Polyimide separators for rechargeable batteries

Separators are indispensable components of modern electrochemical energy storage devices such as lithium-ion batteries(LIBs).They perform the critical function of physically separating the electrodes to prevent short-circuits while permitting the ions to pass through.While conventional separators using polypropylene(PP) and polyethylene(PE) are prone to shrinkage and melting at relatively high temperatures(150℃ or above) causing short circuits and thermal runaway,separators made of thermally stable polyimides(PIs) are electrochemically stable and resistant to high temperatures,and possess good mechanical strength-making them a promising solution to the safety concerns of LIBs.In this review,the research progress on PI separators for use in LIBs is summarized with a special focus on molecular design and microstructural control.In view of the significant progress in advanced chemistries beyond LIBs,recent advances in PI-based membranes for applications in lithium-sulfur,lithium-metal,and solid-state batteries are also reviewed.Finally,practical issues are also discussed along with their prospects.

Computational discovery of energy materials in the era of big data and machine learning:A critical review

The discovery of novel materials with desired properties is essential to the advancements of energy-related technologies.Despite the rapid development of computational infrastructures and theoretical approaches,progress so far has been limited by the empirical and serial nature of experimental work.Fortunately,the situation is changing thanks to the maturation of theoretical tools such as density functional theory,high-throughput screening,crystal structure prediction,and emerging approaches based on machine learning.Together these recent innovations in computational chemistry,data informatics,and machine learning have acted as catalysts for revolutionizing material design and hopefully will lead to faster kinetics in the development of energy-related industries.In this report,recent advances in material discovery methods are reviewed for energy devices.Three paradigms based on empiricism-driven experiments,database-driven high-throughput screening,and data informatics-driven machine learning are discussed critically.Key methodological advancements involved are reviewed including high-throughput screening,crystal structure prediction,and generative models for target material design.Their applications in energy-related devices such as batteries,catalysts,and photovoltaics are selectively showcased.

Identifying Hidden Li–Si–O Phases for Lithium-Ion Batteries via First-Principle Thermodynamic Calculations

SiO–based materials are promising alloys and conversion-type anode materials for lithium-ion batteries and are recently found to be excellent dendrite-proof layers for lithium-metal batteries.However,only a small fraction of the Li–Si–O compositional space has been reported,significantly impeding the understanding of the phase transition mechanisms and the rational design of these materials both as anodes and as protection layers for lithium-metal anodes.Herein,we identify three new thermodynamically stable phases within the Li–Si–O ternary system(Li_(2)SiO_(5),Li_(4)SiO_(6),and Li_(4)SiO_(8))in addition to the existing records via first-principle calculations.The electronic structure simulation shows that Li_(2)SiO_(5)and Li_(4)SiO_(8)phases are metallic in nature,ensuring high electronic conductivity required as electrodes.Moduli calculations demonstrate that the mechanical strength of Li–Si–O phases is much higher than that of lithium metal.The diffusion barriers of interstitial Li range from 0.1 to 0.6 eV and the interstitial Li hopping serves as the dominating diffusion mechanism in the Li–Si–O ternary systems compared with vacancy diffusion.These findings provide a new strategy for future discovery of improved alloying anodes for lithium-ion batteries and offer important insight towards the understanding of the phase transformation mechanism of alloy-type protection layers on lithium-metal anodes.

Editorial for the special issue“Machine learning and artificial intelligence for energy materials”

With the development of advanced methods in the area of machine learning(ML),artificial intelligence(AI)is rapidly revolutionizing many fields and is starting to change the landscapes of physics and chemistry.Over the past decade,AI-assisted designs of novel materials have been gradually reshaping how researchers explore new chemistries for energy conversion and storage.In addition,innovative data-driven techniques have led to unprecedented perspectives on the physics and chemistries of the reactions/processes involved.With this background in mind,this themed issue of“Machine Learning and Artificial Intelligence for Energy Materials”presents a collection of 5 highly selected papers in machine learning and artificial intelligence for energy materials.1–5 By including comprehensive reviews,and original research report,this issue aims at providing a broad overview of the theoretical advancements and applications of related methods in the areas of energy storage and conversion.

Computational insights into the ionic transport mechanism and interfacial stability of the Li_(2)OHCl solid-state electrolyte

Lithium-rich antiperovskites are promising solid-state electrolytes for all-solid-state lithium-ion batteries because of their high structural tolerance and good formability.However,the experimentally reported proton-free Li_(3)OCl is plagued by its inferior interfacial compatibility and harsh synthesis conditions.In contrast,Li_(2)OHCl is a thermodynamically favored phases and is easier to achieve than Li_(3)OCl.Due to the proton inside this material,it exhibits interesting lithium diffusion mechanisms.Herein,we present a systematic investigation of the ionic transport,phase stability,and electrochemicalchemical stability of Li_(2)OHCl using first-principles calculations.Our results indicate that Li_(2)OHCl is thermodynamically metastable and is an electronic insulator.The wide electrochemical stability window and high chemical stability of Li_(2)OHCl against various electrodes are confirmed.The charged defects are the dominant conduction mechanism for Li-transport,with a low energy barrier of~0.50 eV.The Li-ion conductivity estimated by ab initio molecular dynamics simulations is about 1.3×10^(-4) S cm^(-1) at room temperature.This work identifies the origin of the high interfacial stability and ionic conductivity of Li_(2)OHCl,which can further lead to the design of such as a cathode coating.Moreover,all computational methods for calculating the properties of Li_(2)OHCl are general and can guide the design of highperformance solid-state electrolytes.

Towards prediction of ordered phases in rechargeable battery chemistry via group–subgroup transformation

The electrochemical thermodynamic and kinetic characteristics of rechargeable batteries are critically influenced by the ordering of mobile ions in electrodes or solid electrolytes.However,because of the experimental difficulty of capturing the lighter migration ion coupled with the theoretical limitation of searching for ordered phases in a constrained cell,predicting stable ordered phases involving cell transformations or at extremely dilute concentrations remains challenging.Here,a group-subgroup transformation method based on lattice transformation and Wyckoff-position splitting is employed to predict the ordered ground states.We reproduce the previously reported Li_(0.75)CoO_(2),Li_(0.8333)CoO_(2),and Li_(0.8571)CoO_(2)phases and report a new Li_(0.875)CoO_(2)ground state.Taking the advantage of Wyckoff-position splitting in reducing the number of configurations,we identify the stablest Li_(0.0625)C_(6) dilute phase in Li-ion intercalated graphite.We also resolve the Li/La/vacancy ordering in Li_(3x)La_(2/3−x)TiO_(3)(0<x<0.167),which explains the observed Li-ion diffusion anisotropy.These findings provide important insight towards understanding the rechargeable battery chemistry.

Author Correction: Towards prediction of ordered phases in rechargeable battery chemistry via group-subgroup transformation

The original version of this Article contained error in DATA AVAILABILITY,in which the website hyperlink is not valid and should be revised to https://github.com/shuhebing/gsop.The same error also occurs in CODE AVAILABILITY,in which the website hyperlink should also be revised to https://github.com/shuhebing/gsop.