A 3rd generation CALPHAD description for pure Mo is presented, with several approaches explored and the final optimized model parameters provided. The lattice stabilities of Mo are critically reviewed, and the inflectiondetection method is recommended for their estimation. Ab initio data are employed to train a machine learning potential, which is then used to support the determination of the instability temperature and the modelling of the liquid phase. The thermodynamic properties of the various phases are successfully described, demonstrating an overall good agreement with the experimental data, even at low temperatures. The unique characteristics of Mo, including significant electronic and anharmonic contributions, are addressed during the modelling. The Equal Entropy Criterion (EEC) is adopted to avoid solid phase stabilization above the melting point. Each modelling choice is thoughtfully discussed and analysed, providing a comprehensive overview of the current best practice for 3rd generation CALPHAD modelling of pure high-melting elements like Mo.

We present an extension of the SLUSCHI package (Solid and Liquid in Ultra Small Coexistence with Hovering Interfaces) to enable automated diffusion calculations from first-principles molecular dynamics. While the original SLUSCHI workflow was designed for melting temperature estimation via solid–liquid coexistence, we adapt its input and output handling to isolate the volume search stage and generate one production trajectory suitable for diffusion analysis. Post-processing tools parse VASP outputs, compute mean-square displacements (MSD), and extract tracer diffusivities using the Einstein relation with robust error estimates through block averaging. Diagnostic plots, including MSD curves, running slopes, and velocity autocorrelations, are produced automatically to help identify diffusive regimes. The method has been validated through representative case studies: self-diffusion in Al–Cu liquid alloys, sublattice melting in Li7La3Zr2O12 and Er2O3, interstitial oxygen transport in bcc and fcc Fe, and oxygen diffusivity in Fe–O liquids with variable Si and Al contents. Viscosity and diffusivity are linked through the Stokes–Einstein relation, with composition dependence assessed via simple linear mixing. This capability broadens SLUSCHI from melting-point predictions to transport property evaluation, enabling high-throughput, fully first-principles datasets of diffusion coefficients and viscosities across metals and oxides

Molten salts play a crucial role in numerous industrial applications, including nuclear reactors, thermal energy storage, and high-temperature electrochemical processes. Their thermophysical properties, such as viscosity, surface tension, and molar volume, are essential for optimizing performance and ensuring operational safety, as they govern heat transfer, fluid flow, and interfacial behavior in high-temperature environments. The TCSALT Molten Salts Database (Version 2.0) provides critically assessed thermodynamic and thermophysical data for fluoride- and chloride-based salts with oxide additions: AlCl3–AlF3–Al2O3–CaCl2–CaF2–CaO–KCl–KF–K2O–LiCl–LiF–Li2O–MgCl2–MgF2–MgO–NaCl–NaF–Na2O–SiCl4–SiF4–SiO2–SrCl2–SrF2–SrO–ZnCl2–ZnF2–ZnO. The database employs the Ionic Two-Sublattice Liquid Model to describe the molten salt solutions, enabling accurate predictions of multicomponent phase diagrams together with both thermodynamic and thermophysical properties. Using this database, viscosity and surface tension can be directly predicted from the underlying ionic structure description of the melt, offering quantitative insights into species distribution, connectivity, and structural evolution across a wide range of temperatures and compositions. The database also includes molar volume descriptions for both liquid and solid phases, further enhancing its applicability in high-temperature material processing and engineering applications. The integration of these thermophysical properties within a unified computational thermodynamic framework provides a powerful tool for material design and process optimization.

The development of advanced thermodynamic descriptions for pure elements is essential for accurate modelling of multicomponent systems. The third-generation Calphad descriptions incorporate physical effects such as electronic, vibrational and anharmonic contributions. In this study, we have developed a third-generation Calphad description for pure niobium (Nb). Thermodynamic properties of key phases — bcc, fcc, hcp and liquid — are presented for pure Nb. The vibrational contribution to the heat capacity of the solid phases has been modelled with the Einstein model, and the liquid phase has been modelled with the two-state model. In addition, the modelling of unstable phases has been extensively analysed. The traditional Calphad approach is evaluated and compared with the ab initio approach, which has a stronger theoretical basis. The 0 K energies of the unstable phases, fcc and hcp, have been selected from ab initio calculations using the inflection–detection method. Good agreement has been achieved with the selected experimental and ab initio data.

