Correction methods for first-principles calculations of the solution enthalpy of gases and compounds in liquid metals

AFLOW-CCE for the thermodynamics of ionic materials

Accurate thermodynamic stability predictions enable data-driven computational materials design. Standard density functional theory (DFT) approximations have limited accuracy with average errors of a few hundred meV/atom for ionic materials, such as oxides and nitrides. Thus, insightful correction schemes as given by the coordination corrected enthalpies (CCE) method, based on an intuitive parametrization of DFT errors with respect to coordination numbers and cation oxidation states, present a simple, yet accurate solution to enable materials stability assessments. Here, we illustrate the computational capabilities of our AFLOW-CCE software by utilizing our previous results for oxides and introducing new results for nitrides. The implementation reduces the deviations between theory and experiment to the order of the room temperature thermal energy scale, i.e., ∼25 meV/atom. The automated corrections for both materials classes are freely available within the AFLOW ecosystem via the AFLOW-CCE module, requiring only structural inputs.

Temperature dependence of O solubility in liquid Na by atomistic simulation of Na(l)-Na2O(s) interfaces using corrected machine learning potential: a step towards simulating Na combustion

Liquid Na combustion is a significant safety concern in sodium-cooled fast reactors. Atomistic simulations are an alternative to experiments for studying detailed mechanisms of complex combustion processes. However, accurate simulations of the interfaces involved in combustion are challenging even for density functional theory (DFT), because the systematic error between different chemical systems cannot be fully cancelled. Herein, we report the achievement of a key milestone in atomistic simulation of liquid Na combustion, which involves the development of a machine learning (ML) moment tensor potential that allows accurate simulation of interface systems between liquid Na and solid Na2O. The ML potential is trained by using supervised and active learning to ensure DFT-level accuracy. An empirical correction is then applied to achieve experimental accuracy by reducing systematic error. Consequently, the basic properties of liquid Na and solid Na2O are accurately simulated. In addition, with empirical correction, experimental O solubility data for liquid Na at 350-900 K are reproduced by using interface molecular dynamics simulations and a thermodynamic model. The temperature dependence of the enthalpy and entropy of the Na2O solution and their effect on O solubility are evaluated. The results show that, despite the increase in solution enthalpy with temperature, O solubility increases more rapidly than the linear Arrhenius plot due to the effect of solution entropy. The results of this study indicate that, with appropriate correction, ML potentials can achieve near-experimental accuracy, beyond the accuracy of DFT, in interface simulations and material properties calculations, paving the way for sodium combustion simulations in the future.

Accurate and Efficient Calculation of the Solution Enthalpy and Diffusivity of Solutes in Liquid Metals Using Machine Learning Potential

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.