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Dynamic evolution of CaO solidification at 2720 K (a)-(c), solid–liquid coexistence at 2730 K (d)-(f), and liquidation at 2740 K (g)-(i) using DPMD simulations. The region represented by the green shadow indicates the presence of the molten phase. The initial configuration consists of a liquid phase at 3300 K and a solid phase at 3000 K. [Display omitted] Although the physical properties of CaO have been extensively discussed as a crucial component of the Earth's mantle, there remains a significant discrepancy between the theoretically predicted melting temperature (T m) and experimental measurements. Therefore, further investigation of the melting properties of CaO is required to clarify these discrepancies. In this study, a deep learning potential (DP) model is used to perform large-scale molecular dynamics simulations with greater accuracy. The DP model accurately reproduces the lattice constants, elastic parameters and equations of state of CaO, in contrast to empirical potentials. The atomic structures of the solid and liquid states are also discussed using radial distribution functions. Using the DP model, the T m of CaO were obtained to be 3140, 2915, and 2730 K by single-phase method, void method, and solid–liquid coexistence method. This study discusses possible reasons for the discrepancy in the T m of CaO. Despite agreement with first-principles calculations, inherent defects in the pseudopotential lead to discrepancies between the simulation results and the experimental measurements in the DP model. In addition, the relative strength of atomic bonding can be assessed by comparing the Vickers hardness and the cohesive energy of MgO and CaO. It is evident that MgO has the lowest energy and is the most stable, suggesting a higher theoretical T m than CaO. [ABSTRACT FROM AUTHOR]

The melting temperature, a crucial material property, is particularly challenging to measure accurately for inorganic crystals. The data-driven approach emerges as a potential solution, although its effectiveness is hindered by the limitation of available data. To counter this challenge, we implement transfer learning, leveraging a vast computational database of atomization energy. We first pre-train a geometric-information-enhanced crystal graph neural network (GeoCGNN) using atomization energies of approximately 36,000 materials that are computed by the density functional theory. Subsequently, the pre-trained model is fine-tuned using melting temperatures measured for 799 crystals, encompassing 83 elements, ranging from unary to quaternary systems. This transfer learning strategy decreases the root mean square error from 407 to 218 K, attesting to a marked improvement in prediction accuracy. Furthermore, transfer learning significantly mitigates error variability across unary, binary, and ternary (or higher-order) systems, thereby enhancing the reliability of predictions across a broader range of crystals. We also show that transfer learning allows effective task adaptation by leveraging representation learned from pre-training. Therefore, it can achieve better prediction performance even with a limited number of data for predicting melting points. [Display omitted] • Transfer learning is employed to predict melting points for inorganic crystals. • Crystal graph neural network is utilized to deal with a broad class of crystals. • By applying transfer learning, the accuracy of predictions is improved by 46%. • Improvements in each crystal class rely on physical relationship to pre-training set. • Effective task adaptation by transfer learning gives high accuracy for limited data. [ABSTRACT FROM AUTHOR]

Atomistic modeling of CoCrFeNiMn (0 < x Mn  < 25 at.%) high entropy alloys shows that the melting temperature as a function of Mn concentration does not follow the behavior consistent with a homogeneous solid solution, exhibiting a maximum value for 8.7 at.% Mn. Using the concepts of the BFS method for alloys, a description of the phenomenon is provided, showing that sluggish diffusion generates changes in the atomic distribution that lead to this anomalous behavior. This theoretical analysis is meant to provide a template for studying complex compositions and fine effects in high entropy alloys, which would be hard to detect experimentally but that could have an impact on potential applications of these complex materials. [ABSTRACT FROM AUTHOR]

