High-entropy nanoparticles have become a rapidly growing area of research in recent years. Because of their multielemental compositions and unique high-entropy mixing states (i.e., solid-solution) that can lead to tunable activity and enhanced stability, these nanoparticles have received notable attention for catalyst design and exploration. However, this strong potential is also accompanied by grand challenges originating from their vast compositional space and complex atomic structure, which hinder comprehensive exploration and fundamental understanding. Through a multidisciplinary view of synthesis, characterization, catalytic applications, high-throughput screening, and data-driven materials discovery, this review is dedicated to discussing the important progress of high-entropy nanoparticles and unveiling the critical needs for their future development for catalysis, energy, and sustainability applications.

Abstract

Forecasting the health of a battery is a modeling effort that is critical to driving improvements in and adoption of electric vehicles. Purely physics-based models and purely data-driven models have advantages and limitations of their own. Considering the nature of battery data and end-user applications, we outline several architectures for integrating physics-based and machine learning models that can improve our ability to forecast battery lifetime. We discuss the ease of implementation, advantages, limitations, and viability of each architecture, given the state of the art in the battery and machine learning fields.

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Li x MnO 2 made by ion exchange of glycine-nitrate combustion synthesis-processed ͑GNP͒ orthorhombic Na 0.44 MnO 2 (GNP-Li x MnO 2 ) has been cycled in lithium/liquid electrolyte cell configurations at room temperature and lithium/polymer cell configurations at 85°C over one hundred times without showing capacity fading or phase conversion to spinel. At 2.5 mA/cm 2 in liquid cells ͑5C rate͒ or 1 mA/cm 2 ͑1.5C rate͒ in polymer cells, 80-95% of the expected capacity is delivered. The remarkable stability is attributable to the unusual double tunnel structure, which cannot easily undergo rearrangement to spinel. The enhanced rate capability of GNP-Li x MnO 2 compared to conventionally prepared materials is attributable to the shorter particle length, which allows faster diffusion of lithium ions along the tunnels.