Currently commercialized lithium-ion batteries all use flammable organic electrolytes, in which lithium salts are dissolved in organic solutions and contain additives; gel electrolytes are colloidal electrolytes formed by dissolving salts and solvents in polymers at the same time. When the battery is charged and discharged, the internal temperature rises easily causing gas expansion, which can lead to rupture of the packaging material, leakage, fire, or even explosion. In addition to potential safety hazards, the irreversible reaction of the electrolyte can consume the active Li in the electrode and reduce the cycle capacity of the battery; side reaction products may increase resistance and affect charge and discharge power. In addition, such batteries are difficult to miniaturize, and improper recycling may pollute the environment. The need for safety has led to interest in more electrolyte types. Solid polymer electrolytes combine lithium salts with polymers and can be used in flexible and bendable batteries. Because there is no free liquid electrolyte, leakage is avoided, and the integrated structure of electrodes, electrolytes and diaphragms makes the battery more resistant to impact, vibration, and deformation, which greatly alleviates safety issues such as combustion and explosion. However, its conductivity is still difficult to meet application requirements. Inorganic ceramics are another competitive alternative electrolyte material. They are non-combustible, non-explosive, non-leakage and non-corrosive and are a fundamental solution to safety issues. Since inorganic solid electrolytes have periodic crystal structures, their properties are easy to calculate. Combining calculations, databases and experiments based on the material genome idea can greatly reduce the material development cycle and research and development costs. Progress in high-throughput computing of inorganic solid electrolytes for lithium (ion) batteries As early as 2011, my country has attached great importance to computational simulation research on lithium-ion batteries. The Xiangshan Conference with the theme of "Materials Science Systems Engineering" was held in Beijing that year. Lithium-ion battery materials were used as one of the representative demonstration materials. It was planned to apply material databases, open source software tools and machine learning methods based on computational simulations to lithium ( The entire industry chain of lithium (ion) battery research and development systematically improves energy density, reduces costs, improves service life, and greatly shortens the research and development cycle of the lithium (ion) battery industry chain. An overview of the Ceder team’s progress. Professor Ceder’s team is the leader of the Materials Genome Project and a pioneer in the field of lithium battery computing. Recommended reading: CederGroup, the top team in the field of materials computing. Ceder Team: Choose 3 out of 100,000! High-throughput computational screening of battery cathode coating materials Professor Ceder is one of the initiators of the Materials Genome Project. He formerly worked at the Massachusetts Institute of Technology and now works at the University of California, Berkeley. His research group develops machine learning methods to predict new structures and mine missing materials in existing databases, build new databases based on existing databases, and perform first-principles calculations on the materials in them. The construction of an open source interactive visualization platform has been completed, and it is expected to combine the design, calculation, verification, release and analysis testing of materials to increase the speed of material development.


Designprinciples for solid-statelithiumsuperionicconductors.NatMater. In a 2015 Nature Materials article (pictured above), they proposed the body-centered cubic stacking of oxygen from the perspective of oxygen ion stacking and lithium ion channels, indicating the possibility of high conductivity. InterfaceStabilityinSolid-StateBatteries,Chem.Mater.


