally tailored in manganites via chemical substitution, charge doping, or strain engineering. Alternately, the recently discovered high entropy oxides (HEOs), owing to the presence of multiple-principal cations on a given sub-lattice, exhibit indications of an inherent magneto-electronic phase separation encapsulated in a single crystallographic phase. Here, the high entropy (HE) concept is combined with standard property control by hole doping in a series of single-phase orthorhombic HE-manganites (HE-Mn), (Gd_0.25 La_0.25 Nd_0.25 Sm_0.25 )_{1- x} Sr_x MnO₃ (x = 0-0.5). High-resolution transmission microscopy reveals hitherto-unknown lattice imperfections in HEOs: twins, stacking faults, and missing planes. Magnetometry and electrical measurements infer three distinct ground states-insulating antiferromagnetic, unpercolated metallic ferromagnetic, and long-range metallic ferromagnetic-coexisting or/and competing as a result of hole doping and multi-cation complexity. Consequently, CMR ≈1550% stemming from an MIT is observed in polycrystalline pellets, matching the best-known values for bulk conventional manganites. Hence, this initial case study highlights the potential for a synergetic development of strongly correlated oxides offered by the high entropy design approach.

Graph neural networks for materials science and chemistry

Machine learning plays an increasingly important role in many areas of chemistry and materials science, being used to predict materials properties, accelerate simulations, design new structures, and predict synthesis routes of new materials. Graph neural networks (GNNs) are one of the fastest growing classes of machine learning models. They are of particular relevance for chemistry and materials science, as they directly work on a graph or structural representation of molecules and materials and therefore have full access to all relevant information required to characterize materials. In this Review, we provide an overview of the basic principles of GNNs, widely used datasets, and state-of-the-art architectures, followed by a discussion of a wide range of recent applications of GNNs in chemistry and materials science, and concluding with a road-map for the further development and application of GNNs.

Challenges and Opportunities of Chemiresistors Based on Microelectromechanical Systems for Chemical Olfaction

Microelectromechanical-system (MEMS)-based semiconductor gas sensors are considered one of the fastest-growing, interdisciplinary high technologies during the post-Moore era. Modern advancements within this arena include wearable electronics, Internet of Things, and artificial brain-inspired intelligence, among other modalities, thus bringing opportunities to drive MEMS-based gas sensors with higher performance, lower costs, and wider applicability. However, the high demand for miniature and micropower sensors with unified processes on a single chip imposes great challenges. This review focuses on recent developments and pitfalls in MEMS-based micro- and nanoscale gas sensors and details future trends. We also cover the background of the topic, seminal efforts, current applications and challenges, and opportunities for next-generation systems.

Studying Complex Evolution of Hyperelastic Materials under External Field Stimuli using Artificial Neural Networks with Spatiotemporal Features in a Small-Scale Dataset

Deep-learning (DL) methods, in consideration of their excellence in dealing with highly complex structure-performance relationships for materials, are expected to become a new design paradigm for breakthroughs in material performance. However, in most cases, it is impractical to collect massive-scale experimental data or open-source theoretical databases to support training DL models with sufficient prediction accuracy. In a dataset consisting of 483 porous silicone rubber observations generated via ink-writing additive manufacturing, this work demonstrates that constructing low-dimensional, accurate descriptors is the prerequisite for obtaining high-precision DL models based on small experimental datasets. On this basis, a unique convolutional bidirectional long short-term memory model with spatiotemporal features extraction capability is designed, whose hierarchical learning mechanism further reduces the requirement for the amount of data by taking full advantage of data information. The proposed approach can be expected as a powerful tool for innovative material design on small experimental datasets, which can also be used to explore the evolutionary mechanisms of the structures and properties of materials under complex working conditions.

Prospects and challenges for autonomous catalyst discovery viewed from an experimental perspective

Autonomous catalysis research requires elaborate integration of operando experiments into automated workflows. Suitable experimental data for analysis by artificial intelligence can be measured more readily according to standard operating procedures.