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Implanted neural network potentials: Application to Li-Si alloys
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DOI：10.1103/PhysRevB.97.094106.png)
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Abstract
En 中文
Modeling the behavior of materials composed of elements with different bonding and electronic structure character for large spatial and temporal scales and over a large compositional range is a challenging problem. Cases in point are amorphous alloys of Si, a prototypical covalent material, and Li, a prototypical metal, which are being considered as anodes for high-energy-density batteries. To address this challenge, we develop a methodology based on neural networks that extends the conventional training approach to incorporate pre-trained parts that capture the character of different components, into the overall network; we refer to this model as the implanted neural network method. We show that this approach works well for the Si-Li amorphous alloys for a wide range of compositions, giving good results for key quantities like the diffusion coefficients. The method is readily generalizable to more complicated situations that involve two or more different elements.
Keywords:
AMORPHOUS-SILICON
GLASSY LIQUIDS
1ST PRINCIPLES
LITHIATION
DIFFUSION
RECOGNITION
RELAXATION
BATTERIES
MECHANISM
CLUSTERS
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