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Martensitic and magnetic properties of ferromagnetic shape memory alloys are known to depend up on structural modulations and associated changes in the Fermi surface. These modulations although periodic and spanning over multiple unit…

Materials Science · Physics 2008-04-24 K. R. Priolkar , P. A. Bhobe , P. R. Sarode

Fe, Mg, and O are among the most abundant elements in terrestrial planets. While the behavior of the Fe-O, Mg-O, and Fe-Mg binary systems under pressure have been investigated, there are still very few studies of the Fe-Mg-O ternary system…

Solid-solution strengthening results from solutes impeding the glide of dislocations. Existing theories of strength rely on solute-dislocation interactions, but do not consider dislocation core structures, which need an accurate treatment…

Materials Science · Physics 2012-08-27 Joseph A. Yasi , Louis G. Hector, , Dallas R. Trinkle

A class of Fe-Mn-Si-based alloys exhibits a reversible martensitic transformation between the $\gamma$ phase with a face-centered cubic~(fcc) structure and an $\epsilon$ phase with a hexagonal close-packed (hcp) structure. During the…

Materials Science · Physics 2021-10-05 Takao Tsumuraya , Ikumu Watanabe , Takahiro Sawaguchi

We present a structural data set of the 20 proteinogenic amino acids and their amino-methylated and acetylated (capped) dipeptides. Different protonation states of the backbone (uncharged and zwitterionic) were considered for the amino…

Biomolecules · Quantitative Biology 2016-01-12 Matti Ropo , Markus Schneider , Carsten Baldauf , Volker Blum

The thermodynamics properties of the wurtzite and zinc-blende \InGaN alloys are calculated using first-principles density-functional calculations. Special quasi-random structures are used to describe the disordered alloys, for $x= 1/4,…

Materials Science · Physics 2007-05-23 Chee Kwan Gan , Yuan Ping Feng , David J. Srolovitz

Scanning tunneling microscopy (STM) is a powerful technique for imaging atomic structure and inferring information on local elemental composition, chemical bonding, and electronic excitations. However, traditional methods of visual…

Superconductivity · Physics 2023-02-21 Qiang Zou , Basu Dev Oli , Huimin Zhang , Joseph Benigno , Xin Li , Lian Li

Machine learning has emerged as a novel tool for the efficient prediction of materials properties, and claims have been made that machine-learned models for the formation energy of compounds can approach the accuracy of Density Functional…

Materials Science · Physics 2020-07-14 Christopher J. Bartel , Amalie Trewartha , Qi Wang , Alexander Dunn , Anubhav Jain , Gerbrand Ceder

Motivated by interest in the elastic properties of high strength amorphous metals, we examine the elastic properties of select crystalline phases. Using first principles methods, we calculate elastic moduli in various chemical systems…

We present a forward prediction material screening framework designed to discover Pd-Cu alloys with improved B2 phase stability, thereby unlocking simultaneous $H_2$ generation and utilization. First, we trained CatBoost models with…

The discovery of intrinsic magnetic topological order in $\rm MnBi_2Te_4$ has invigorated the search for materials with coexisting magnetic and topological phases. These multi-order quantum materials are expected to exhibit new topological…

The behavior of shape memory alloy (SMA) nanostructures is influenced by strain rate and temperature evolution during dynamic loading. The coupling between temperature, strain and strain rate effects is essential to capture inherent…

Materials Science · Physics 2014-07-01 R. Dhote , H. Gomez , R. Melnik , J. Zu

A multitude of observed boron-based materials have outstanding superconducting, mechanical, and refractory properties. Yet, the structure, the composition, and the very existence of some reported metal boride (M-B) compounds have been a…

Materials Science · Physics 2014-10-03 A. G. Van Der Geest , A. N. Kolmogorov

Machine learning (ML) is shown to predict new alloys and their performances in a high dimensional, multiple-target-property design space that considers chemistry, multi-step processing routes, and characterization methodology variations. A…

Materials Science · Physics 2020-10-12 Sen Liu , Branden B. Kappes , Behnam Amin-ahmadi , Othmane Benafan , Xiaoli Zhang , Aaron P. Stebner

The design and development of cutting-edge light materials for extreme conditions including high-speed impact remains a continuing and significant challenge in spite of steady advances. Magnesium (Mg) and its alloys have gained much…

Materials Science · Physics 2022-10-19 Xingsheng Sun

Employing the Ginzburg-Landau phase-field theory, a new coupled dynamic thermo-mechanical 3D model has been proposed for modeling the cubic-to-tetragonal martensitic transformations in shape memory alloy (SMA) nanostructures. The…

Materials Science · Physics 2015-05-20 R. Dhote , H. Gomez , R. Melnik , J. Zu

We analyze generic sequences for which the geometrically linear energy \[E_\eta(u,\chi):= \eta^{-\frac{2}{3}}\int_{B_{0}(1)} \left| e(u)- \sum_{i=1}^3 \chi_ie_i\right|^2 d x+\eta^\frac{1}{3} \sum_{i=1}^3 |D\chi_i|(B_{0}(1))\] remains…

Analysis of PDEs · Mathematics 2017-10-24 Thilo Simon

Intermetallics, which encompass a wide range of compounds, often exhibit similar or closely related crystal structures, resulting in various intermetallic systems with structurally derivative phases. This study examines the hypothesis that…

Decades of hardware, methodological, and algorithmic development have propelled molecular dynamics (MD) simulations to the forefront of materials-modeling techniques, bridging the gap between electronic-structure theory and continuum…

Soft Condensed Matter · Physics 2020-11-11 Tristan Bereau

Stable or metastable crystal structures of assembled atoms can be predicted by finding the global or local minima of the energy surface within a broad space of atomic configurations. Generally, this requires repeated first-principles energy…