Silicon phase transitions in nanoindentation: Advanced molecular dynamics simulations with machine learning phase recognition

Abstract

Closing the gap between experiments and simulations in the investigation of high-pressure silicon phase transitions calls for advanced, new-generation modeling approaches. By exploiting massive parallelization, we here provide molecular dynamics (MD) simulations of Si nanoindentation based on the Gaussian Approximation Potential (GAP). Results are analyzed by exploiting a customized Neural Network Phase Recognition (NN-PR) approach, helping to shed light on the phase transitions occurring during the simulations. Our results show that GAP provides a realistic description of silicon phase transitions. With the support of NN-PR method, the formation mechanism and stability of high-pressure phases are comprehensively studied. Additionally, we also show how simulations based on the less demanding and widely-used Tersoff potential are still useful to investigate the role played by the indenter tip modeling. However, high-pressure phases obtained with GAP are more consistent with observations made in nanoindentation experiments, removing a spurious phase that is shown by Tersoff simulations. This behavior is explained on the base of relative phase stability with comparison with Density Functional Theory (DFT) calculations. This work provides insight into the application of state-of-the-art Machine Learning (ML) methods on nanoindentation simulations, enabling further understanding of the phase transition mechanisms in silicon.