The DFT analysis revealed fast 3D-lithium-ion diffusion pathways and high electronic conductivity in the Li2MnSiO4 surface, and thus paving the way for designing and developing efficient and low-cost rechargeable lithium-ion batteries.

This article presents a review of current advances and prospects in the field of forecasting renewable energy generation using machine learning (ML) and deep learning (DL) techniques. With the increasing penetration of renewable energy sources (RES) into the electricity grid, accurate forecasting of their generation becomes crucial for efficient grid operation and energy management. Traditional forecasting methods have limitations, and thus ML and DL algorithms have gained popularity due to their ability to learn complex relationships from data and provide accurate predictions. This paper reviews the different approaches and models that have been used for renewable energy forecasting and discusses their strengths and limitations. It also highlights the challenges and future research directions in the field, such as dealing with uncertainty and variability in renewable energy generation, data availability, and model interpretability. Finally, this paper emphasizes the importance of developing robust and accurate renewable energy forecasting models to enable the integration of RES into the electricity grid and facilitate the transition towards a sustainable energy future.

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Redox flow batteries (RFBs) have emerged as a promising option for large-scale energy storage, owing to their high energy density, low cost, and environmental benefits. However, the identification of organic compounds with high redox activity, aqueous solubility, stability, and fast redox kinetics is a crucial and challenging step in developing an RFB technology. Density functional theory-based computational materials prediction and screening is a time-consuming and computationally expensive technique, yet it has a high success rate. To speed up the discovery of new materials with desired properties, machine-learning-based models can be trained on large data sets. Graph neural networks (GNNs) are particularly well-suited for non-Euclidean data and can model complex relationships, making them ideal for accelerating the discovery of novel materials. In this study, a GNN-based model called MolGAT was developed to predict the redox potential of organic molecules using molecular structures, atomic properties, and bond attributes. The model was trained on a data set of over 15,000 compounds with redox potentials ranging from −4.11 to 2.56. MolGAT outperformed other GNN variants, such as the Graph Attention Network, Graph Convolution Network, and AttentiveFP models. The trained model was used to screen a vast chemical data set comprising 581,014 molecules, namely OMDB, QM9, ZINC, CHEMBL, and DELANEY, and identified 23,467 potential redox-active compounds for use in redox flow batteries. Of those, 20,716 molecules were identified as potential catholytes with predicted redox potentials up to 2.87 V, while 2,751 molecules were deemed potential anolytes with predicted redox potentials as low as −2.88 V. This work demonstrates the capabilities of graph neural networks in condensed matter physics and materials science to screen promising redox-active species for further electronic structure calculations and experimental testing.