Thermodynamic calculations were employed to estimate activities and phase composition of the oxide layer for ternary dilute aluminum magnesium alloys; Al-Mg-M (M = Si, Zn, Zr, Ti, Be, or Ca). Previously, it has been reported that adding a ternary element modifies the vapor phase of Mg above the melt surface. When Be or Ca was added, a strong reduction of Mg partial pressure was recorded for small concentrations of the ternary element, in contrast with Si or Zn, where the effect was orders of magnitude smaller. This study aims to use Thermo-Calc simulations to reveal the distribution of alloying elements between the liquid metal, oxide layer, and vapor phase and predict the phase composition of oxide layers. The variation of the Mg vapor phase can be linked, at least in part, to the formation of oxide layers on the melt surface that may inhibit the diffusion of Mg.

Melting properties are critical for designing novel materials, especially for discovering high-performance, high-melting refractory materials. Experimental measurements of these properties are extremely challenging due to their high melting temperatures. Complementary theoretical predictions are, therefore, indispensable. One of the most accurate approaches for this purpose is the ab initio free-energy approach based on density functional theory (DFT). However, it generally involves expensive thermodynamic integration using ab initio molecular dynamic simulations. The high computational cost makes high-throughput calculations infeasible. Here, we propose a highly efficient DFT-based method aided by a specially designed machine learning potential. As the machine learning potential can closely reproduce the ab initio phase-space distribution, even for multi-component alloys, the costly thermodynamic integration can be fully substituted with more efficient free energy perturbation calculations. The method achieves overall savings of computational resources by 80% compared to current alternatives. We apply the method to the high-entropy alloy TaVCrW and calculate its melting properties, including the melting temperature, entropy and enthalpy of fusion, and volume change at the melting point. Additionally, the heat capacities of solid and liquid TaVCrW are calculated. The results agree reasonably with the CALPHAD extrapolated values.

A new generation of high-performance, ultra-high strength (UHS) steelsSteel are required for lightweight applications at low prices. Traditionally, the outstanding properties of maraging (martensite-aging) steelsSteel emerge from semi-coherent nanoprecipitation in a martensitic matrix. However, coherency strains originating from the semi-coherent precipitates promote crackCrack initiation under load and thus inevitably reduce ductility. Multiple authors have suggested as a solution a highly-dense nanoprecipitation of Ni(AlAl,Fe) coherent precipitates with minimal latticeLattices misfit and validated the idea experimentally. In this work, we computationally designed, from processing to performance, a UHS steelSteel composed of Fe-Ni-Al-Mo with a higher specific yield strength and much lower price. Several foundational computational tools within the ICMEICME framework were adopted to design the steelSteel, including Thermo-Calc, TC-PRISMA, DICTRA, and TC-Python.

Metal additive manufacturing (MAM) technology has experienced rapid development in recent years. As both equipment and materials progress towards increased maturity and commercialization, material metallurgy technology based on high energy sources has become a key factor influencing the future development of MAM. The calculation of phase diagrams (CALPHAD) is an essential method and tool for constructing multi-component phase diagrams by employing experimental phase diagrams and Gibbs free energy models of simple systems. By combining with the element mobility data and non-equilibrium phase transition model, it has been widely used in the analysis of traditional metal materials. The development of CALPHAD application technology for MAM is focused on the compositional design of printable materials, the reduction of metallurgical imperfections, and the control of microstructural attributes. This endeavor carries considerable theoretical and practical significance. This paper summarizes the important achievements of CALPHAD in additive manufacturing (AM) technology in recent years, including material design, process parameter optimization, microstructure evolution simulation, and properties prediction. Finally, the limitations of applying CALPHAD technology to MAM technology are discussed, along with prospective research directions.

In-situ laser-induced breakdown spectroscopy (LIBS) was used for measurements on molten aluminum alloys containing 0.6 wt pct magnesium in the melt temperature range 685 °C to 790 °C. With increasing melt temperature, an exponential growth of magnesium LIBS emission signals was observed, a phenomenon that has previously been attributed to the presence of Mg vapor above the melt surface. Here we show how this temperature dependence of the magnesium signal is affected by the presence of a second alloying element in the melt. For dilute ternary aluminum alloys Al–Mg–M, with M = Si, Zn, or Sn, the change in vapor-phase contribution to the Mg signal was found to be linearly correlated with the concentration of the additional alloying element but differing in sign and magnitude. Ternary alloys containing group-II alloying elements (M = Be, Ca, or Sr), known to inhibit oxidation of the melt, were also studied. The presence of these elements had a strongly reducing effect on the vapor-phase component of the Mg LIBS signal. We attribute this decrease to the formation of Be, Ca, or Sr-containing oxides that effectively inhibit the transport of Mg from the melt to the surface and into the vapor phase.