相关论文: Capturing short-range order in high-entropy alloys…
We present a thorough theoretical study of ordering phenomena in nitride ternary alloys GaInN, AlInN, and AlGaN. Using the Monte Carlo approach and energetics based on the Keating model we analyze the influence of various factors on…
In high-entropy alloys (HEAs), the local chemical fluctuations from disordered solute solution state into segregation, precipitation and ordering configurations are complex due to the large number of elements. In this work, the cluster…
Amorphous grain-boundary (GB) complexions in thermally stable nanocrystalline alloys are commonly assumed to be structurally homogeneous, yet their disordered nature makes them susceptible to local short-range ordering (SRO). The influence…
Phase diagrams exhibiting extended solid-solution and lens-like melting are often reproduced using ideal solutions, where ideal mixing considers a fully random configurational entropy of mixing. In the field of irreversible thermodynamics,…
Chemical short-range order (SRO) provides new opportunities for tuning alloy properties, but conventional computational thermodynamics frameworks such as CALPHAD, based on Bragg-Williams mean-field approximations, cannot properly describe…
Machine learning potentials (MLPs) represent atomic interactions with quantum mechanical accuracy offering an efficient tool for atomistic simulations in many fields of science. However, most MLPs rely on local atomic energies without…
The equilibrium short-range order (SRO) in Cu-Pd alloys is studied theoretically. The evolution of the Fermi surface-related splitting of the (110) diffuse intensity peak with changing temperature is examined. The results are compared with…
The metallurgy and materials communities have long known and exploited fundamental links between chemical and structural ordering in metallic solids and their mechanical properties. The highest reported strength achievable through the…
Equiatomic NiCoCr solid solutions have been recently shown to display exceptional mechanical properties, that have been suggested to be linked to nanostructural short-range order (SRO) features that may arise from thermal treatments, such…
Machine learning has emerged as a powerful approach in materials discovery. Its major challenge is selecting features that create interpretable representations of materials, useful across multiple prediction tasks. We introduce an…
Nitrogen (N) is a key alloying element that enhances the performance of Fe-Ni-Cr austenitic stainless steels, improving austenite stability, corrosion resistance, and yield strength. However, the role of N in modifying chemical ordering,…
We present an automated procedure for computing stacking fault energies in random alloys from large-scale simulations using moment tensor potentials (MTPs) with the accuracy of density functional theory (DFT). To that end, we develop an…
Recent developments in computational chemistry facilitate the automated quantum chemical exploration of chemical reaction networks for the in-silico prediction of synthesis pathways, yield, and selectivity. However, the underlying quantum…
We provide strong numerical evidence for a hidden multihyperuniform long-range order (MHLRO) in SiGeSn medium-entropy alloys (MEAs), in which the normalized infinite-wavelength composition fluctuations for all three atomic species are…
Short-range ordering (SRO) in Fe-Cr has been the subject of a number of recent experimental and theoretical investigations, as ordering effects are significant for the phase stability of this technologically important alloy. Recently,…
Metal-organic frameworks (MOFs) incorporating open metal sites (OMS) have been identified as promising sorbents for many societally relevant-adsorption applications including CO$_2$ capture, natural gas purification and H$_2$ storage. It is…
In many cases, the predictions of machine learning interatomic potentials (MLIPs) can be interpreted as a sum of body-ordered contributions, which is explicit when the model is directly built on neighbor density correlation descriptors, and…
Structural prediction for the discovery of novel materials is a long sought after goal of computational physics and materials sciences. The success is rather limited for methods such as the simulated annealing method (SA) that require…
Semiconductor materials manufacturing presents unique challenges for machine learning deployment due to evolving process conditions, equipment degradation, and raw material variability that can cause model performance deterioration over…
Metal-organic frameworks (MOFs) are porous crystalline materials with broad applications such as carbon capture and drug delivery, yet accurately predicting their 3D structures remains a significant challenge. While Large Language Models…