Related papers: Force Field-Agnostic Phase Classification of Zeoli…
Josephson junctions containing ferromagnetic layers have generated interest for application in cryogenic memory. In a junction containing both a magnetically hard fixed layer and soft free layer with carefully chosen thicknesses, the…
We study how solidification of model freely rotating polymers under athermal quasistatic compression varies with their bond angle $\theta_0$. All systems undergo two discrete, first-order-like transitions: entanglement at $\phi =…
An impedance spectroscopy (IS) is a versatile tool to study the effect of grains (bulk), grain boundaries and electrode-electrolyte interface on dielectric and electrical properties of electro-ceramics. This study only focuses the high…
Mobile apps, such as mHealth and wellness applications, can benefit from deep learning (DL) models trained with mobile sensing data collected by smart phones or wearable devices. However, currently there is no mobile sensing DL system that…
The paper focuses on numerical simulation of the phase-field (PF) equations for modeling martensitic transformations in shape memory alloys (SMAs), their complex microstructures and thermo-mechanical behavior. The PF model is based on the…
Understanding the structural and magnetic properties in layered hybrid organic-inorganic metal halide perovskites (HOIPs) is key for their design and integration in spin-electronic devices. Here, we have conducted a systematic study on ten…
The phase diagram of the XXZ spin-1 quantum magnet on the kagome lattice is studied for all cases where the $J_z$ coupling is antiferromagnetic. In the zero magnetic field case, the six previously introduced phases, found using various…
Machine learning has been establishing its potential in multiple areas of condensed matter physics and materials science. Here we develop and use an unsupervised machine learning workflow within a framework of first-principles-based…
This study investigates the feasibility and performance of federated learning (FL) for multi-label ICD code classification using clinical notes from the MIMIC-IV dataset. Unlike previous approaches that rely on centralized training or…
Engineering alloys generally exhibit multi-phase microstructures. For simulating their microstructure evolution during solid-state phase transformation, CALPHAD-guided multi-phase-field models coupled with micro-mechanics have proven to be…
Machine-learned force fields (MLFFs), especially pre-trained foundation models, are transforming computational materials science by enabling ab initio-level accuracy at molecular dynamics scales. Yet their rapid rise raises a key question:…
Predicting polymer glass transition temperatures (Tg) with first-principles fidelity has long remained out of reach, as cooling multi-thousand-atom systems over a broad temperature range at acceptable rates exceeds the computational limits…
The square-lattice Ising antiferromagnet subjected to the imaginary magnetic field $H=i \theta T /2 $ with the "topological" angle $\theta$ and temperature $T$ was investigated by means of the transfer-matrix method. Here, as a probe to…
We introduce and explore an approach for constructing force fields for small molecules, which combines intuitive low body order empirical force field terms with the concepts of data driven statistical fits of recent machine learned…
Fundamental differences between materials originate from the unique nature of their constituent chemical elements. Before specific differences emerge according to the precise ratios of elements in a given crystal structure, a material can…
In this work, we study a biquadratic Heisenberg model with coupled orbital degree of freedom using Monte Carlo simulation in order to investigate the phase transitions in iron-based superconductors. The antiferro-quadrupolar state, which…
Phase diagram of solid solutions of antiferroelectric and ferroelectric materials like PbZr$_{1-x}$Ti$_x$O$_3$ (PZT), Rb$_x$(NH$_4$)$_{1-x}$H$_2$PO$% _4$ (RADP) is calculated at the region $x\leq 0.1$. Antiferroelectric and ferroelectric…
We present a formalism for developing cyclic and helical symmetry-informed machine learned force fields (MLFFs). In particular, employing the smooth overlap of atomic positions descriptors with the polynomial kernel method, we derive cyclic…
Knowledge of phase diagrams is essential for material design as it helps in understanding microstructure evolution during processing. The determination of phase diagrams is thus one of the central tasks in materials science. When exploring…
Topological insulators have been studied intensively over the last decades. Among these materials, three-dimensional (3D) zirconium pentatelluride (ZrTe$_5$) stands out as one of the most intriguing for both theoretical and experimental…