Browsing by Author "Yang, Kaiqi"

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    A mechanism of defect-enhanced phase transformation kinetics in lithium iron phosphate olivine
    (Springer Nature, 2019) Hong, Liang; Yang, Kaiqi; Tang, Ming
    Antisite defects are a type of point defect ubiquitously present in intercalation compounds for energy storage applications. While they are often considered a deleterious feature, here we elucidate a mechanism of antisite defects enhancing lithium intercalation kinetics in LiFePO4 by accelerating the FePO4 → LiFePO4 phase transformation. Although FeLi antisites block Li movement along the [010] migration channels in LiFePO4, phase-field modeling reveals that their ability to enhance Li diffusion in other directions significantly increases the active surface area for Li intercalation in the surface-reaction-limited kinetic regime, which results in order-of-magnitude improvement in the phase transformation rate compared to defect-free particles. Antisite defects also promote a more uniform reaction flux on (010) surface and prevent the formation of current hotspots under galvanostatic (dis)charging conditions. We analyze the scaling relation between the phase boundary speed, Li diffusivity and particle dimensions and derive the criteria for the co-optimization of defect content and particle geometry. A surprising prediction is that (100)-oriented LiFePO4 plates could potentially deliver better performance than (010)-oriented plates when the Li intercalation process is surface-reaction-limited. Our work suggests tailoring antisite defects as a general strategy to improve the rate performance of phase-changing battery compounds with strong diffusion anisotropy.
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    Mesoscale Modeling of Microstructure Evolution in Lithium Battery Electrode Material
    (2021-04-28) Yang, Kaiqi; Tang, Ming
    Thirty years after its commercialization, lithium-ion rechargeable battery (LIB) has become a key technology in reducing the society’s dependence on fossil fuels and shifting towards clean, renewable energy sources. Despite tremendous progress, Li- ion batteries still face significant barriers to their wide adoption in electrical vehicles and electric grid storage. Fundamental understanding of the physical phenomena in battery systems at all length scales, including the mesoscopic level, is essential to enable further technological advancement in cost reduction, energy density improvement and cycle life extension. Many battery electrode materials undergo first-order phase transformations upon battery cycling, which frequently control the lithium intercalation kinetics and significantly impact on the degradation process. Extensive research reveals that phase transformations in intercalation compounds exhibit distinct characteristics from other types of material systems, many of which are still under debate and remain to be fully understood. A clear elucidation of these unique features has important implications for discovering and designing better battery materials. In this thesis, computational modeling is applied to investigate the mesoscale phase transformation kinetics in lithium battery electrodes, using lithium iron phosphate olivine (LiFePO4) cathode as the model system. Phase-field models are developed and implemented to simulate the concurrent lithium transport, phase transition and stress evolution in LiFePO4 during (de)lithiation, which generates several significant findings including: 1) The coherency stress between the LiFePO4 and FePO4 phases induces the morphological instability in the phase growth front and results in non-uniform intercalation in single crystalline particles. 2) Phase transition in LiFePO4 secondary particles exhibits one-dimensional growth behavior, which can be attributed to the anisotropic stress generated by the LiFePO4 /FePO4 lattice misfit and the strong elastic interaction between primary particles, 3) Antisite defects have the surprising effect of accelerating the phase growth kinetics by increasing the surface area of active Li intercalation during battery charge/discharge. Due to high computational cost, simulating microstructure evolution in battery systems is still limited in spatiotemporal scales. To address this challenge, we develop a data-driven modeling approach by replacing PDE-based simulations with machine learning algorithms based on convolutional recurrent neural networks (ConvRNN). The ConvRNN model is demonstrated to accelerate predictions up to 1000 folds for several classical microstructure evolution examples. It offers a promising alternative for efficient battery simulation at the mesoscale level in the future.
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    Self-supervised learning and prediction of microstructure evolution with convolutional recurrent neural networks
    (Elsevier, 2021) Yang, Kaiqi; Cao, Yifan; Zhang, Youtian; Fan, Shaoxun; Tang, Ming; Aberg, Daniel; Sadigh, Babak; Zhou, Fei
    Microstructural evolution is a key aspect of understanding and exploiting the processing-structure-property relationship of materials. Modeling microstructure evolution usually relies on coarse-grained simulations with evolution principles described by partial differential equations (PDEs). Here we demonstrate that convolutional recurrent neural networks can learn the underlying physical rules and replace PDE-based simulations in the prediction of microstructure phenomena. Neural nets are trained by self-supervised learning with image sequences from simulations of several common processes, including plane-wave propagation, grain growth, spinodal decomposition, and dendritic crystal growth. The trained networks can accurately predict both short-term local dynamics and long-term statistical properties of microstructures assessed herein and are capable of extrapolating beyond the training datasets in spatiotemporal domains and configurational and parametric spaces. Such a data-driven approach offers significant advantages over PDE-based simulations in time-stepping efficiency and offers a useful alternative, especially when the material parameters or governing PDEs are not well determined.