Sensitivity of Short-Range Order Prediction to Machine Learning Potential Formalisms: A Case Study on NbMoTaW High-Entropy Alloy

Chemical short-range order (SRO), a phenomenon at the atomic scale resulting from inhomogeneities in the local chemical environment, is usually studied using machine learning force field-based molecular dynamics simulations due to the limitations of experimental methods. To promote the reliable application of machine potentials in high-entropy alloy simulations, First, this work uses NEP models trained on two different datasets to predict the Short-Range Order (SRO) coefficients of NbMoTaW. The results show that within the same machine learning framework, there are significant differences in the prediction of SRO coefficients for the Nb–Nb atomic pair. Subsequently, this work predicts the SRO coefficients of NbMoTaW using the NEP model and SNAP model, both of which are trained on the same dataset. The results revealed significant discrepancies in SRO predictions for like-element pairs (e.g. Nb–Nb and W–W) between the two potentials, despite the identical training data. The findings of this study indicate that discrepancies in the prediction results of SRO coefficients can arise from either the same machine learning framework trained on different datasets or different learning frameworks trained on the same dataset. This reflects possible incompleteness in the current training set’s coverage of local chemical environments at the atomic scale. Future research should establish unified evaluation standards to assess the capability of training sets to accurately describe complex atomic-scale behaviors such as SRO.