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Galib, Mirza

Publications and source records attributed to Galib, Mirza.

5 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Controlled Formation of Conduction Channels in Memristive Devices Observed by X–ray Multimodal Imaging

Neuromorphic computing provides a means for achieving faster and more energy efficient computations than conventional digital computers for artificial intelligence (AI). However, its current accuracy is generally less than the dominant software-based AI. The key to improving accuracy is to reduce the intrinsic randomness of memristive devices, emulating synapses in the brain for neuromorphic computing. Here using a planar device as a model system, the controlled formation of conduction channels is achieved with high oxygen vacancy concentrations through the design of sharp protrusions in the electrode gap, as observed by X-ray multimodal imaging of both oxygen stoichiometry and crystallinity. Classical molecular dynamics simulations confirm that the controlled formation of conduction channels arises from confinement of the electric field, yielding a reproducible spatial distribution of oxygen vacancies across switching cycles. Furthermore, this work demonstrates an effective route to control the otherwise random electroforming process by electrode design, facilitating the development of more accurate memristive devices for neuromorphic computing.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Near-Quantitative Predictions of the First-Shell Coordination Structure of Hydrated First-Row Transition Metal Ions Using K-Edge X-ray Absorption Near-Edge Spectroscopy

Understanding the solvation structure of transition metal ions is important due to its broad impact on the kinetics, stability, and reactivity in a variety of applications in geochemistry, biochemistry, energy storage, and environmental chemistry. Using water as the common ligand, we study the X-ray absorption pre-edge and near-edge spectra at the K-edge of a near-complete series of hydrated first-row transition metal ions with d-orbital occupancy ranging from d 2 to d 10 . Starting with optimized structures that were derived from an explicit solvation treatment at the density functional theory (DFT) level, we then compute the pre-edge X-ray absorption spectra at the metal ion K-edge at the time dependent density functional theory (TDDFT) and restricted active-space second-order perturbation theory (RASPT2) levels of theory. TDDFT calculations provide accurate results for spectra that are dominated by single excitations, while significant improvements were obtained with RASPT2 calculations that correctly distinguish between singly and doubly excited states, where relevant, with quantitative accuracy compared with experiment. We analyze and assign the dierent pre-edge features for each metal ion in order to reveal the impact of the variations in the d orbital occupancy on the rst-shell coordination environment. For completeness, we also report the lowest energy ligand-field d-d transitions for all the transition metal aqua ions considered in this study using complete active space second order perturbation theory (CASPT2).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Uptake of N 2 O 5 by aqueous aerosol unveiled using chemically accurate many-body potentials

The reactive uptake of N 2 O 5 to aqueous aerosol is a major loss channel for nitrogen oxides in the troposphere. Despite its importance, a quantitative picture of the uptake mechanism is missing. Here we use molecular dynamics simulations with a data-driven many-body model of coupled-cluster accuracy to quantify thermodynamics and kinetics of solvation and adsorption of N 2 O 5 in water. The free energy profile highlights that N 2 O 5 is selectively adsorbed to the liquid–vapor interface and weakly solvated. Accommodation into bulk water occurs slowly, competing with evaporation upon adsorption from gas phase. Leveraging the quantitative accuracy of the model, we parameterize and solve a reaction–diffusion equation to determine hydrolysis rates consistent with experimental observations. We find a short reaction–diffusion length, indicating that the uptake is dominated by interfacial features. The parameters deduced here, including solubility, accommodation coefficient, and hydrolysis rate, afford a foundation for which to consider the reactive loss of N 2 O 5 in more complex solutions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Learning intermolecular forces at liquid–vapor interfaces

By adopting a perspective informed by contemporary liquid-state theory, we consider how to train an artificial neural network potential to describe inhomogeneous, disordered systems. Here, we find that neural network potentials based on local representations of atomic environments are capable of describing some properties of liquid-vapor interfaces but typically fail for properties that depend on unbalanced long-ranged interactions that build up in the presence of broken translation symmetry. These same interactions cancel in the translationally invariant bulk, allowing local neural network potentials to describe bulk properties correctly. By incorporating explicit models of the slowly varying long-ranged interactions and training neural networks only on the short-ranged components, we can arrive at potentials that robustly recover interfacial properties. We find that local neural network models can sometimes approximate a local molecular field potential to correct for the truncated interactions, but this behavior is variable and hard to learn. Generally, we find that models with explicit electrostatics are easier to train and have higher accuracy. We demonstrate this perspective in a simple model of an asymmetric dipolar fluid, where the exact long-ranged interaction is known, and in an ab initio water model, where it is approximated.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