DOCSIC: A Mean-Field Method for Orbital-by-Orbital Self-Interaction Correction
Not Available
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Engineering topics
Barone, Veronica
Publications and source records attributed to Barone, Veronica.
Local lattice distortions and electronic orders in strongly correlated systems by resonant total x-ray scattering: A case study of A Pt 2 X 2 intermetallics ( A = U , Ce, or La and X = Si or Ge)
Revealing local lattice distortions in physical systems is nontrivial, largely because such structural features often are not necessarily amenable to traditional crystallographic investigations. This poses limits to our understanding of the underlying physic, particularly in the case of strongly correlated systems where structural, electronic and magnetic degrees of freedom are intertwined. Using resonant total x-ray scattering coupled to differential pair distribution function analysis, we reveal the presence of pronounced local lattice distortions in the square net Pt planes in the prototypical strongly correlated APt 2 X 2 intermetallics (A = U, Ce or La and X = Si or Ge). The distortions are present before charge density wave, magnetic, Kondo lattice coherence and/or superconducting orders emerge in these materials and, as density functional calculations suggest, are likely to affect them. Finally, our study sheds light on the poorly known interactions between electronic orders and structural disorder in strongly correlated systems and also demonstrates an advanced experimental approach to determine it that is relevant to any physical system showing deviations from perfect crystallinity.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Charge density wave order, local lattice distortions, and topological electronic states in Nb Te 4
Assessing the atomic and electronic structure of strongly correlated systems in which the crystal symmetry changes due to emergent lattice distortions, such as charge density waves (CDWs), is nontrivial because the distortions are not necessarily amenable to a traditional crystallographic description. Using advance scattering and modeling techniques, we reveal the evolution of the atomic displacement modes behind the CDW phases of quasi-one-dimensional NbTe 4 and also derive the so far unknown atomic structure of its low-temperature commensurate (C) CDW phase. Electronic structure calculations based on the experimental C-CDW structure data predict the existence of Dirac fermions whose multiplicity turns out to depend on the degree of lattice distortions accounted for in the experimental structure derivation. Finally, we argue that the electronic structure of CDW systems in general, and in particular in transition metal tetra-tellurides, can be strongly impacted by local lattice distortions and, therefore, they should be fully accounted for when their rich physics is considered.
36 MATERIALS SCIENCE↗
Accelerating the discovery of battery electrode materials through data mining and deep learning models
The availability of crystalline materials databases allows for building accurate machine learning (ML) models that can accelerate the exploration of materials chemical space for energy storage applications. In this work, we screen all inorganic materials included in the Materials Project and AFLOW databases as potential metal-ion battery electrodes. We develop an efficient protocol to mine and screen raw data in current databases and provide a new database of electrode materials by considering pairs of charged and discharged electrodes. This effort leads to a new database with over 190,000 instances, in contrast to the original battery database which contains about 5000. The expanded battery data set is then used to build regression-based deep neural network models for predicting average voltages and percentage volume changes upon charging and discharging, which present improvements of at least 28% for target properties with respect to previous models, and are now able to predict anode electrodes (low voltage region) as well as electrodes that will not work in electrochemical cells (negative voltages), overcoming the challenges identified in previous ML models for battery electrodes. Additionally, a further screening of the expanded database itself allowed us to identify 35 novel electrode candidates with excellent battery performance metrics.
25 ENERGY STORAGE↗