Melting is a high temperature process that requires extensive sampling of configuration space, thus making melting temperature prediction computationally very expensive and challenging. Over the past few years, I have built two methods to address this challenge, one via direct density functional theory (DFT) molecular dynamics (MD) simulations and the other via deep learning graph neural networks. The DFT approach is based on statistical analysis of small-size solid–liquid coexistence MD simulations. It eliminates the risk of metastable superheated solid in the fast-heating method, while also significantly reducing the computer cost relative to the traditional large-scale coexistence method. Being both accurate and efficient (at the speed of several days per material), it is considered as one of the best methods for direct DFT melting temperature calculation. The deep learning method is based on graph neural networks that effectively handles permutation invariance in chemical formula, which drastically improves efficiency and reduces cost. At the speed of milliseconds per material, the model is extremely fast, while being moderately accurate, especially within the composition space expanded by the dataset. I have implemented both methods into automated computer code packages, making them publicly available and free to download. The DFT and deep learning methods are highly complementary to each other, and hence they can be potentially well integrated into a framework for melting temperature prediction. I demonstrated examples of applying the methods to materials design and discovery of high-melting-point materials. [Display omitted] [ABSTRACT FROM AUTHOR]

In this paper, molecular dynamics simulations are carried out to study the melting process of the β-crystobalite structure under the effect of various electric fields. The results reveal that the higher electric field contributes to the damage of the β-crystobalite. Meanwhile, the applied electric field can affect the damage process of β-crystobalite, especially the melting temperature. And the melting point reduces as the strength of the electric field is increased. It is also found that the time required for the collapse of the structure strongly depends on the electric field. In addition, the presence of external electric fields results in significant changes in the electronic structures. It can be seen that there are noticeable changes in the shape of the density of states (DOS) curves. [ABSTRACT FROM AUTHOR]

Highlights: [•] We obtained the equation of states of perfect metals with bcc and fcc structures. [•] We find the melting curve equation of perfect metals using SMM. [•] We have derived a equation for the melting curve of metals with defects. [•] The pressure dependence of the melting curve of Cu, Al, Fe and W metals with defects being estimated. [ABSTRACT FROM AUTHOR]

Abstract: A systematic study of the effect of surface anisotropy on the melting temperatures of free-standing gold nanofilms for various film thicknesses was carried out using molecular dynamics (MD) simulations. Caloric curves obtained from progressive heating of nanofilms showed sharp increase in energy at the transition temperatures due to the absorption of latent heat of melting. The importance of identifying the correct transition point (i.e., lower transition point) as the melting temperature was demonstrated. Melting temperatures of nanofilms were found to be lower than the bulk melting point. Significant decrease in the melting temperature with film thickness was observed for thicknesses below ∼6nm. Among the films with low index crystallographic surfaces, those with {110} surface (least close-packed and highest surface energy) showed lowest melting temperatures whilst those with {111} surface (most close-packed and lowest surface energy) showed highest melting temperatures. Prolonged isothermal heat treatment demonstrated that there is no coexistence of equilibrium thickness of liquid layer with solid, in the case of free-standing nanofilm, below its melting temperature. [Copyright &y& Elsevier]

• Melting of tungsten under orientation-dependent complex stresses was studied by MD. • Melting point increases (decreases) under applied compressive (tensional) stresses. • Melting point reaches minimum under applied pure shear stress. • Stress-dependent melting is supported by RDF, Lindeman and Born criteria. Atomic-level insight into pre- and post- melting phenomena under complex stresses spanning compression, tension, and shearing in refractory metals is critical. The exploration of high temperature and melting behavior in tungsten under magnitude- and orientation-dependent uniaxial and biaxial compression and tension, approaching pure shear stresses was thoroughly investigated by molecular dynamics using extended Finnis-Sinclair potential. Using equilibrium solid-liquid coexistence simulations, we demonstrated the enhancement (reduction) of the melting point temperature T m taking place with the increase of applied compression (tension) stress magnitudes, reaching a minimum under pure shear stress. Explored heating and melting behaviors under various stress types, magnitudes, and orientations were well supported by the corresponding trends in radial distribution function g (r) and Lindemann index δ. The highest resistance to melting was found under uniaxial compression likely due to bonds stiffening. On the other hand, the highest compliance to melting was revealed under pure shear stress, triggered by the accelerated vibrational instability and destruction of the bcc crystalline order driven by simultaneous shear deformation and formation of fcc and hcp phases. [ABSTRACT FROM AUTHOR]

