Abstract
To clarify atomic diffusion in amorphous materials, which is important in novel information and energy devices, theoretical methods having both reliability and computational speed are eagerly anticipated. In the present study, we applied neural network (NN) potentials, a recently developed machine learning technique, to the study of atom diffusion in amorphous materials, using Li3PO4 as a benchmark material. The NN potential was used together with the nudged elastic band, kinetic Monte Carlo, and molecular dynamics methods to characterize Li vacancy diffusion behavior in the amorphous Li3PO4 model. By comparing these results with corresponding DFT calculations, we found that the average error of the NN potential is 0.048 eV in calculating energy barriers of diffusion paths, and 0.041 eV in diffusion activation energy. Moreover, the diffusion coefficients obtained from molecular dynamics are always consistent with those from ab initio molecular dynamics simulation, while the computation speed of the NN potential is 3-4 orders of magnitude faster than DFT. Lastly, the structure of amorphous Li3PO4 and the ion transport properties in it were studied with the NN potential using a large supercell model containing more than 1000 atoms. The formation of P2O7 units was observed, which is consistent with the experimental characterization. The Li diffusion activation energy was estimated to be 0.55 eV, which agrees well with the experimental measurements.
Highlights
Atom/ion diffusion in amorphous materials is an important elementary process that has a great influence on the performance of various information and energy devices
We examined diffusion behaviors by combining the neural network (NN) potential with various computational approaches, i.e., NEB, kinetic Monte Carlo (KMC), and molecular dynamics (MD), and compared the results with those obtained by the combination of these approaches with DFT
We investigated the application of the high-dimensional neural network (NN) potential in the study of atomic diffusion using Li3PO4 as the model system
Summary
Atom/ion diffusion in amorphous materials is an important elementary process that has a great influence on the performance of various information and energy devices. A new method, which involves the construction of interatomic potentials via machine learning techniques to determine the relationship between atomic structures and corresponding energies, has attracted much attention as a promising way to achieve high computational accuracy and speed. In this method, interatomic potentials are a)Present address: Research Center for Computational Design of Advanced Functional Materials, National Institute of Advanced Industrial Science and Technology, Tsukuba, Ibaraki 305-8568, Japan. B)Author to whom correspondence should be addressed: watanabe@ cello.t.u-tokyo.ac.jp “trained” using the results of first-principles calculations as the reference (or “training”) data for the learning process It becomes capable of predicting structural energies that are not included in the training dataset.
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