Neural-network-based interatomic potential: A case study on lithium

Advancements in neural-network-based force fields have led to the predictions of materials for applications in widespread applications. In this work, we will show a general scheme that can be used to develop a neural-network-based interatomic potential using our in-house developed python atom-centered machine learning force field package (PyAMFF) with GPU capabilities. Using an example of lithium, we will show a force field can be developed using neural networks: (a) Data Collection step: atomic positions, energies, and forces from density functional theory (DFT) calculations for different lithium systems; (b) Fingerprint Selection step: an automated Behler-Parrinello representation selection for a dataset using radial and angular distribution function; (c) Training Dataset Generation step: selection criteria in fingerprint space to reduce the size of DFT dataset for neural network training; (d) Model Training step: analyzing the effect of neural network size and fingerprint selection on model accuracy; (e) Model Performance step: rigorous testing of neural-network-based force field on rare event searches using Adaptive Kinetic Monte Carlo and global optimization of lithium clusters using basin hopping. This force field will help in answering questions related to kinetics of lithium deposition on lithium metal surfaces at experimental timescales for battery applications.