Nan Yao

The cycle life of lithium (Li) metal batteries is plagued by the rapid depletion of electrolyte and Li inventory due to the continuous formation of a massive, thick solid-electrolyte interphase (SEI). Here, in situ generation of an ultrathin SEI is proposed by accelerating defluorination kinetics of the electrolyte to retrench the amount of electrolyte and Li inventory in SEI formation for durable batteries. The ultrathin SEI is generated in a dimethoxy(dimethyl)silane (DMDMS)-based electrolyte. Compared with the dispersed contact ion pairs in routine electrolytes, DMDMS, with both ultrahigh solubility of Li salts and weak solvating power, is favorable for the formation of joint aggregates in the electrolyte. The joint aggregates exhibit a fast defluorination rate, inducing uniform and compact nucleation sites on the Li metal surface. The compact nucleation sites grow simultaneously and subsequently come into contact with each other. This process rapidly passivates the exposed anode surface and facilitates the formation of an ultrathin SEI. The prototype pouch cell (≥500 Wh kg^-1) with the ultrathin SEI achieves 190 cycles and can operate at -30 °C with 66% energy retention. This work reveals the formation mechanism of the SEI and demonstrates the potential of meticulous SEI engineering in the development of durable Li metal batteries.

Polymer electrolytes paired with lithium-rich manganese-based layered oxide (LRMO) cathodes and anode-free cell design are considered one of the most promising high-energy-density and high-safety systems^1,2,3,4. However, the unstable anode morphological changes and the irreversible anionic reactions at the electrolyte–cathode interfaces induce oxygen escape and catalytic decomposition of polymer electrolytes, resulting in severe interfacial degradation and poor cycling stability. Here we design an in-built fluoropolyether-based polymer electrolyte composed of strongly solvating polyether and weakly solvating fluorohydrocarbon pendants, creating an anion-rich solvation structure and thus anion-derived fluorine-rich interfacial layers on the cathode and anode to resist interfacial issues. The LRMO cathode exhibits improved oxygen redox reversibility with substantially reduced oxygen-involving interfacial side reactions. This quasi-solid-state polymer electrolyte with 30 wt% trimethyl phosphate enables an LRMO cathode with a reversible high-areal-capacity cycling (>8 mAh cm⁻²) in pouch cells and long-term stability (>500 cycles at 25 °C) in coin cells, respectively. The pouch cells exhibit an energy density of 604 Wh kg⁻¹ (1,027 Wh l⁻¹) and excellent safety under a nail penetration at a fully charged condition. Our work, therefore, provides a promising direction for creating practical high-energy-density and high-safety lithium batteries.

This review comprehensively presents the progress in artificial intelligence-assisted discovery of molecules for rechargeable batteries and outlines future research directions.

A solvent-free molten salt electrolyte with enhanced Li⁺-transport kinetics was reported for high-temperature lithium batteries. The cation–cation concerted effect and inorganic CEI/SEI interphases endow fast-cycling and long-cycling abilities.

Developing rechargeable batteries that operate within a wide temperature range and possess high safety has become necessary with increasing demands. Rapid and accurate assessment of the melting points (MPs), boiling points (BPs), and flash points (FPs) of electrolyte molecules is essential for expediting battery development. Herein, we introduce Knowledge‐based electrolyte Property prediction Integration (KPI), a knowledge–data dual‐driven framework for molecular property prediction of electrolytes. Initially, the KPI collects molecular structures and properties, and then automatically organizes them into structured datasets. Subsequently, interpretable machine learning further explores the structure–property relationships of molecules from a microscopic perspective. Finally, by embedding the discovered knowledge into property prediction models, the KPI achieved very low mean absolute errors of 10.4, 4.6, and 4.8 K for MP, BP, and FP predictions, respectively. The KPI reached state‐of‐the‐art results in 18 out of 20 datasets. Utilizing molecular neighbor search and high‐throughput screening, 15 and 14 promising molecules, with and without Chemical Abstracts Service Registry Number, respectively, were predicted for wide‐temperature‐range and high‐safety batteries. The KPI not only accurately predicts molecular properties and deepens the understanding of structure–property relationships but also serves as an efficient framework for integrating artificial intelligence and domain knowledge.

Developing rechargeable batteries that operate within a wide temperature range and possess high safety has become necessary with increasing demands. Rapid and accurate assessment of the melting points (MPs), boiling points (BPs), and flash points (FPs) of electrolyte molecules is essential for expediting battery development. Herein, we introduce Knowledge‐based electrolyte Property prediction Integration (KPI), a knowledge–data dual‐driven framework for molecular property prediction of electrolytes. Initially, the KPI collects molecular structures and properties, and then automatically organizes them into structured datasets. Subsequently, interpretable machine learning further explores the structure–property relationships of molecules from a microscopic perspective. Finally, by embedding the discovered knowledge into property prediction models, the KPI achieved very low mean absolute errors of 10.4, 4.6, and 4.8 K for MP, BP, and FP predictions, respectively. The KPI reached state‐of‐the‐art results in 18 out of 20 datasets. Utilizing molecular neighbor search and high‐throughput screening, 15 and 14 promising molecules, with and without Chemical Abstracts Service Registry Number, respectively, were predicted for wide‐temperature‐range and high‐safety batteries. The KPI not only accurately predicts molecular properties and deepens the understanding of structure–property relationships but also serves as an efficient framework for integrating artificial intelligence and domain knowledge.

Lithium (Li) metal batteries (LMBs) are regarded as one of the most promising energy storage systems due to their ultrahigh theoretical energy density. However, the high reactivity of the Li anodes leads to the decomposition of the electrolytes, presenting a huge impediment to the practical application of LMBs. The routine trial-and-error methods are inefficient in designing highly stable solvent molecules for the Li metal anode. Herein, a data-driven approach is proposed to probe the origin of the reductive stability of solvents and accelerate the molecular design for advanced electrolytes. A large database of potential solvent molecules is first constructed using a graph theory-based algorithm and then comprehensively investigated by both first-principles calculations and machine learning (ML) methods. The reductive stability of 99% of the electrolytes decreases under the dominance of ion-solvent complexes, according to the analysis of the lowest unoccupied molecular orbital (LUMO). The LUMO energy level is related to the binding energy, bond length, and orbital ratio factors. An interpretable ML method based on Shapley additive explanations identifies the dipole moment and molecular radius as the most critical descriptors affecting the reductive stability of coordinated solvents. This work not only affords fruitful data-driven insight into the ion-solvent chemistry but also unveils the critical molecular descriptors in regulating the solvent's reductive stability, which accelerates the rational design of advanced electrolyte molecules for next-generation Li batteries.