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Polymer architecture and self-assembly are of fundamental importance for us to investigate in order to design new functional materials. Helical macromolecules have been widely recognized as self-assembly templates to develop more functional materials. As we know, double helix structure is exceedingly rare in synthetic polymers. We had initially discovered a double helix conformation in an artificially synthesized lyotropic liquid crystal poly-2,2′-disulfonyl-4,4′-benzidine terephthalamide (PBDT), which can be prepared by simple and robust condensation polymerization. This double helix macromolecule represents one of the most rigid simple molecular structures, exhibiting an extremely high axial persistence length (~1 micrometer). This liquid crystalline polymer shows great potential as template material for next-generation electrolyte materials, which require tunable nano-scale molecular architecture.
Building on the newly developed ionic liquid-based polymer electrolyte obtained in step 1, we also successfully fabricated an organic-inorganic composite polymer electrolyte as shown in step 2 with a nanocrystalline and 3D network structure, which shows incredible high conductivity. The Li ion conductivity of this material can reach 1 mS/cm2 , which is 1000 times higher compared to a traditional solid state PEO based polymer electrolytes. By introducing a liquid crystal-oriented polymer structure, the material is nano-reinforced, which greatly improves the material's modulus (~ GPa), inhibits the growth of lithium dendrites at the electrode/electrolyte interface, thus guarantee the safety of the solid-state batteries. In addition, the material greatly reduces the interface resistance of the material to 32 Ω·cm2 , which is ~100 times lower than that of the traditional solid polymer electrolytes, indicating the importance of polymer in reducing the interfacial resistance for solid state batteries. Through multi-array characterizations, we investigated the ion conduction, activation energy, phase transition, nano-scale structure and related electrochemical properties in materials.
In material design, the design of nano-scale ordered structures, the study of interface chemistry in polymer composites, the conduction mechanism of metal ions in solid electrolyte materials, the phase transition in ion composite materials, and the nano-constraint theory are very important for material design in field of nanotechnology. Theoretical research is especially important for material design. Through the nano-confinement theory, electrolyte materials with ultra-high conductivity can be prepared by architecting the atomic structure of molecules and composites. If the conduction mechanism of solid electrolytes is introduced into polymer electrolyte materials, it will be an important breakthrough for the development of solid-state electrolytes. At current stage, the understanding of the conduction mechanism of lithium ions in solid electrolytes still needs further investigation. Experimental evidence associated with theoretical calculations and computer simulation will reveal the ion conduction mechanism underneath the solid-state electrolytes. It is also worth noting that the phase transition in solid electrolyte materials is also a critical point to break through the bottleneck in finding the suitable phase with high ionic conductivity.
In recent decades, theoretical computation and AI have received great attention in material science and chemical engineering. It is important for us to narrow down the discovery scope. AI is also widely used to predict the corresponding electrochemical property and life cycle of battery materials. Thus, a complete database of the system is expected to be constructed through the experimental parameters of the whole battery high-throughput experimental design. The unsupervised learning of AI, for example k-means classification, PCA, can help us classify and label materials, while the supervised learning of AI and various theories including random forest, support vector machine, gradient boosting and neural network can help predict the electrochemical properties of the material, such as conductivity, interface resistance, electrochemical window, capacitance, etc. These high-throughput parameters can help narrow the searching scope thus improve the efficiency to discover new applicable materials. Through reasonable and ingenious experimental design and intelligent analysis, AI can be truly applied to scientific research base on a high-accuracy data sample, combined with the supervised learning and unsupervised learning. After reasonable comparison among various models, we can selectively find the best model to predict the properties of materials. Looking back to the development of materials science, we start from the traditional trial and error phase to computer simulation and now we are approaching to the domain of AI. Of course, there are still many challenges, such as data incompleteness and inaccuracy, which requires enough high-throughput experimental results and systematic database design and development. In the future, we will be actively participating in the construction of a material database, so that the material genome engineering project can achieve a long-term development and enrich the collaborations with computational science projects.
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