In what ways does the materials genome approach promote the development of new materials for lithium batteries? -Lithium - Ion Battery Equipment
In recent years, in the process of research and development of new materials for lithium secondary batteries, high-throughput computing theoretical tools and research platforms based on the idea of material genome have been gradually established. On this platform, by combining calculation methods with different precisions, ion-based Material screening of transport properties; by introducing data mining algorithms in informatics into the analysis of high-throughput computational data, the feasibility of material big data interpretation is confirmed. The above platform realizes high-throughput screening and optimization of solid electrolytes in lithium batteries As a demonstration application for the research and development of new materials in terms of design and design, two compounds, Li2SiO3 and Li2SnO3, that can be used as lithium-rich cathode coating materials were obtained through high-throughput computational screening, which effectively improved the cycle stability of lithium-rich cathodes; by doping Through high-throughput screening of strategies, a method to improve the ionic conductivity and stability of solid electrolyte β-Li3PS4 was obtained; a new oxysulfide solid electrolyte LiAlSO was designed through high-throughput structure prediction; and the zero-strain electrode material structure and performance were designed Big data analysis was attempted in the study of structure-activity relationships, and the design basis for zero-strain electrode materials was analyzed. The application of the above-mentioned material genome method in the research and development of lithium battery materials provides an opportunity to promote this new research and development model in the research and development of other types of materials. Supply possible.(Lithium - Ion Battery Equipment)
Traditional battery material research and development is based on a development model characterized by a "trial and error method". The cycle from discovery to application is very long, generally taking 20 years or more. The proposal of the "Materials Genome Project" provides new ideas for the development of new materials for lithium batteries. The key to "materials genome" scientific research is to achieve "high-throughput" materials research and development, that is, to complete "a batch" of material samples concurrently instead of "one" material sample.
Computational simulation, preparation and characterization, that is, high-throughput calculation, high-throughput preparation and high-throughput characterization, realize systematic screening and optimization of materials, thereby accelerating the process of materials from discovery to application. Using the "materials genetic engineering" method, through High-throughput, multi-scale large-scale calculations and searches, with the help of data mining technology and methods, are expected to screen out new materials that may have excellent properties. A high-throughput screening process that combines different precision calculation methods is designed: first, narrow the scope through element screening according to the usage conditions of the material, then use fast bond valence calculation to conduct preliminary screening to remove compounds with large ion transport barriers, and finally use Density functional-based simulations further accurately calculate the materials screened in the previous step to obtain the final candidate materials, thereby effectively improving the overall screening efficiency and achieving efficient screening of fast ion conductors in lithium secondary battery materials.
1. Screening of new coating materials for lithium-rich cathodes
By using high-throughput computational screening and comprehensively considering structural matching, diffusion channels, conductivity and other factors, two coating compounds Li2SiO3 and Li2SnO3 that may match the lithium-rich cathode materials of lithium batteries were discovered. Both materials are ionic compounds, have good ionic conductivity, and are similar in chemical structure to the parent phase material Li2MnO3 in lithium-rich materials ((1..x)Li2MnO3xLiMO2), so you can try to choose them as rich lithium materials. Surface modification layer of lithium material.
2. High-throughput computational screening of solid electrolyte-Li3PS4 optimization modification method
By combining density functional calculations with bond valence calculations, a large number of doping modification methods can be screened for high-throughput calculations. Density functional calculations that can accurately determine the crystal structure are used to obtain doped atoms. Position information, and then quickly select the doping method that is beneficial to reducing the lithium ion migration barrier through bond valence calculation. By doping the P site of β-Li3PS4 with Sb, Zn, Al, Ga, Si, Ge, Sn, And research on O doping of S sites found that replacing part of the sulfur in the crystal lattice with oxygen or co-doping β-Li3PS4 with zinc and oxygen elements can effectively improve its ion conductivity.
