Active learning of interatomic potentials to investigate thermodynamic and elastic properties of Ti0.5Al0.5N at elevated temperature

Abstract: With the immense increase in the computational power available for the material science community in recent years, a range of new discoveries were made possible. Accurate investigations of large scale atomic systems, however, still come with an extremely high computational demand. While the recent development of Graphics Processing Unit (GPU) accelerated supercomputing might offer a solution to some extent, most well known electronic structure codes have yet to be fully ported to utilize this new power. With a soaring demand for new and better materials from both science and industry, a more efficient approach for the investigation of material properties needs to be implemented. The use of Machine Learning (ML) to obtain Interatomic Potentials (IP) which far outperform the classical potentials has increased greatly in recent years. With successful implementation of ML methods utilizing neural networks or Gaussian basis functions, the accuracy of ab-initio methods can be achieved at the demand of simulations with empirical potentials. Most ML approaches, however, require high accuracy data sets to be trained sufficiently. If no such data is available for the system of interest, the immense cost of creating a viable data set from scratch can quickly negate the benefit of using ML. In this diploma project, the elastic and thermodynamic properties of the Ti0.5Al0.5N random alloy at elevated temperature are therefore investigated using an Active Learning (AL) approach with the Machine Learning Interatomic Potentials (MLIP) package. The obtained material properties are found to be in good agreement with results from computationally demanding ab-initio studies of Ti0.5Al0.5N, at a mere fraction of the demand. The AL approach requires no high accuracy data sets or previous knowledge about the system, as the model is initially trained on low accuracy data which is removed from the training set (TS) at a later stage. This allows for an iterative process of improving and expanding the data set used to train the IP, without the need for large amounts of data.