Machine learning the electronic structure of matter across temperatures

We introduce machine learning (ML) models that predict the electronic structure of materials across a wide temperature range. Our models employ neural networks and are trained on density functional theory (DFT) data. Unlike other ML models that use DFT data, our models directly predict the local density of states (LDOS) of the electronic structure. This provides several advantages, including access to multiple observables such as the electronic density and electronic total free energy. Moreover, our models account for both the electronic and ionic temperatures independently, making them ideal for applications like laser-heating of matter. We validate the efficacy of our LDOS-based models on a metallic test system. They accurately capture energetic effects induced by variations in ionic and electronic temperatures over a broad temperature range, even when trained on a subset of these temperatures. These findings open up exciting opportunities for investigating the electronic structure of materials under both ambient and extreme conditions

Elastically Induced Coexistence of Surface Reconstructions

Scanning tunneling microscopy of Sb-capped GaAs shows the coexistence of different surface reconstructions. The majority of the surface consists of an α2(2×4) reconstruction typically observed for GaAs(001) surfaces. At step edges, an α(4×3) reconstruction, common for GaSb(001), is observed. We argue that strain couples the surface reconstruction to the film morphology. Density functional theory calculations show that the (2×4) reconstruction is stabilized in GaSb films when the lattice parameter is constrained to that of GaAs, as happens in the middle of a terrace, while the (4×3) reconstruction is stabilized when the lattice parameter is allowed to relax toward that of GaSb at step edges. This result confirms the importance of elastic relaxation in the coexistence of surface reconstructions

Atomic Size Mismatch Strain Induced Surface Reconstructions

The effects of lattice mismatch strain and atomic size mismatch strain on surface reconstructions are analyzed using density functional theory. These calculations demonstrate the importance of an explicit treatment of alloying when calculating the energies of alloyed surface reconstructions. Lattice mismatch strain has little impact on surface dimer ordering for the α2(2×4) reconstruction of GaAs alloyed with In. However, atomic size mismatch strain induces the surface In atoms to preferentially alternate position, which, in turn, induces an alternating configuration of the surface anion dimers. These results agree well with experimental data for α2(2×4) domains in InGaAs∕GaAs surfaces

Variational finite-difference representation of the kinetic energy operator

A potential disadvantage of real-space-grid electronic structure methods is the lack of a variational principle and the concomitant increase of total energy with grid refinement. We show that the origin of this feature is the systematic underestimation of the kinetic energy by the finite difference representation of the Laplacian operator. We present an alternative representation that provides a rigorous upper bound estimate of the true kinetic energy and we illustrate its properties with a harmonic oscillator potential. For a more realistic application, we study the convergence of the total energy of bulk silicon using a real-space-grid density-functional code and employing both the conventional and the alternative representations of the kinetic energy operator.Comment: 3 pages, 3 figures, 1 table. To appear in Phys. Rev. B. Contribution for the 10th anniversary of the eprint serve

Prognostic value of the ratio between prothesis area and indexed annulus area measured by multiSlice-CT for transcatheter aortic valve implantation procedures

Background Postprocedural aortic regurgitations following transcatheter aortic valve implantation (TAVI) procedures remain an is- sue. Benefit of oversizing strategies to prevent them isn’t well established. We compared different level of oversizing in our cohort of con- secutive patients to address if severe oversizing compared to normal sizing had an impact on post-procedural outcomes. Methods From January 2010 to August 2013, consecutive patients were referred for TAVI with preoperative Multislice-CT (MSCT) and the procedures were achieved using Edwards Sapien® or Corevalve devices®. Retrospectively, according to pre-procedural MSCT and the valve size, pa- tients were classified into three groups: normal, moderate and severe oversizing; depending on the ratio between the prosthesis area and the annulus area indexed and measured on MSCT. Main endpoint was mid-term mortality and secondary endpoints were the Valve Academic Research Consortium (VARC-2) endpoints. Results Two hundred and sixty eight patients had a MSCT and underwent TAVI procedure, with mainly Corevalve®. While all-cause and cardiovascular mortality rates were similar in all groups, post-procedural new pacemaker (PM) implantation rate was significantly higher in the severe oversizing group (P = 0.03), while we observed more in-hospital congestive heart-failure (P = 0.02) in the normal sizing group. There was a trend toward more moderate to severe aortic regurgitation (AR) in the normal sizing group (P = 0.07). Conclusions Despite a higher rate of PM implantation, oversizing based on this ratio reduces aortic leak with lower rates of post-procedural complications and a similar mid-term survival

Predicting electronic structures at any length scale with machine learning

The properties of electrons in matter are of fundamental importance. They give rise to virtually all molecular and material properties and determine the physics at play in objects ranging from semiconductor devices to the interior of giant gas planets. Modeling and simulation of such diverse applications rely primarily on density functional theory (DFT), which has become the principal method for predicting the electronic structure of matter. While DFT calculations have proven to be very useful to the point of being recognized with a Nobel prize in 1998, their computational scaling limits them to small systems. We have developed a machine learning framework for predicting the electronic structure on any length scale. It shows up to three orders of magnitude speedup on systems where DFT is tractable and, more importantly, enables predictions on scales where DFT calculations are infeasible. Our work demonstrates how machine learning circumvents a long-standing computational bottleneck and advances science to frontiers intractable with any current solutions. This unprecedented modeling capability opens up an inexhaustible range of applications in astrophysics, novel materials discovery, and energy solutions for a sustainable future

Spectroscopic ellipsometry characterization of thin-film silicon nitride

We have measured and analyzed the optical characteristics of a series of silicon nitride thin films prepared by plasma-enhanced chemical vapor deposition on silicon substrates for photovoltaic applications. Spectroscopic ellipsometry measurements were made by using a two-channel spectroscopic polarization modulator ellipsometer that measures N, S, and C data simultaneously. The data were fit to a model consisting of air / roughness / SiN / crystalline silicon. The roughness was modeled using the Bruggeman effective medium approximation, assuming 50% SiN, 50% voids. The optical functions of the SiN film were parameterized using a model by Jellison and Modine. All the {Chi}{sup 2} are near 1, demonstrating that this model works extremely well for all SiN films. The measured dielectric functions were used to make optimized SiN antireflection coatings for crystalline silicon solar cells