Modeling local chemistry in the presence of collective phenomena.

Confinement within the nanoscale pores of a zeolite strongly modifies the behavior of small molecules. Typical of many such interesting and important problems, realistic modeling of this phenomena requires simultaneously capturing the detailed behavior of chemical bonds and the possibility of collective dynamics occurring in a complex unit cell (672 atoms in the case of Zeolite-4A). Classical simulations alone cannot reliably model the breaking and formation of chemical bonds, while quantum methods alone are incapable of treating the extended length and time scales characteristic of complex dynamics. We have developed a robust and efficient model in which a small region treated with the Kohn-Sham density functional theory is embedded within a larger system represented with classical potentials. This model has been applied in concert with first-principles electronic structure calculations and classical molecular dynamics and Monte Carlo simulations to study the behavior of water, ammonia, the hydroxide ion, and the ammonium ion in Zeolite-4a. Understanding this behavior is important to the predictive modeling of the aging of Zeolite-based desiccants. In particular, we have studied the absorption of these molecules, interactions between water and the ammonium ion, and reactions between the hydroxide ion and the zeolite cage. We have shown that interactions with the extended Zeolite cage strongly modifies these local chemical phenomena, and thereby we have proven out hypothesis that capturing both local chemistry and collective phenomena is essential to realistic modeling of this system. Based on our results, we have been able to identify two possible mechanisms for the aging of Zeolite-based desiccants

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

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

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

Quantum computing accelerator I/O : LDRD 52750 final report.

In a superposition of quantum states, a bit can be in both the states '0' and '1' at the same time. This feature of the quantum bit or qubit has no parallel in classical systems. Currently, quantum computers consisting of 4 to 7 qubits in a 'quantum computing register' have been built. Innovative algorithms suited to quantum computing are now beginning to emerge, applicable to sorting and cryptanalysis, and other applications. A framework for overcoming slightly inaccurate quantum gate interactions and for causing quantum states to survive interactions with surrounding environment is emerging, called quantum error correction. Thus there is the potential for rapid advances in this field. Although quantum information processing can be applied to secure communication links (quantum cryptography) and to crack conventional cryptosystems, the first few computing applications will likely involve a 'quantum computing accelerator' similar to a 'floating point arithmetic accelerator' interfaced to a conventional Von Neumann computer architecture. This research is to develop a roadmap for applying Sandia's capabilities to the solution of some of the problems associated with maintaining quantum information, and with getting data into and out of such a 'quantum computing accelerator'. We propose to focus this work on 'quantum I/O technologies' by applying quantum optics on semiconductor nanostructures to leverage Sandia's expertise in semiconductor microelectronic/photonic fabrication techniques, as well as its expertise in information theory, processing, and algorithms. The work will be guided by understanding of practical requirements of computing and communication architectures. This effort will incorporate ongoing collaboration between 9000, 6000 and 1000 and between junior and senior personnel. Follow-on work to fabricate and evaluate appropriate experimental nano/microstructures will be proposed as a result of this work