LAUSR

Liquid metals (LMs) have various applications in energy systems, such as coolants in advanced nuclear reactors. In addition, room-temperature LMs are attracting attention as flexible components in robotics and electronics and as novel chemical reaction media to form low-dimensional materials. In many of these applications, the capabilities of LMs can be further enhanced if one can better understand and control the chemical reactivity of LMs, which is largely affected by the stability and mobility of solutes in LMs. Here, we propose an automated method using a machine learning moment tensor potential to efficiently calculate the solution enthalpy and diffusivity of solutes in LMs. From several test cases in liquid Na, we demonstrate that the method can achieve an accuracy comparable to that of a direct calculation using first-principles molecular dynamics, while significantly reducing the calculation cost to the order of 1/10 to 1/100. The method is expected to contribute to the advancement of LM chemistry and the development of new LMs.