Related papers: Phase discovery with active learning: Application …
Titanium is the base material for a number of technologically important alloys for energy conversion and structural applications. Atomic-scale studies of Ti-based metals employing first-principles methods, such as density functional theory,…
Machine learning models of materials$^{1-5}$ accelerate discovery compared to ab initio methods: deep learning models now reproduce density functional theory (DFT)-calculated results at one hundred thousandths of the cost of DFT$^{6}$. To…
Investigating Li$^+$ transport within the amorphous lithium phosphorous oxynitride (LiPON) framework, especially across a Li||LiPON interface, has proven challenging due to its amorphous nature and varying stoichiometry, necessitating large…
Accounting for nuclear quantum effects (NQEs) can significantly alter material properties at finite temperatures. Atomic modeling using the path-integral molecular dynamics (PIMD) method can fully account for such effects, but requires…
Polycrystalline Ni-Co-Mn-Sn based ferromagnetic shape memory alloys (FSMAs) show promise as actuator materials, but their practical application involving magnetic field induced strain (MFIS) is often limited by three factors: the…
Machine-learned interatomic potentials (MLPs) provide near density functional theory (DFT) accuracy at reduced computational cost, but their reliability depends on representative training data and often deteriorates in transition-state…
Shape memory Ti-Ni alloys attracted much attention in the recent years, since they are shape memory, intelligent as well as functional materials. In the present investigation Ti51Ni49 and Ti51Ni45Fe4 alloys were synthesized through radio…
Ti-N material system have range of compounds with different stoichiometry like Ti2N, Ti3N2, Ti6N5, Ti4N3 alongwith Ti , TiN and solid solutions of N in Ti with a maximum of 23% solubility. In this work, we develop an interatomic potential…
Contactless, laser-based resonant ultrasound spectroscopy was utilized to monitor changes in elastic properties in single-crystalline NiTi shape memory alloy. It was observed that the elastic behavior of the temperature-induced B19$^\prime$…
Orbital-free density functional theory (OF-DFT) holds the promise to compute ground state molecular properties at minimal cost. However, it has been held back by our inability to compute the kinetic energy as a functional of the electron…
Two-dimensional electronic spectroscopy (2DES) provides rich information about how the electronic states of molecules, proteins, and solid-state materials interact with each other and their surrounding environment. Atomistic molecular…
Fracture behavior in nickel-titanium (NiTi) shape memory alloys (SMAs) subjected to mode-I, isothermal loading is studied using finite element analysis (FEA). Compact tension (CT) SMA specimen is modeled in Abaqus finite element suite and…
Machine-learned interatomic potentials (MLIPs), particularly graph neural network (GNN)-based models, offer a promising route to achieving near-density functional theory (DFT) accuracy at significantly reduced computational cost. However,…
Magnetic 2D materials have achieved significantly consideration owing to their encouraging applications. A variation of these 2D materials by occurrence of defects, by the transition-metal doping or adsorption or by the surface…
Superelastic NiTi 1 shape memory alloy (SMA) wire was subjected to thermomechanical loading tests in tension to evaluate stress and temperature conditions under which the wire deforms plastically. Although the alloy demonstrated a very high…
Shape Memory Alloys (SMAs) are a unique class of intermetallic alloys that can cyclically sustain large deformations and recover a designed geometry through a solid-to-solid phase transformation. SMAs provide favorable actuation energy…
We propose to adapt the confined pseudo-atomic orbitals underpinning the precalculated Slater-Koster (SK) interaction tables in Density Functional Tight Binding (DFTB) to local atomic environments. We demonstrate significant improvement in…
The phase stability and transformation between hexagonal close-packed (hcp) {\alpha}-phase and body-centered cubic (bcc) \b{eta}-phase in titanium (Ti) alloys are critical to their mechanical properties and manufacturing processes for…
High-throughput computational screening has emerged as a critical component of materials discovery. Direct density functional theory (DFT) simulation of inorganic materials and molecular transition metal complexes is often used to describe…
We present a novel deep learning (DL) approach to produce highly accurate predictions of macroscopic physical properties of solid solution binary alloys and magnetic systems. The major idea is to make use of the correlations between…