Related papers: Computational screening of magnetocaloric alloys
Direct Dark Matter detection and studies on the nature of neutrinos demand detector systems with extremely low background levels, including from radioactivity. Additive-free, electroformed copper, in addition to a set of advantages,…
Layered Li(Ni,Mn,Co,)O$_2$ (NMC) presents an intriguing ternary alloy design space for optimization as a cathode material in Li-ion batteries. Recently, the high cost and resource limitations of Co have added a new design constraint and…
Co$_{1-x}$Mn$_x$Cr$_2$O$_4$ crystallizes as a normal spinel in the cubic $Fd \overline{3}m$ space group, and the end members have been reported to display a region of collinear ferrimagnetism as well as a low-temperature spin-spiral state…
We introduce machine learning models of quantum mechanical observables of atoms in molecules. Instant out-of-sample predictions for proton and carbon nuclear chemical shifts, atomic core level excitations, and forces on atoms reach…
Transition metal chromophores with earth-abundant transition metals are an important design target for their applications in lighting and non-toxic bioimaging, but their design is challenged by the scarcity of complexes that simultaneously…
We demonstrate the imaging of ferromagnetic carbon steel samples and we detect the thinning of their profile with a sensitivity of 0.1 mm using a Cs radio-frequency atomic magnetometer. Images are obtained at room temperature, in…
Recent advances in scanning transmission electron and scanning tunneling microscopies allow researchers to measure materials structural and electronic properties, such as atomic displacements and charge density modulations, at an Angstrom…
A thorough in situ characterization of materials at extreme conditions is challenging, and computational tools such as crystal structural search methods in combination with ab initio calculations are widely used to guide experiments by…
Using Density Functional Theory and a thermodynamic model [Physical Review B 86, 134418 (2012)], in this paper, we provide an approach to systematically screen compounds of a given Heusler family to predict ones that can yield giant…
Accurate and efficient calculations of absorption spectra of molecules and materials are essential for the understanding and rational design of broad classes of systems. Solving the Bethe-Salpeter equation (BSE) for electron-hole pairs…
This paper presents a combined numerical-theoretical study of the macroscopic behavior and local field distributions in a special class of two-dimensional periodic composites with viscoplastic phases. The emphasis is on strongly nonlinear…
Ultrafast diffraction imaging is a powerful tool to retrieve the geometric structure of gas-phase molecules with combined picometre spatial and attosecond temporal resolution. However, structural retrieval becomes progressively difficult…
We have built an integrated computational platform for material properties at extreme conditions, ProME (Professional Materials at Extremes) v1.0, which enables integrated calculations for multicomponent alloys, covering high temperatures…
Designing alloys for additive manufacturing (AM) presents significant opportunities. Still, the chemical composition and processing conditions required for printability (ie., their suitability for fabrication via AM) are challenging to…
Multi-principal element alloys open large composition spaces for alloy development. The large compositional space necessitates rapid synthesis and characterization to identify promising materials, as well as predictive strategies for alloy…
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…
Point defects in solid-state materials are now routinely simulated using large supercell structures, requiring efficient quantum mechanical solutions. Data-driven and machine learning (ML) models trained on computational data can enable…
Active learning (AL) can drastically accelerate materials discovery; its power has been shown in various classes of materials and target properties. Prior efforts have used machine learning models for the optimal selection of physical…
Metal-Organic Frameworks (MOFs) are materials with a high degree of porosity that can be used for applications in energy storage, water desalination, gas storage, and gas separation. However, the chemical space of MOFs is close to an…
Searching ferromagnetic semiconductor materials with electrically controllable spin polarization is a long-term challenge for spintronics. Bipolar magnetic semiconductors (BMS), with valence and conduction band edges fully spin-polarized in…