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Your search keyword '"Aditya Koneru"' showing total 4 results
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4 results on '"Aditya Koneru"'

1. Multi-reward reinforcement learning based development of inter-atomic potential models for silica

Author

Aditya Koneru, Henry Chan, Sukriti Manna, Troy D. Loeffler, Debdas Dhabal, Andressa A. Bertolazzo, Valeria Molinero, and Subramanian K. R. S. Sankaranarayanan

Subjects
Materials of engineering and construction. Mechanics of materials, TA401-492, Computer software, QA76.75-76.765
Abstract

Abstract Silica is an abundant and technologically attractive material. Due to the structural complexities of silica polymorphs coupled with subtle differences in Si–O bonding characteristics, the development of accurate models to predict the structure, energetics and properties of silica polymorphs remain challenging. Current models for silica range from computationally efficient Buckingham formalisms (BKS, CHIK, Soules) to reactive (ReaxFF) and more recent machine-learned potentials that are flexible but computationally costly. Here, we introduce an improved formalism and parameterization of BKS model via a multireward reinforcement learning (RL) using an experimental training dataset. Our model concurrently captures the structure, energetics, density, equation of state, and elastic constants of quartz (equilibrium) as well as 20 other metastable silica polymorphs. We also assess its ability in capturing amorphous properties and highlight the limitations of the BKS-type functional forms in simultaneously capturing crystal and amorphous properties. We demonstrate ways to improve model flexibility and introduce a flexible formalism, machine-learned ML-BKS, that outperforms existing empirical models and is on-par with the recently developed 50 to 100 times more expensive Gaussian approximation potential (GAP) in capturing the experimental structure and properties of silica polymorphs and amorphous silica.

Published
2023
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3. Nanoporous Dielectric Resistive Memories Using Sequential Infiltration Synthesis

Author

Ilke Arslan, Bhaswar Chakrabarti, Suman Datta, A. Khanna, Leonidas E. Ocola, Henry Chan, Benjamin Grisafe, Daniel Rosenmann, Aditya Koneru, Thomas E. Gage, Khan Alam, Supratik Guha, Subramanian K. R. S. Sankaranarayan, Toby Sanders, and Ralu Divan

Subjects
Resistive touchscreen, Materials science, Nanoporous, business.industry, General Engineering, General Physics and Astronomy, 02 engineering and technology, Dielectric, 010402 general chemistry, 021001 nanoscience & nanotechnology, 01 natural sciences, 0104 chemical sciences, Resistive random-access memory, Non-volatile memory, Infiltration (hydrology), Neuromorphic engineering, Optoelectronics, General Materials Science, 0210 nano-technology, business
Abstract

Resistance switching in metal-insulator-metal structures has been extensively studied in recent years for use as synaptic elements for neuromorphic computing and as nonvolatile memory elements. However, high switching power requirements, device variabilities, and considerable trade-offs between low operating voltages, high on/off ratios, and low leakage have limited their utility. In this work, we have addressed these issues by demonstrating the use of ultraporous dielectrics as a pathway for high-performance resistive memory devices. Using a modified atomic layer deposition based technique known as sequential infiltration synthesis, which was developed originally for improving polymer properties such as enhanced etch resistance of electron-beam resists and for the creation of films for filtration and oleophilic applications, we are able to create ∼15 nm thick ultraporous (pore size ∼5 nm) oxide dielectrics with up to 73% porosity as the medium for filament formation. We show, using the Ag/Al

Published
2021

4. Effect of graphene oxide (GO) nanosheet sizes, pinhole defects and non-ideal lamellar stacking on the performance of layered GO membranes: an atomistic investigation

Author

Aditya Koneru, K. Anki Reddy, and Abhijit Gogoi

Subjects
Materials science, Graphene, General Engineering, Oxide, Stacking, Bioengineering, General Chemistry, Pinhole, Permeation, Atomic and Molecular Physics, and Optics, law.invention, chemistry.chemical_compound, Membrane, Chemical engineering, chemistry, law, General Materials Science, Lamellar structure, Nanosheet
Abstract

The effect of non-idealities, namely pinhole defects and non-ideal lamellar stacking of nanosheets, on the performance of size-differentiated graphene oxide (GO) laminates is investigated using equilibrium molecular dynamics (MD) simulations. With the increase in sizes of the constituent GO nanosheets the water permeability of the layered GO membranes decreases and salt rejection increases. But with the inclusion of non-idealities the difference in water permeability between these membranes substantially reduced. The pinholes on the GO nanosheets provide shorter routes for trans-sheet flow, thereby increasing the water permeability of the membranes. The non-ideal stacking of the nanosheets without pinhole defects results in slight reduction in water permeability because of blockage of permeation pathways inside the membranes. However, with pinhole defects non-ideal stacking becomes favorable for water permeation through the layered GO membranes; as this time the non-ideal stacking leads to formation of voids inside the membranes, which act as routes for shorter permeation pathways. The effect of these non-idealities is more significant for layered GO membranes composed of large GO nanosheets. Although the water permeability through the layered GO membrane is greatly enhanced because of these non-idealities (about 10 times), the corresponding variation in the salt rejection is very low (

Published
2019

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