Fast 3D-lithium-ion diffusion and high electronic conductivity of Li2MnSiO4 surfaces for rechargeable lithium-ion batteries

Phase-Dependent Properties of Manganese Oxides and Applications in Electrovoltaics

This study reports first-principles predictions as well as experimental synthesis of manganese oxide nanoparticles under different conditions. The theoretical part of the work comprised density functional theory (DFT)-based calculations and first-principles molecular dynamics (MD) simulations. The extensive research efforts and the current challenges in enhancing the performance of the lithium-ion battery (LIB) provided motivation to explore the potential of these materials for use as an anode in the battery. The structural analysis of the synthesized samples carried out using X-ray diffraction (XRD) confirmed the tetragonal structure of Mn3O4 on heating at 450 and 550 °C and the cubic structure of Mn2O3 on heating at 650 °C. The structures are found in the form of nanoparticles at 450 and 550 °C, but at 650 °C, the material appeared in the form of a nanoporous structure. Further, we investigated the electrochemical functionality of Mn2O3 and Mn3O4 as anode materials for utilization in LIBs via MD simulations. Based on the investigations of their electrical, structural, diffusion, and storage behavior, the anodic character of Mn2O3 and Mn3O4 is predicted. The findings indicated that 10 lithium atoms adsorb on Mn2O3, whereas 5 lithium atoms adsorb on Mn3O4 when saturation is taken into account. The storage capacities of Mn2O3 and Mn3O4 are estimated to be 1697 and 585 mAh g-1, respectively. The maximum value of lithium insertion voltage per Li in Mn2O3 is 0.93 and 0.22 V in Mn3O4. Further, the diffusion coefficient values are found as 2.69 × 10-9 and 2.65 × 10-10 m2 s-1 for Mn2O3 and Mn3O4, respectively, at 300 K. The climbing image nudged elastic band method (Cl-NEB) was implemented, which revealed activation energy barriers of Li as 0.30 and 0.75 eV for Mn2O3 and Mn3O4, respectively. The findings of the work revealed high specific capacity, low Li diffusion energy barrier, and low open circuit voltage for the Mn2O3-based anode for use in LIBs.

Advancing energy storage through solubility prediction: leveraging the potential of deep learning

Solubility prediction plays a crucial role in energy storage applications, such as redox flow batteries, because it directly affects the efficiency and reliability. Researchers have developed various methods that utilize quantum calculations and descriptors to predict the aqueous solubilities of organic molecules. Notably, machine learning models based on descriptors have shown promise for solubility prediction. As deep learning tools, graph neural networks (GNNs) have emerged to capture complex structure-property relationships for material property prediction. Specifically, MolGAT, a type of GNN model, was designed to incorporate n-dimensional edge attributes, enabling the modeling of intricacies in molecular graphs and enhancing the prediction capabilities. In a previous study, MolGAT successfully screened 23 467 promising redox-active molecules from a database of over 500 000 compounds, based on redox potential predictions. This study focused on applying the MolGAT model to predict the aqueous solubility (log S) of a broad range of organic compounds, including those previously screened for redox activity. The model was trained on a diverse sample of 8494 organic molecules from AqSolDB and benchmarked against literature data, demonstrating superior accuracy compared with other state of the art graph-based and descriptor-based models. Subsequently, the trained MolGAT model was employed to screen redox-active organic compounds identified in the first phase of high-throughput virtual screening, targeting favorable solubility in energy storage applications. The second round of screening, which considered solubility, yielded 12 332 promising redox-active and soluble organic molecules suitable for use in aqueous redox flow batteries. Thus, the two-phase high-throughput virtual screening approach utilizing MolGAT, specifically trained for redox potential and solubility, is an effective strategy for selecting suitable intrinsically soluble redox-active molecules from extensive databases, potentially advancing energy storage through reliable material development. This indicates that the model is reliable for predicting the solubility of various molecules and provides valuable insights for energy storage, pharmaceutical, environmental, and chemical applications.

High-Throughput Screening of Promising Redox-Active Molecules with MolGAT

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.

Rational Design of Biaxial Tensile Strain for Boosting Electronic and Ionic Conductivities of Na2 MnSiO4 for Rechargeable Sodium-Ion Batteries

Using first-principles calculations, biaxial tensile (ϵ=2 and 4 %) and compressive (ϵ=-2 and -4 %) straining of Na2 MnSiO4 lattices resulted into radial distance cut offs of 1.65 and 2 Å, respectively, in the first and second nearest neighbors shell from the center. The Si-O and Mn-O bonds with prominent probability density peaks validated structural stability. Wide-band gap of 2.35 (ϵ=0 %) and 2.54 eV (ϵ=-4 %), and narrow bandgap of 2.24 eV (ϵ=+4 %) estimated with stronger coupling of p-d σ bond than that of the p-d π bond, mainly contributed from the oxygen p-state and manganese d-state. Na+ -ion diffusivity was found to be enhanced by three orders of magnitude as the applied biaxial strain changed from compressive to tensile. According to the findings, the rational design of biaxial strain would improve the ionic and electronic conductivity of Na2 MnSiO4 cathode materials for advanced rechargeable sodium-ion batteries.