General-Purpose Machine-Learned Potential for CrCoNi Alloys Enabling Large-Scale Atomistic Simulations with First-Principles Accuracy

Abstract

CrCoNi medium-entropy alloys exhibit exceptional mechanical properties arising from pronounced chemical complexity, including short-range order (SRO), and low stacking fault energy, posing challenges for large-scale atomistic simulations. While most models focus on equimolar compositions, deviations from equimolarity provide an effective route to tuning properties, requiring transferable interatomic potentials that capture composition-dependent behavior. Here we develop a general-purpose machine-learned interatomic potential for the CrCoNi system within the neuroevolution potential (NEP) framework, achieving near first-principles accuracy with high computational efficiency. Trained on a comprehensive dataset spanning pure elements, binary and ternary alloys across a wide compositional range, diverse crystal structures and thermodynamic conditions, and based on spin-polarized \textit{ab initio} data, the model accurately reproduces equations of state, phonons, elastic constants, dislocation dissociation, surface and defect energies, melting temperatures and strain-induced phase transformations. It further captures SRO and its effect on stacking fault energies across both equimolar and non-equimolar compositions, in agreement with first-principles and experiments. In contrast to existing potentials, typically limited to equimolar alloys and less accurate for pure elements, the present model delivers consistent accuracy across the full compositional space while retaining superior efficiency. These results enable reliable atomistic simulations of composition-dependent behaviour and provide a framework for the design of non-equimolar CrCoNi alloys.