Related papers: Modeling refractory high-entropy alloys with effic…
This work presents a pilot study on a strained complex concentrated alloy based on refractory elements: MoNbTaTiZr. Initially, the as-cast and homogenization-annealed conditions were characterized. After casting, the alloy consists of two…
We present a scale-bridging approach based on a multi-fidelity (MF) machine-learning (ML) framework leveraging Gaussian processes (GP) to fuse atomistic computational model predictions across multiple levels of fidelity. Through the…
Atomistic simulations can provide useful insights into the physical properties of multi-principal-element alloys. However, classical potentials mostly fail to capture key quantum (electronic-structure) effects. We present a deep 3D…
EXAFS analysis of pure elements, binary and ternary equiatomic refractory alloys within the Nb-Zr-Ti-Hf-Ta system is performed at the Nb and Zr K-edges to analyze the evolution of the chemical local environment and the lattice distortion. A…
Here we analyze multiple symmetry-inequivalent atomic configurations across the entire composition range of the isovalent and isostructural Mo$_x$W$_{1-x}$S$_2$ alloy using density-functional theory and Monte Carlo simulations. Our results…
Segregation to defects, in particular to grain boundaries (GBs), is an unavoidable phenomenon leading to changed material behavior over time. With the increase of available computational power, unbiased quantum-mechanical predictions of…
Quantum computers can potentially achieve an exponential speedup versus classical computers on certain computational tasks, as recently demonstrated in systems of superconducting qubits. However, these qubits have large footprints due to…
Machine-learning interatomic potentials (MLIPs) enable large-scale atomistic simulations at moderate computational cost while retaining ab initio accuracy. MLIPs trained on coupled-cluster data, particularly CCSD(T), have emerged as a…
Recent experiments demonstrate a "robust superconductivity phenomenon" in niobium-based alloys, where the superconducting state remains intact and the critical temperature ($T_c$) is largely unaffected by external pressure well above tens…
Van der Waals (vdW) crystals are prone to twisting, sliding, and buckling due to inherently weak interlayer interactions. While thickness-controlled vdW structures have attracted considerable attention as ultrathin semiconducting channels,…
The apparent contradiction between the recently observed weak charge disproportion and the traditional Mn$^{3+}$/Mn$^{4+}$ picture of the charge-orbital orders in half-doped manganites is resolved by a novel Wannier states analysis of the…
Twisted layered van-der-Waals materials often exhibit unique electronic and optical properties absent in their non-twisted counterparts. Unfortunately, predicting such properties is hindered by the difficulty in determining the atomic…
We introduce machine-learned potentials for Ag-Pd to describe the energy of alloy configurations over a wide range of compositions. We compare two different approaches. Moment tensor potentials (MTP) are polynomial-like functions of…
Li$_6$PS$_5$Cl is a promising candidate for the solid electrolyte in all-solid-state Li-ion batteries. In applications, this material is in a polycrystalline state with grain boundaries (GBs) that can affect ionic conductivity. While…
Metal-organic frameworks (MOFs) are highly interesting and tunable materials. By incorporating spatial defects into their atomic structure, MOFs can be finetuned to exhibit precise chemical functionalities, extending their applicability in…
Phase equilibria in the Al-Ti-Nb-Zr-Ta refractory complex concentrated alloy system were investigated using a high throughput experimental approach. A pseudo-ternary section of the quinary compositional space was prepared by a honeycomb…
The Gaussian approximation potential (GAP) is an accurate machine-learning interatomic potential that was recently extended to include the description of radiation effects. In this study, we seek to validate a faster version of GAP, known…
Machine learning interatomic potentials (MLIPs) are routinely used to model diverse atomistic phenomena, yet parameterizing them to accurately capture solid-state phase transformations remains difficult. We present error metrics and…
Superconducting high-entropy alloys have recently emerged as a new platform for exploring superconductivity in highly disordered metallic systems and may offer advantages for applications requiring mechanical robustness and tolerance to…
With first-principles theoretical analysis of the local structure using Bond Orientational Order parameters and Voronoi partitioning, we establish (a) HCP$\rightarrow$BCC structural transformation in high-entropy alloys (HEAs)…