Atomic‐Scale Design of Anode Materials for Alkali Metal (Li/Na/K)‐Ion Batteries: Progress and Perspectives

Abstract

a variety of renewable energy technologies (based on solar, hydro, wind, geothermal power, etc.), 2) provide power sources for electric and hybrid vehicles for low-carbon or zero-carbon emissions of transportation systems, [3,4] and 3) provide power sources for various portable and wearable electronic devices. Alkali metal (AM) ion batteries (AMIBs) including lithium (Li)-ion batteries (LIBs), sodium (Na)-ion batteries (NIBs), and potassium (K)-ion batteries (KIBs) are important rechargeable battery technologies to support the decarbonization of both electricity supply and transportation systems. Currently dominating the rechargeable battery market is LIBs. NIBs and KIBs are developed as more sustainable alternatives and indeed complements, to mitigate challenges associated with the limited and geopolitically isolated Li resources. [1] Post-alkali metal ion batteries are also pursued such as multivalent ion batteries and lithium sulfur batteries. These battery technologies will not be covered in this review, but have been reviewed elsewhere. [5][6][7][8][9][10][11] The three AMIBs follow the same general cell operation, with variations in material selections (Figure 1). [12][13][14] During charge/discharge the AM ions move via an electrolyte between the cathode (positive electrode) and the anode (negative The development and optimization of high-performance anode materials for alkali metal ion batteries is crucial for the green energy evolution. Atomic scale computational modeling such as density functional theory and molecular dynamics allows for efficient and adventurous materials design from the nanoscale, and have emerged as invaluable tools. Computational modeling cannot only provide fundamental insight, but also present input for multiscale models and experimental synthesis, often where quantities cannot readily be obtained by other means. In this review, an overview of three main anode classes; alloying, conversion, and intercalation-type anodes, is provided and how atomic scale modeling is used to understand and optimize these materials for applications in lithium-, sodium-, and potassium-ion batteries. In the last part of this review, a novel type of anode materials that are largely predicted from density functional theory simulations is presented. These 2D materials are currently in their early stages of development and are only expected to gain in importance in the years to come, both within the battery field and beyond, highlighting the ability of atomic scale materials design.

Precisely Tunable Synthesis of Binder-Free Cobalt Oxide-Based Li-Ion Battery Anode Using Scalable Electrothermal Waves

“…Furthermore, after anchoring the active materials on the substrates, it is difficult to control more diverse and meticulous phase changes, making it challenging to obtain an elaborately optimized binder-free design that improves the performance . Nowadays, as the computational approaches for modeling an optimal design of electrodes have been developed rigorously, rapid preparation and analysis of precisely tuned phases and corresponding electrochemical performances are mandatory for the fast screening of the optimal binder-free electrodes employing specific active materials. − Therefore, for the fabrication of next-generation electrodes, facile yet precise synthesis method should be rationally developed for the scalable fabrication of TMO-based binder-free electrodes.…”

Exploring the configurational space of amorphous graphene with machine-learned atomic energies

“…Describing local environments us-ing ML methods can provide insight into the relation between atomic structure and energetics, and therefore structural stability, in amorphous materials-including amorphous carbon, which has emerging applications in biosensing 88 or batteries. 89 The fact that NN-averaged energies yield reasonable, partly paracrystalline structural models may be attributed to the fact that they provide "smoothing" over the variance in local atomic energies. This finding is consistent with earlier findings for the electronic DOS 43,44 and might have wider consequences for ML predictions of local properties, which are yet to be fully explored.…”

Exploring the configurational space of amorphous graphene with machine-learned atomic energies

“…Describing local environments using ML methods can provide insight into the relation between atomic structure and energetics, and therefore structural stability, in amorphous materials-including amorphous carbon, which has emerging applications in biosensing 78 or batteries. 79 The fact that NN-averaged energies yield reasonable, partly paracrystalline structural models may be attributed to the fact that they provide "smoothing" over the variance in local atomic energies. This finding is consistent with earlier findings for the electronic DOS 43,44 and might have wider consequences for ML predictions of local properties, which are yet to be fully explored.…”