Speaker
Description
Atomistic simulations provide a way to observe atomic behavior at the nanoscale level. There are two main approaches: the first is based on quantum mechanics (ab initio simulations), and the second relies on Newton's mechanics (molecular dynamics). However, despite advancements in computer science, including quantum computing, both approaches remain limited. Ab initio simulations are constrained by the size of the simulation cell and the necessity to perform quasi-static simulations at T = 0 K due to their complexity and high computational demands, while molecular dynamics primarily suffer from the accuracy of interatomic potentials. Many of these limitations can be mitigated through machine learning, which enables the construction of interatomic potentials from training sets obtained in ab initio simulations.
In this work, we present an interatomic potential for NiTi based on the Atomic Cluster Expansion, developed using the Pacemaker software package. We validate this potential by comparing it against the results of simulations using other interatomic potentials, quantum-mechanical calculations, as well as our own experimental data. Our quantum-mechanical calculations utilize density functional theory (DFT) within the generalized gradient approximation (GGA) to determine the ground-state structural, electronic, thermodynamic, and elastic properties of NiTi in low-temperature (martensitic) phase. The target properties include elastic constants, phonon spectra calculations, and vacancy formation energy. Specifically, the stress-strain method was employed to compute the full tensor of the second- order elastic constants and assess the mechanical stability of the studied phases, ensuring that the results are consistent with those obtained using other established potential.
