A deep learning approach to predict thermophysical properties of metastable liquid Ti-Ni-Cr-Al alloy

Abstract

The physical properties of liquid alloy are crucial for many science fields. However, acquiring these properties remains challenging. By means of the deep neural network (DNN), here we presented a deep learning interatomic potential for the Ti–Ni–Cr–Al liquid system. Meanwhile, the thermophysical properties of the Ti–Ni–Cr–Al liquid alloy were experimentally measured by electrostatic levitation and electromagnetic levitation technologies. The DNN potential predicted this liquid system accurately in terms of both atomic structures and thermophysical properties, and the results were in agreement with the ab initio molecular dynamics calculation and the experimental values. A further study on local structure carried out by Voronoi polyhedron analysis showed that the cluster exhibited a tendency to transform into high-coordinated cluster with a decrease in the temperature, indicating the enhancement of local structure stability. This eventually contributed to the linear increase in the density and surface tension, and the exponential variation in the viscosity and the diffusion coefficient with the rise of undercooling.