After obtaining the optimized method of material modification through high-throughput computational screening, high-precision calculations based on density functional can effectively reveal the mechanism of improvement of material properties by doping
3. High-throughput structure prediction method discovers a new structure of solid electrolyte LiAlSO
By using CAPSO software to construct crystal structures with various space groups in the element space of Li-Al-S-O, and performing structural optimization and energy calculations on them, the particle swarm optimization algorithm is used to generate new structures based on the structures with low energy. During this optimization process, the most stable structure formed by these four elements in a ratio of 1:1:1:1 was gradually found. The calculation results show that this new oxysulfide LiAlSO has an orthorhombic structure similar to -NaFeO2 , the AlS2O2 layers are arranged parallel along the b-axis direction, Li ions are located between the layers and S and O form a twisted tetrahedral unit.
4. Data mining methods to study the correlation between structural and volume changes in zero-strain electrode materials
High-throughput calculations and high-throughput experimental tests based on the idea of material genes not only provide new research ideas for the field of new materials research and development, but also bring exponentially more data information, laying the foundation for the application of big data methods in materials science. foundation. Machine learning technology has been used to obtain statistical models between material properties and various complex physical factors, such as finding new thermodynamically stable compounds by predicting the atomization energy of molecules.
By using the "Leave-One-Out" method for evaluation, it was found that the Q2 index obtained when using 11 relevant variables (11 components) in the above problem was the largest, indicating that the model obtained at this time was the most stable. Further factor importance analysis showed that ( Figure 6), although the ionic radius is an important determinant of the lattice volume change, the volume change is not only related to the ionic radius. The bonding parameters of the transition metal and the local structure of the transition metal oxygen octahedron also play a role in the volume change. Purpose. On the basis of this model, a cathode material containing a variety of transition metals can be constructed to jointly adjust the volume change of the system during the deintercalation of lithium and minimize the lattice volume change rate caused by changes in lithium content.
For the research and development of solid-state lithium secondary batteries, we promptly carried out the exploration of high-throughput calculation methods suitable for lithium battery materials, developed calculation methods including ion transport properties and integrated different precisions, and established a lithium-ion based The high-throughput calculation screening and optimization process of transport barriers realizes functions such as concurrent calculation of multiple materials, monitoring of calculation intermediate processes, analysis of calculation results, and judgment and assessment of material performance based on calculation results. Using this independently developed high-tech The flux calculation platform has successfully screened lithium-containing oxides in the inorganic crystal structure database and discovered two coating materials that can improve the cycle performance of lithium-rich cathodes; and has conducted a doping method for sulfide solid electrolytes. Through mass calculation optimization, the design idea of constructing a solid electrolyte in which a variety of anions coexist was proposed, and a new oxysulfide solid electrolyte was invented. Based on the data collected by high-throughput calculations, an attempt was made to delithiate the cathode material. The data analysis method of multiple linear regression was used in the study of volume changes in the study, which provides the possibility to further introduce artificial intelligence methods such as data mining and machine learning in the research and development of lithium secondary batteries.
Traditional battery material research and development is based on a development model characterized by a "trial and error method". The cycle from discovery to application is very long, generally taking 20 years or more. The proposal of the "Materials Genome Project" provides new ideas for the development of new materials for lithium batteries. The key to "materials genome" scientific research is to achieve "high-throughput" materials research and development, that is, to complete "a batch" of material samples concurrently instead of "one" material sample.
Computational simulation, preparation and characterization, that is, high-throughput calculation, high-throughput preparation and high-throughput characterization, realize systematic screening and optimization of materials, thereby accelerating the process of materials from discovery to application. Using the "materials genetic engineering" method, through High-throughput, multi-scale large-scale calculations and searches, with the help of data mining technology and methods, are expected to screen out new materials that may have excellent properties. A high-throughput screening process that combines different precision calculation methods is designed: first, narrow the scope through element screening according to the usage conditions of the material, then use fast bond valence calculation to conduct preliminary screening to remove compounds with large ion transport barriers, and finally use Density functional-based simulations further accurately calculate the materials screened in the previous step to obtain the final candidate materials, thereby effectively improving the overall screening efficiency and achieving efficient screening of fast ion conductors in lithium secondary battery materials.