[Display omitted] Shape effect on Metallic Nano particles has many applications in technology, engineering and industry. Molecular dynamic simulations have been performed to investigate size and shape effects of Iridium nanocluster on its surface energy (E sur) and self-diffusion coefficient (D) as a function of temperature (T). Truncated octahedron (TO), octahedron (Oh), cubic (C), and face center cubic (FCC) of Iridium nanocluster were studied in this survey. At temperatures lower than 1000 K, E sur order is as follows: TO > C > Oh > FCC while at temperatures higher than 1000 K it has a different order as TO > Oh > C > FCC. Self-diffusion coefficient increases versus temperatures as expected, but results show that it is roughly the same for all systems at T < 1800 K and T greater than 2500. At temperature range T (1800, 2500), self-diffusion coefficient order is as follows: TO ≈ Oh ≈ C < FCC. T O ≅ O h ≅ C < F C C Interestingly, D FCC is smaller than D for other shapes of Iridium nanocluster. Self-diffusion coefficient as a function of nanocluster size was calculated with molecular dynamics simulation. It is shown there is a peak for D at N = number of particles = 700 which corresponds to surface effect. MD results show that FCC has more solid like long-range interaction, but others have more liquid like order. [ABSTRACT FROM AUTHOR]

[Display omitted] • Melting temperature of metal clusters is estimated from molecular dynamics simulations. • Structural changes are monitored as a function of temperature. • Trimetallic clusters exhibit the highest melting temperature. • DFT calculations predict metallic behavior of Cu 36 Au 1 Ag 1 cluster isomers. The thermal behavior of 38-atom mono-, bi-, and trimetallic clusters consisting of Cu, Ag, and Au atoms, is analyzed employing molecular dynamics simulations and DFT calculations for selected cluster compositions. Low-energy structures were singled out to perform NVT molecular dynamics simulations at several temperatures, using the Andersen thermostat for temperature control. The caloric curve is used to estimate the melting temperature and the specific heat. The pair distribution function g(r) of the solid and liquid-phase clusters is examined at different temperatures. When comparing the estimated melting points (T m) among the monatomic clusters, the order becomes T m Cu 38 > T m Ag 38 > T m Au 38 . For bimetallic clusters, an increase of T m is observed for Cu-Au compared to their monatomic counterparts, while the opposite occurs for Cu-Ag clusters. For trimetallic clusters, two low-energy isomers of the Cu 36 Ag 1 Au 1 cluster are investigated. In this case, T m is estimated to be 475 K, for the two isomers with the lowest-energy and second-to-lowest energy, respectively. For all the clusters studied, the pair distribution function g(r) shows that the first peak position is not shifted as an effect of temperature and its maximum value varies with composition, while the second peak essentially vanishes upon melting. The common-neighbor analysis (CNA) technique is used to analyze the local structural changes for the trimetallic clusters, again demonstrating a clear structural change upon melting. The HOMO-LUMO energy gap indicates that the trimetallic isomers' behavior is metallic, while the average binding energy show these clusters' energetic stability to be similar. [ABSTRACT FROM AUTHOR]

• The convention void method has been improved. • Nonequilibrium thermodynamic integration method displays the highest accuracy for melting temperature. • The melting temperature of Ni-Zr compound system were investigated with five different methods. Finding the efficient and accurate schemes to calculate the melting temperature is important for molecular dynamics simulation. We propose a modified void method based on heterogeneous nucleation to validly calculate the melting temperature from solid-liquid coexistence state. It decreases the degree of superheating in the conventional voids method. The efficiency and accuracy of five methods (i.e., hysteresis method, two phase coexistence method, the interface pinning method, the Frenkel-Ladd path method and the modified void method) have been discussed in detail. The calculated results of hysteresis method strongly depend on heating/cooling rate. For the nonequilibrium thermodynamic integration method, the calculated accuracy is consistent with the interface pinning method for monoatomic Cu. For the modified void method the calculated results agree with the nonequilibrium thermodynamic integration method. [ABSTRACT FROM AUTHOR]