Materials Sciences and Engineering Research Highlights

Complex magnetic energy landscapes in topological magnets

Inelastic neutron scattering measurements and first-principles calculations reveal a hierarchy of competing magnetic energy scales in the RMn6Sn6 (R=rare earth) family of kagome-lattice-based topological magnets.

Demonstration of dark-state metasurface laser

Demonstrated lasing in dark resonant states of a surface laser and controllable coherent emission through the aid of a tightly coupled, weakly radiative metasurface.

2D Binary Superlattices of Gold Nanoparticles

Two distinct complex two-dimensional binary superlattices were self-assembled by co-crystallizing gold nanoparticles (AuNPs) of two distinct sizes.

The Atomic-Level Structure of Mesoporous h-BN Revealed

Solid-state NMR spectroscopy and plane-wave DFT reveal the structure of pores/defects of mesoporous hexagonal boron nitride (p-BN).

A New Fermionic State Revealed

A new type of Fermi arcs have been discovered, with spin textures and novel magnetic splitting driven by long range antiferromagnetic order in cubic NdBi not readily explained by existing theories.

Designing Transformations in High-Entropy Ferritic Steels

Design of order-disorder transformations in high-entropy ferritic (Ti-Cr-Fe-Ni-Al) steels by a novel electron-based theory that identifies electronic origins for chemical ordering and how to control it.

Spintronics by guided chemical doping and disorder

Established simple electron-count, chemical-alloy design guide to enhance half-metal behavior, accelerating discovery of new spintronic systems for use in memory and nano-electronic devices.

Tuning electron count and phase stability graphs.

Small-moment antiferromagnetic ordering

Three zero-field magnetic transitions temperatures and six ordered phases found. These magnetically ordered phases are associated with small moments (∼0.12 μB/Ni) in the saturated state and small changes in entropy.

Magnetic Field vs Temperature phase diagram of La2Ni7

Improved quantum computing algorithm to find ground states of correlated quantum models

A new variational quantum algorithm improves the preparation of quantum many-body ground states by automatically building efficient quantum circuits.

Deep learning crystal field parameters from thermodynamic observables

Discovery of deep neural net algorithm that extracts crystal field parameters in an efficient and unbiased way from thermodynamic results.

Visual of workflow from CNN input wavelet to crystal field parameter output.