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Jiewei Cheng

Publications and source records attributed to Jiewei Cheng.

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Coupling Lattice Distortion and Cation Disorder to Control Li-ion Transport in Cation-Disordered Rocksalt Oxides

Cation-disordered solids offer a rich chemical landscape where local coordination, lattice responses, and configurational disorder collectively, yet often implicitly, govern ion transport. In cation-disordered rocksalt oxides, Li+ diffusion has conventionally been rationalized by the static 0-transition-metal (0-TM) percolation rule, which assumes an ideal, passive lattice and thus fails to capture experimentally accessible capacities. Here, we show that lattice distortion is an essential, previously overlooked degree of freedom that actively reshapes Li+ percolation networks. By developing a lattice-responsive framework combining Monte Carlo sampling of cation configurations with machine-learning-accelerated molecular dynamics, we quantitatively predict Li+ percolation and electrochemical capacities within 5% of experiment. Our results reveal a causal coupling between lattice distortion and cation short-range order: enhanced local distortions precede and suppress short-range ordering, activating Li+ migration through nominally inaccessible 1-TM channels, fundamentally extending percolation beyond the 0-TM paradigm. Guided by this, we design and synthesize a high-entropy oxide, Li1.2Mn0.2Ti0.2V0.2Mo0.2O2, which exhibits enhanced distortion and achieves a 71.9% Li+ percolation network, surpassing 65.8% in Li1.2Mn0.4Ti0.4O2, delivering 256.3 mAh/g capacity, closely matching our prediction of 255.1 mAh/g. These findings establish lattice distortion as an active control parameter for ion transport, revising percolation concepts and offering a general design principle beyond metal-ion cathodes.

cond-mat.mtrl-sci

The relationship between activated H2 bond length and adsorption distance on MXenes identified with graph neural network and resonating valence bond theory

Motivated by the recent experimental study on hydrogen storage in MXene multilayers [Nature Nanotechnol. 2021, 16, 331], for the first time we propose a workflow to computationally screen 23,857 compounds of MXene to explore the general relation between the activated H2 bond length and adsorption distance. By using density functional theory (DFT), we generate a dataset to investigate the adsorption geometries of hydrogen on MXenes, based on which we train physics-informed atomistic line graph neural networks (ALIGNNs) to predict adsorption parameters. To fit the results, we further derived a formula that quantitatively reproduces the dependence of H2 bond length on the adsorption distance from MXenes within the framework of Pauling's resonating valence bond (RVB) theory, revealing the impact of transition metal's ligancy and valence on activating dihydrogen in H2 storage.

cond-mat.mtrl-sci