22 research outputs found
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
Recommended from our members
Defect-related internal dissipation in mechanical resonators and the study of coupled mechanical systems.
Understanding internal dissipation in resonant mechanical systems at the micro- and nanoscale is of great technological and fundamental interest. Resonant mechanical systems are central to many sensor technologies, and microscale resonators form the basis of a variety of scanning probe microscopies. Furthermore, coupled resonant mechanical systems are of great utility for the study of complex dynamics in systems ranging from biology to electronics to photonics. In this work, we report the detailed experimental study of internal dissipation in micro- and nanomechanical oscillators fabricated from amorphous and crystalline diamond materials, atomistic modeling of dissipation in amorphous, defect-free, and defect-containing crystalline silicon, and experimental work on the properties of one-dimensional and two-dimensional coupled mechanical oscillator arrays. We have identified that internal dissipation in most micro- and nanoscale oscillators is limited by defect relaxation processes, with large differences in the nature of the defects as the local order of the material ranges from amorphous to crystalline. Atomistic simulations also showed a dominant role of defect relaxation processes in controlling internal dissipation. Our studies of one-dimensional and two-dimensional coupled oscillator arrays revealed that it is possible to create mechanical systems that should be ideal for the study of non-linear dynamics and localization
Recommended from our members
Ab-Initio Studies of ALSB(001) Adatom Behavior and Reconstruction
Recommended from our members
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
Recommended from our members
First-principles study of Se-intercalated graphite
Se-intercalated graphite compounds (Se-GICs) are considered as promising candidates for room-temperature thermoelectric cooling devices. Here the authors analyze the crystallographic structure and electronic properties of these materials within the framework of density-functional theory. First, the Adaptive-Coordinate Real-space Electronic Structure (ACRES) code is used to determine the stable structure of a representative stage-2 Se-GIC by relaxing atomic positions. The stable configuration is found to be a pendant-type structure, in which each selenium is bonded covalently to two atoms within the same carbon layer, causing a local distortion of the in-plane conjugation of the graphite. Then, they use the full potential linearized augmented plane wave (FP-LAPW) method to calculate the electronic band structure of the material and discuss its properties. Near the Fermi energy E{sub F}, there are wide bands originating from the host graphitic electronic structure and a few very narrow bands mainly of Se 4p character. The latter bands contribute to high peaks in the density of states close to E{sub F}. They show that this feature, although typical of many good thermoelectrics, does not necessarily imply high thermopower in the case of Se-GICs