Considering that the interface resistance between solid electrolytes and electrode materials is often an important source of full cell resistance, they extensively predicted the stability between different electrolytes/coatings/electrode materials in a Chem.Mater. article (above), and possible interfacial phase products formed by chemical reactions, and calculate the ionic conductivity of the interfacial phase to predict high-performance combinations. Ceder's students currently working in various schools are still doing a lot of related work for high-throughput computing of all-solid-state batteries. Mo Yifei from the University of Maryland conducted high-throughput calculations on the electrochemical stability of solid electrolyte materials relative to positive/negative electrodes from the perspective of electrochemical windows. In the service process of all-solid-state batteries, the mechanical properties of solid electrolytes are equally important. Shyue Ping Ong of UC San Diego calculated the elastic properties of alkali metal superionic conductors. Considering that inorganic fast ion conductors can not only be used as solid electrolyte materials to replace organic electrolytes, they can also be used as separator materials for aqueous batteries. They also used first principles to calculate the stability of lithium-ion and sodium-ion superion conductors relative to aqueous electrolytes under different potentials and acid-base conditions, and made a Pourbaix diagram. They also pointed out that the stability of oxide materials is usually superior. For sulfide/halide materials, the alkali metal type also affects the stability of the material relative to the environment. Experimental and computational methods, how to obtain lithium ion channels? Having high lithium ion conductivity is the primary requirement for inorganic solid electrolyte materials, and connected lithium ion channels are a prerequisite for high conductivity. For experimental means, lithium ion channels can be directly obtained through neutron diffraction. For theoretical research, it can be calculated through first principles based on energy changes, bond valence method (BVM) based on changes in coordination environment, and geometric structure-based Voroni-Dirichlet segmentation, Colony surface, Procrystal analysis and other methods. . Brief introduction to the progress of Academician Chen Liquan's team. Academician Chen Liquan's research team from the Institute of Physics, Chinese Academy of Sciences obtained the structural data of materials from the International Center for Diffraction Data (ICDD) database, and extracted materials containing lithium, no heavy metals, and no variable valence elements as candidate databases. BVM calculates its ion channels.


Candidatestructuresforinorganiclithiumsolid-stateelectrolytesidentifiedbyhigh-throughputbond-valencecalculations.JournalofMateriomicsThe screening process is shown in the figure above. Materials with connected lithium ion channels are used as solid electrolyte candidate materials, and first-principles calculations are conducted on the target materials.


High-throughput design and optimization of fast lithium ion conductors by the combination of bond-valence method and density functional theory. SciRep. The research group used an improved method to express the bond valence parameters as Morse potential and electrostatic Coulomb potential to obtain the activation energy that connects the ion channels, and calculated values with density functional theory (DFT) Compare (see image above). Accurate calculation of common lithium-ion inorganic solid electrolyte materials. The electrolyte material is located between the positive and negative electrode materials and plays a role in transporting ions and isolating electrons. Therefore, oriented to practical applications, computational research on inorganic solid electrolyte materials mostly focuses on composition/component optimization, diffusion coefficient, transport mechanism, and stability relative to electrode materials and the environment of high conductivity materials. Since there are often excess lithium ions to occupy positions in fast ion conductors, establishing a disordered structural model is usually one of the difficulties in theoretical calculations. Disorder often contributes to the cooperative transition of lithium ions, which requires atomic-scale calculation methods to elucidate the transport mechanism. The following briefly describes the research progress of some calculations of common solid electrolyte materials. GoodenoughGoodenough and Hong proposed a high conductivity NASICON (NaSuperIonicCONductor) lithium ion inorganic solid electrolyte material LiM2(PO4)3, in which MO6 octahedron and PO4 tetrahedron form a covalent [M1M2P3O12]-skeleton, and conductive lithium ions are distributed in the skeleton to form Three-dimensional lithium ion channels. There are two types of site occupancy in lithium ions, A1 and A2. In the pure phase, the A1 site is fully occupied and the A2 site is left empty. Low-cost doping can partially occupy the A2 site. ConradR.StoldtConradR.Stoldt combined first-principles calculations with experimental results to discuss the entropy changes caused by different occupancy modes (see the figure below).


EnergeticsofiontransportinNasicon-typeelectrolytes.JournalofPhysicalChemistryC The rich doping substitution makes the NASICON structure one of the typical examples for studying the relationship between chemical doping-crystal structure-conductivity, among which Li1.3Al0.3Ti1.7(PO4)3 has the highest conductivity. However, the mechanism by which doping increases conductivity is not clear because the increases in carrier concentration and mobility are difficult to distinguish experimentally. HenryY.P.HongIn 1978, HenryY.P.Hong proposed a LISICON (LiSuperIonicCONductor) structure that also has a three-dimensional ion channel, Li14Zn(GeO4)4, and can be expanded to a γ-Li3PO4 system (xLi4MIVO4—(1-x)Li3MVO4; MIV =Ge, Ti; MV=As, V), this system can form a solid solution pure phase only within a limited concentration (x=0.4~0.6) and temperature range. The first-principles method is mainly used to calculate periodic structures. For this solid solution system, it is usually necessary to expand the unit cell to meet the stoichiometric ratio requirements, and the complex chemical environment and multiple transport mechanisms during the lithium ion diffusion process in this system bring problems to the calculation. A big challenge. IsaoTanakaIsaoTanaka uses the cluster expansion method to establish a wide range of solid solution component models, determines the order-disorder phase transition temperature and the ternary phase diagram of various doping/substituted components, and uses first-principles molecular dynamics methods to simulate The high temperature lithium ion conductivity is improved (see the figure below);


