major obstacles in the development of all-solid-state batteries (ASSBs) with high energy density, high intrinsic safety, and long service life. [1][2][3][4][5] Compared with modification (such as doping, [6,7] coating [8][9][10][11][12] ) on well-known SSEs, high-throughput screenings on less-investigated candidate materials with high ion conductivities and wide ESWs can provide a much larger database for SSE selection, thereby tremendously accelerating the development of ASSBs. [13][14][15][16] Recently, the rapid development of materials databases (e.g., Materials Project, [17] Inorganic Crystal Structure Database [18,19] ) has enabled the possibility for such high-throughput screenings. Jun et al. demonstrated the strong predictive power of the corner-sharing structural descriptor in high-throughput screening, leading to the discovery of 10 new oxide frameworks predicted to exhibit superionic conductivity. [15] In our previous work, 1270 possible fast ion conductors with low ionic migration energy barriers [20] were identified from the Inorganic Crystal Structure Database (released 2016/2) [18,19] based on the filter combining geometric analysis [21,22] and bond valence site energy (BVSE) methods. [23,24] However, a critical question still remains in the screening of materials with wide ESWs: how to develop a reliable high-throughput method to predict ESW accurately?The accurate prediction of the electrochemical stability windows (ESWs) enables the rational design of solid state electrolytes (SSEs). Currently, the ESW prediction is based on direct and indirect decomposition analysis methods (DDAM and IDAM). However, DDAM/IDAM can only involve thermodynamically/kinetically favorable decomposition pathway, both resulting in the large deviation between the predicted ESW and the experimental one. Specifically, certain excellent candidate SSEs may be continuously neglected in the highthroughput screening due to underpredicted ESWs. Herein, a high-accuracy ESW prediction method is proposed enabling dynamical determination of the appropriate decomposition pathway by analyzing the electronic conductivities of all direct and indirect decomposition products. Following this, a high-throughput computation is performed on the ESWs of 328 possible fast Li-ion conductors with low ionic migration energy barriers from the previous research, obtaining good agreement with the available experimental results (Li 10 GeP 2 S 12 and Li 7 La 3 Zr 2 O 12 ). Furthermore, six previously neglected fluorides exhibiting ESWs over 4 V, oxidation potentials exceeding 6 V, excellent phase stability, and interfacial compatibility with seven typical cathodes are reclaimed as promising SSEs. This work demonstrates a strategy to accelerate the SSE development by improving the accuracy of the ESW prediction and enlarging the database of promising SSEs.