Related papers: Capturing short-range order in high-entropy alloys…
The computational prediction of the structure and stability of hybrid organic-inorganic interfaces provides important insights into the measurable properties of electronic thin film devices, coatings, and catalyst surfaces and plays an…
Equiatomic CoCrNi medium entropy alloys are known for their unique properties linked to chemical short-range order (CSRO), crucial in both percolation processes and/or nucleation and growth processes influencing alloy passivation in aqueous…
The vastness of chemical space makes generalization a central challenge in the development of machine learning interatomic potentials (MLIPs). While MLIPs could enable large-scale atomistic simulations with near-quantum accuracy, their…
Entropy-stabilized oxides (ESOs), driven by high configurational entropy, have gained phenomenological research interest due to their potential for tailoring structure property relationships. However, the chemical short range ordering (SRO)…
Molecular dynamics simulations using an interatomic potential developed by artificial neural network deep machine learning are performed to study the local structural order in Al90Tb10 metallic glass. We show that more than 80% of the…
Chemical short-range order (CSRO), a form of nanoscale special atom arrangement, has been found to significantly alter material properties such as dislocation motion and defect dynamics in various alloys. Here, we use Fe-Ni-Cr alloys to…
The effects of atomic short-range order on the properties of Pb(Zr_{1-x}Ti_x)O_3 alloy in its morphotropic phase boundary (MPB) are predicted by combining first-principles-based methods and annealing techniques. Clustering is found to lead…
Thermomechanical processing alters the microstructure of metallic alloys through coupled plastic deformation and thermal exposure, with dislocation motion driving plasticity and microstructural evolution. Our previous work (Islam et al.,…
Machine learning interatomic potentials (MLIPs) enable large-scale atomistic simulations but remain challenged in describing mixed-valence materials where charge ordering strongly influences thermodynamic stability. Here we investigate the…
The chemical short-range order (CSRO) in the crystalline materials influences the properties and its effect is particularly important in the context of the multicomponent materials. We propose a scheme for CSRO parameter or…
We employ a descriptor based machine-learning approach to assess the effect of chemical alloying on formation-enthalpy of rare-earth intermetallics. Application of machine-learning approaches in rare-earth intermetallic design have been…
Machine learning potentials (MLPs) trained on accurate quantum chemical data can retain the high accuracy, while inflicting little computational demands. On the downside, they need to be trained for each individual system. In recent years,…
Corrosion has a wide impact on society, causing catastrophic damage to structurally engineered components. An emerging class of corrosion-resistant materials are high-entropy alloys. However, high-entropy alloys live in high-dimensional…
To describe chemical ordering in solid solutions systems Warren-Cowley short-range parameters are ordinarily used. However, they are not directly suited for application to long-range ordered systems, as they do not converge to zero for…
Machine-learning potentials (MLPs) for atomistic simulations are a promising alternative to conventional classical potentials. Current approaches rely on descriptors of the local atomic environment with dimensions that increase…
Machine Learning Potentials (MLPs) can enable simulations of ab initio accuracy at orders of magnitude lower computational cost. However, their effectiveness hinges on the availability of considerable datasets to ensure robust…
In recent years, many types of machine learning potentials (MLPs) have been introduced, which are able to represent high-dimensional potential-energy surfaces (PES) with close to first-principles accuracy. Most current MLPs rely on atomic…
Chemical short-range order (CSRO) has emerged as a critical structural feature in concentrated alloys, yet its coupling with hydrogen remains an active discussion. Here, we develop a machine-learning interatomic potential for the Co-Ni-V-H…
To investigate the formation of Mg-based long-period stacking ordered (LPSO) structure, we systematically study the preference of the short-range order (SRO) in metastable disordered phases of Mg-RE-Zn (RE = Y, La, Er, Ho, Dy, Tb) and…
Hydrogen embrittlement (HE) in austenitic stainless steels is advanced by hydrogen enhanced localized plasticity (HELP), typically accompanied by a transition from homogeneous to localized slip. Short-range order (SRO) in face-centered…