The screening of novel materials with good performance and the modelling of quantitative structureactivity relationships(QSARs),among other issues,are hot topics in the field of materials science.Traditional experiments and computational modelling often consume tremendous time and resources and are limited by their experimental conditions and theoretical foundations.Thus,it is imperative to develop a new method of accelerating the discovery and design process for novel materials.Recently,materials discovery and design using machine learning have been receiving increasing attention and have achieved great improvements in both time efficiency and prediction accuracy.In this review,we first outline the typical mode of and basic procedures for applying machine learning in materials science,and we classify and compare the main algorithms.Then,the current research status is reviewed with regard to applications of machine learning in material property prediction,in new materials discovery and for other purposes.Finally,we discuss problems related to machine learning in materials science,propose possible solutions,and forecast potential directions of future research.By directly combining computational studies with experiments,we hope to provide insight into the parameters that affect the properties of materials,thereby enabling more efficient and target-oriented research on materials discovery and design.
The physical fundamentals and influences upon electrode materials＇ open-circuit voltage （OCV） and the spatial distribution of electrochemical potential in the full cell are briefly reviewed. We hope to illustrate that a better understanding of these scientific problems can help to develop and design high voltage cathodes and interfaces with low Ohmic drop. OCV is one of the main indices to evaluate the performance of lithium ion batteries （LIBs）, and the enhancement of OCV shows promise as a way to increase the energy density. Besides, the severe potential drop at the interfaces indicates high resistance there, which is one of the key factors limiting power density.
An overview of ion transport in lithium-ion inorganic solid state electrolytes is presented, aimed at exploring and de signing better electrolyte materials. Ionic conductivity is one of the most important indices of the performance of inorganic solid state electrolytes. The general definition of solid state electrolytes is presented in terms of their role in a working cell （to convey ions while isolate electrons）, and the history of solid electrolyte development is briefly summarized. Ways of using the available theoretical models and experimental methods to characterize lithium-ion transport in solid state elec- trolytes are systematically introduced. Then the various factors that affect ionic conductivity are itemized, including mainly structural disorder, composite materials and interface effects between a solid electrolyte and an electrode. Finally, strategies for future material systems, for synthesis and characterization methods, and for theory and calculation are proposed, aiming to help accelerate the design and development of new solid electrolytes.
Volume effect has been extensively investigated in several families of solid electrolytes, i.e., expanding the skeleton lattice by bigger-size substitution favors the ionic conduction. However, this effect is not applicable in α-Li2SO4 and α-Na3PO4 based inorganic ionic plastic crystal electrolytes, a unique family of solid electrolytes. Here, it is proposed that the underlying rotational motion effect of polyanion, which is actually inhibited by the substitution of bigger-size polyanion in single-phase solid solution region and causes the unexpected lowering of the ionic conductivity instead, should need the more consideration. Furthermore, through utilizing the rotational motion effect of polyanion, it is given that a new explanation of the ionic conductivities of Li10MP2S12 （M = Si, Ge, Se） electrolytes deviating from the volume effect. These results inspire new vision of rationalization of the high-performance solid electrolytes by tuning the rotational motion effect of polyanion.