1. Screening of new coating materials for lithium-rich cathodes
By using high-throughput computational screening and comprehensively considering structural matching, diffusion channels, conductivity and other factors, two coating compounds Li2SiO3 and Li2SnO3 that may match the lithium-rich cathode materials of lithium batteries were discovered. Both materials are ionic compounds, have good ionic conductivity, and are similar in chemical structure to the parent phase material Li2MnO3 in lithium-rich materials ((1..x)Li2MnO3xLiMO2), so you can try to choose them as rich lithium materials. Surface modification layer of lithium material.
2. High-throughput computational screening of solid electrolyte-Li3PS4 optimization modification method
By combining density functional calculations with bond valence calculations, a large number of doping modification methods can be screened for high-throughput calculations. Density functional calculations that can accurately determine the crystal structure are used to obtain doped atoms. Position information, and then quickly select the doping method that is beneficial to reducing the lithium ion migration barrier through bond valence calculation. By doping the P site of β-Li3PS4 with Sb, Zn, Al, Ga, Si, Ge, Sn, And research on O doping of S sites found that replacing part of the sulfur in the crystal lattice with oxygen or co-doping β-Li3PS4 with zinc and oxygen elements can effectively improve its ion conductivity.
After obtaining the optimized method of material modification through high-throughput computational screening, high-precision calculations based on density functional can effectively reveal the mechanism of improvement of material properties by doping
3. High-throughput structure prediction method discovers a new structure of solid electrolyte LiAlSO
By using CAPSO software to construct crystal structures with various space groups in the element space of Li-Al-S-O, and performing structural optimization and energy calculations on them, the particle swarm optimization algorithm is used to generate new structures based on the structures with low energy. During this optimization process, the most stable structure formed by these four elements in a ratio of 1:1:1:1 was gradually found. The calculation results show that this new oxysulfide LiAlSO has an orthorhombic structure similar to -NaFeO2 , the AlS2O2 layers are arranged parallel along the b-axis direction, Li ions are located between the layers and S and O form a twisted tetrahedral unit.
4. Data mining methods to study the correlation between structural and volume changes in zero-strain electrode materials
High-throughput calculations and high-throughput experimental tests based on the idea of material genes not only provide new research ideas for the field of new materials research and development, but also bring exponentially more data information, laying the foundation for the application of big data methods in materials science. foundation. Machine learning technology has been used to obtain statistical models between material properties and various complex physical factors, such as finding new thermodynamically stable compounds by predicting the atomization energy of molecules.
By using the "Leave-One-Out" method for evaluation, it was found that the Q2 index obtained when using 11 relevant variables (11 components) in the above problem was the largest, indicating that the model obtained at this time was the most stable. Further factor importance analysis showed that ( Figure 6), although the ionic radius is an important determinant of the lattice volume change, the volume change is not only related to the ionic radius. The bonding parameters of the transition metal and the local structure of the transition metal oxygen octahedron also play a role in the volume change. Purpose. On the basis of this model, a cathode material containing a variety of transition metals can be constructed to jointly adjust the volume change of the system during the deintercalation of lithium and minimize the lattice volume change rate caused by changes in lithium content.
For the research and development of solid-state lithium secondary batteries, we promptly carried out the exploration of high-throughput calculation methods suitable for lithium battery materials, developed calculation methods including ion transport properties and integrated different precisions, and established a lithium-ion based The high-throughput calculation screening and optimization process of transport barriers realizes functions such as concurrent calculation of multiple materials, monitoring of calculation intermediate processes, analysis of calculation results, and judgment and assessment of material performance based on calculation results. Using this independently developed high-tech The flux calculation platform has successfully screened lithium-containing oxides in the inorganic crystal structure database and discovered two coating materials that can improve the cycle performance of lithium-rich cathodes; and has conducted a doping method for sulfide solid electrolytes. Through mass calculation optimization, the design idea of constructing a solid electrolyte in which a variety of anions coexist was proposed, and a new oxysulfide solid electrolyte was invented. Based on the data collected by high-throughput calculations, an attempt was made to delithiate the cathode material. The data analysis method of multiple linear regression was used in the study of volume changes in the study, which provides the possibility to further introduce artificial intelligence methods such as data mining and machine learning in the research and development of lithium secondary batteries.
