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Jitai Yang

Publications and source records attributed to Jitai Yang.

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Disentangling Cation-Polyanion Coupling in Solid Electrolytes: Which Anion Motion Dominates Cation Transport?

Lithium and sodium solid electrolytes feature polyanion frameworks and highly mobile cations. Understanding and quantifying the impact of polyanion dynamics on cations will help us to unravel the complex role that anion play in superionic conductors. However, no experimental or computational method can directly extract this information, as polyanion dynamics are always coupled with other factors that affect ion mobility. Here, we present the pioneering study that combines constraint algorithm and machine-learning molecular dynamics to quantitatively reveal the effects of polyanion translation, rotation, and vibration on cation mobility across a diverse material class. Ultralong-time, large-scale machine-learning molecular dynamics simulations with selective constraints on each anion motion mode unequivocally yield results at near room and elevated temperatures. In sharp contrast to the previous understanding that facile anion rotation primarily facilitates cation transport, the strong coupling between anion translation and vibration with cation diffusion has been unraveled for the first time; we find that translation, rotation, and vibration can each directly drive superionicity, with one typically dominant in each class of materials. Anion rotation dominates cation transport when the rotation frequency matches the cation hopping frequency, whereas anion translation prevails at higher and vibration at lower rotation frequencies. The impact of anion dynamics on cation diffusion becomes more prominent at lower temperatures.

physics.chem-ph

A Rotational/Roto-translational Constraint Method for Condensed Matter

In condensed matter physics, particularly in perovskite materials, the rotational motion of molecules and ions is associated with important issues such as ion conduction mechanism. Constrained Molecular Dynamics (MD) simulations offer a means to separate translational, vibrational, and rotational motions, enabling the independent study of their effects. In this study, we introduce a rotational and roto-translational constraint algorithm based on the Velocity Verlet integrator, which has been implemented into a homebrew version of the CP2K package. The MD results show that our program can selectively constrain the molecules and ions in the system and support long-time MD runs. The algorithm can help the future study of important rotation related dynamic problems in condensed matter systems.

physics.chem-ph

A Machine Learning Approach Based on Range Corrected Deep Potential Model for Efficient Vibrational Frequency Computation

As an ensemble average result, vibrational spectrum simulation can be time-consuming with high accuracy methods. We present a machine learning approach based on the range-corrected deep potential (DPRc) model to improve computing efficiency. DPRc method divides the system into ``probe region'' and ``solvent region''; ``solvent-solvent'' interactions are not counted in the neural network. We applied the approach to two systems: formic acid \ch{C=O} stretching and MeCN \ch{C+N} stretching vibrational frequency shifts in water. All data sets were prepared using Quantum Vibration Perturbation (QVP) approach. Effects of different region divisions, one-body correction, cut-range, and training data size were tested. The model with a single molecule ``probe region'' showed stable accuracy; it ran roughly ten times faster than regular DP and reduced the training time by about four. The approach is efficient, easy to apply, and extendable to calculating various spectra.

physics.chem-ph