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

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

HARES: an efficient method for first-principles electronic structure calculations of complex systems

We discuss our new implementation of the Real-space Electronic Structure method for studying the atomic and electronic structure of infinite periodic as well as finite systems, based on density functional theory. This improved version which we call HARES (for High-performance-fortran Adaptive grid Real-space Electronic Structure) aims at making the method widely applicable and efficient, using high performance Fortran on parallel architectures. The scaling of various parts of a HARES calculation is analyzed and compared to that of plane-wave based methods. The new developments that lead to enhanced performance, and their parallel implementation, are presented in detail. We illustrate the application of HARES to the study of elemental crystalline solids, molecules and complex crystalline materials, such as blue bronze and zeolites.Comment: 17 two-column pages, including 9 figures, 5 tables. To appear in Computer Physics Communications. Several minor revisions based on feedbac

The unusual conduction band minimum formation of Ga(As{sub 0.5{minus}y}P{sub 0.5{minus}y}N{sub 2y}) alloys

The conduction band minimum formation of GaAs{sub 0.5{minus}y}P{sub 0.5{minus}y}N{sub 2y} is investigated for small nitrogen compositions (0.1% < 2y < 1.0%), by using a pseudopotential technique. This formation is caused by two unusual processes both involving the deep-gap impurity level existing in the dilute alloy limit y {r_arrow} 0. The first process is an anticrossing with the {Gamma}{sub Ic}-like extended state of GaAs{sub 0.5}P{sub 0.5}. The second process is an interaction with other impurity levels forming a subband. These two processes are expected to occur in any alloys exhibiting a deep-gap impurity level at one of its dilute limit

Determining The GaSb/GaAs-(2 × 8) Reconstruction

Highly strained thin layers of GaSb/GaAs possess a (2 × 4) reconstruction at low Sb overpressures, and a (2 × 8) reconstruction at high Sb overpressures. While the atomic details of the Sb/GaAs-(2 × 4) are well known, the details of the (2 × 8) are not understood. In this paper, we use density functional theory to analyze possible (2 × 8) structures. Comparing scanning tunneling microscope images from both simulation and experiment and examining the relative energies of possible (2 × 8) structures, we show the α(2 × 8) and β(2 × 8) are the thermodynamically stable surface reconstructions for high Sb content films strained to the GaAs lattice parameter. The α and β(2 × 8) reconstructions are related to the GaAs-α2(2 × 4) and GaAs-β2(2 × 4) through the addition of 2 cations and 8 anions into the trench between adjacent (2 × 4) unit cells