Machine Learning‐Assisted Property Prediction of Solid‐State Electrolyte

Abstract

Machine learning (ML) exhibits substantial potential for predicting the properties of solid‐state electrolytes (SSEs). By integrating experimental or/and simulation data within ML frameworks, the discovery and development of advanced SSEs can be accelerated, ultimately facilitating their application in high‐end energy storage systems. This review commences with an introduction to the background of SSEs, including their explicit definition, comprehensive classification, intrinsic physical/chemical properties, underlying mechanisms governing their conductivity, challenges, and future developments. An in‐depth explanation of the ML methodology is also elucidated. Subsequently, the key factors that influence the performance of SSEs are summarized, including thermal expansion, modulus, diffusivity, ionic conductivity, reaction energy, migration barrier, band gap, and activation energy. Finally, it is offered perspectives on the design prerequisites for upcoming generations of SSEs, focusing on real‐time property prediction, multi‐property optimization, multiscale modeling, transfer learning, automation and high‐throughput experimentation, and synergistic optimization of full battery, all of which are crucial for accelerating the progress in SSEs. This review aims to guide the design and optimization of novel SSE materials for the practical realization of efficient and reliable SSEs in energy storage technologies.

Interfacial Engineering of MoS2@CoS2 Heterostructure Electrocatalysts for Effective pH-Universal Hydrogen Evolution Reaction

“…Electron paramagnetic resonance (EPR) measurements were conducted to examine the existence of the defects in MoS 2 , CoS 2 , , and MoS 2 @CoS 2 . As illustrated in Figure a, the MoS 2 @CoS 2 heterojunction exhibits the strongest EPR peak, while MoS 2 has a nearly negligible peak, the intensity of which is proportional to the defect content. , These defects could stem from the disordered structure creating active sulfur vacancies during the reaction. , Under almost identical preparation conditions, it is evident that the CoS 2 sample also has a notable sulfur vacancy peak, while the MoS 2 sample has an almost negligible peak. Consequently, we tentatively conclude that the sulfur vacancies in the MoS 2 @CoS 2 heterojunction are primarily in CoS 2 .…”

Constructing self-supported Pt/MoO2 on molybdenum mesh for a highly efficient hydrogen evolution reaction

“…The semicircles observed at high frequencies represent the charge-transfer resistance (R ct ). 20,54,55 Notably, with an overpotential held constant at 20 mV, the R ct value for Pt/MoO 2 @Mo (4.1 Ω) was markedly lower than that of MoO 2 @Mo (125.6 Ω) and Pt@Mo (62.2 Ω), indicating a swift transmission of electrons and ions in the hydrogen evolution process. Double-layer capacitance (C dl ) is a crucial parameter for assessing the electrochemical surface area of a catalyst.…”

Merits and Demerits of Machine Learning of Ferroelectric, Flexoelectric, and Electrolytic Properties of Ceramic Materials

“…ML was utilized to accelerate the discovery of new solid electrolytes with high ionic conductivity for the application to all-solid-state batteries [ 10 , 13 , 67 , 165 , 166 , 167 , 168 , 169 , 170 , 171 ]. Fujimura et al [ 67 ] reported that a ML model could predict ionic conductivity at 373 K for various compositions in the system , where or As, and or Si, and .…”

“…The predicted ionic conductivities range from about S to about S . However, it can be pointed out that all-dislocation-ceramics [ 58 ], which could be a new type of solid electrolyte with high ionic conductivity without dendrite formation filled with appropriate dislocations, could not be predicted by ML because ML could not predict beyond the parameter space of the training (experimental and theoretical) data [ 10 , 13 , 67 , 165 , 166 , 167 , 168 , 169 , 170 , 171 ].…”

“…Furthermore, ML could be computationally more economical compared to first principles calculations and could save time and effort [ 2 , 7 , 10 , 11 ]. ML could also save expensive and time-consuming experiments [ 12 , 13 , 14 , 15 , 16 , 17 ]. On the other hand, ML needs large amounts of pre-existing experimental data of high quality, which is not always possible [ 2 , 18 , 19 ].…”