Accelerated materials design of lithium superionic conductors based on first-principles calculations and machine learning algorithms. AdvEnergyMater then combines experimental data, uses machine learning methods to predict the conductivity of each component at 100°C, and systematically optimizes the composition and conductivity of the structure. However, the control of the components of this system is limited, and the theoretical maximum conductivity still does not exceed 10-3S/cm. Ryoji Kanno proposed that the thio-LISICON structure (thio-LISICON) has a higher conductivity than the oxygen series. (DOI:10.1149/1.1379028)


Alithiumsuperionicconductor.NatMater reported in an article in NatureMaterials that Li10GeP2S12 has a room temperature lithium ion conductivity as high as 10-2S/cm, and combined first principles calculations to determine its crystal structure (see the picture above). The P/Ge fractional occupation of the tetrahedral 4d position brings difficulty to modeling. LiangChengdu pointed out in a JACS article that β-Li3PS4 is a high conductivity phase of the thio-γ-Li3PO4 system and its corresponding nanostructure; (DOI: 10.1021/ja3110895) John S. Tse calculated β-Li3PS4 using first principles molecular dynamics. and the diffusion coefficient of γ-Li3PS4. It is believed that the higher conductivity of the former comes from the intrinsic lithium vacancies, while the mobility of lithium ions in nanoclusters with more defective sites is higher. (DOI: 10.1016/j.commatsci.2015.05.022) KojiOhara used density functional theory and reversible Monte Carlo method, combined with experimental results to analyze the phase composition, local structure and electronic structure of the binary Li2S-P2S5 glass system. It is believed that the conductivity of the system can be improved by adjusting the structure of shared edges of PSx and LiSx polyhedrons and reducing the transfer of electrons between P and bridging S. (DOI: 10.1038/srep21302) Perovskite solid electrolyte In 1953, Tetsuhiro Katsumata synthesized Li0.5La0.5TiO3 perovskite solid electrolyte. (DOI: 10.1016/0167-2738(96)00116-6) In 1993, Academician Chen Liquan and others discovered that Li0.33La0.56TiO3 has the highest electrical conductivity. However, due to the asymmetry of the electron 2p orbit of O, two-dimensional of lithium ion channels. (DOI: 0.1016/0038-1098(93)90841-A) Nakayama Masanobu combined cluster expansion, Monte Carlo methods and first-principles calculations to obtain the temperature-dependent arrangement of La and vacancies. (DOI: 10.2109/jcersj2.117.911) Michele Catti established a space group model corresponding to each component based on the experimental results, and used the electrostatic potential distribution to predict the most likely occupied position of lithium ions or determine the most stable Li-La-vacancy distribution through ground state energy calculation. (DOI: 10.1021/cm0709469) Takahisa Ohno predicts the distribution pattern of lithium ions and the corresponding transport channels by analyzing the configuration characteristics of the energy valley in the lithium ion diffusion energy surface. (DOI: 10.1149/2.008306eel) R.W. Grimes and J.A. Kilner innovatively used a genetic algorithm to continuously inherit the "parent body (La-rich layer)" and "parent body () of the high ion conductivity local order structure". The "gene" of "poor La layer" eventually "reproduces" a high conductivity structure. (DOI: 10.1039/C4CP04834B) After the above structural model is established, the study of lithium ion transport mechanism is logical, which is closely related to Li/La/vacancy components and intra-layer/inter-layer sequence structure. However, the limiting factor in the overall conductivity of this type of material is often attributed to excessive grain boundary resistance. Garnet structure solid electrolyte In 2003 WernerJ.F.Wep discovered the garnet structure Li5La3M2O12 (M=Ta,

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