Machine learning based prediction of the electronic structure of quasi-one-dimensional materials under strain

• URL: http://arxiv.org/abs/2202.00930v3
• Date: Mon, 25 Apr 2022 18:14:01 GMT
• Title: Machine learning based prediction of the electronic structure of quasi-one-dimensional materials under strain
• Authors: Shashank Pathrudkar, Hsuan Ming Yu, Susanta Ghosh and Amartya S. Banerjee
• Abstract summary: We present a machine learning based model that can predict the electronic structure of quasi-one-dimensional materials. This technique applies to important classes of materials such as nanotubes, nanoribbons, nanowires and nano-assemblies.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a machine learning based model that can predict the electronic structure of quasi-one-dimensional materials while they are subjected to deformation modes such as torsion and extension/compression. The technique described here applies to important classes of materials such as nanotubes, nanoribbons, nanowires, miscellaneous chiral structures and nano-assemblies, for all of which, tuning the interplay of mechanical deformations and electronic fields is an active area of investigation in the literature. Our model incorporates global structural symmetries and atomic relaxation effects, benefits from the use of helical coordinates to specify the electronic fields, and makes use of a specialized data generation process that solves the symmetry-adapted equations of Kohn-Sham Density Functional Theory in these coordinates. Using armchair single wall carbon nanotubes as a prototypical example, we demonstrate the use of the model to predict the fields associated with the ground state electron density and the nuclear pseudocharges, when three parameters - namely, the radius of the nanotube, its axial stretch, and the twist per unit length - are specified as inputs. Other electronic properties of interest, including the ground state electronic free energy, can then be evaluated with low-overhead post-processing, typically to chemical accuracy. We also show how the nuclear coordinates can be reliably determined from the pseudocharge field using a clustering based technique. Remarkably, only about 120 data points are found to be enough to predict the three dimensional electronic fields accurately, which we ascribe to the symmetry in the problem setup, the use of low-discrepancy sequences for sampling, and presence of intrinsic low-dimensional features in the electronic fields. We comment on the interpretability of our machine learning model and discuss its possible future applications.

Related papers

• Predicting ionic conductivity in solids from the machine-learned potential energy landscape [68.25662704255433]
  Superionic materials are essential for advancing solid-state batteries, which offer improved energy density and safety. Conventional computational methods for identifying such materials are resource-intensive and not easily scalable. We propose an approach for the quick and reliable evaluation of ionic conductivity through the analysis of a universal interatomic potential.
  arXiv  Detail & Related papers  (2024-11-11T09:01:36Z)
• Electronic Correlations in Multielectron Silicon Quantum Dots [0.3793387630509845]
  Silicon metal-oxide-semiconductor based quantum dots present a promising pathway for realizing practical quantum computers. Hartree-Fock theory is an imperative tool for the electronic structure modelling of multi-electron quantum dots. We present a Hartree-Fock-based method that accounts for these complexities for the modelling of silicon quantum dots.
  arXiv  Detail & Related papers  (2024-07-05T06:46:38Z)
• Nanoscale single-electron box with a floating lead for quantum sensing: modelling and device characterization [0.0]
  We present an in-depth analysis of a single-electron box (SEB) biased through a floating node technique. The device is analyzed and characterized in the context of single-electron charge-sensing techniques for integrated silicon quantum dots (QD)
  arXiv  Detail & Related papers  (2024-04-23T13:05:36Z)
• Implementation and characterization of the dice lattice in the electron quantum simulator [0.0]
  We study the experimental realization of the dice lattice with adjustable parameters. The high mobility of Shockley state electrons enables an accurate theoretical description of the artificial lattice. Our theoretical findings suggest that, owing to the exceptional electron mobility, the highly degenerate eigenenergy associated with the Aharonov-Bohm caging mechanism may not manifest in the proposed experiment.
  arXiv  Detail & Related papers  (2024-03-09T23:27:19Z)
• On the importance of catalyst-adsorbate 3D interactions for relaxed energy predictions [98.70797778496366]
  We investigate whether it is possible to predict a system's relaxed energy in the OC20 dataset while ignoring the relative position of the adsorbate. We find that while removing binding site information impairs accuracy as expected, modified models are able to predict relaxed energies with remarkably decent MAE.
  arXiv  Detail & Related papers  (2023-10-10T14:57:04Z)
• Molecular Geometry-aware Transformer for accurate 3D Atomic System modeling [51.83761266429285]
  We propose a novel Transformer architecture that takes nodes (atoms) and edges (bonds and nonbonding atom pairs) as inputs and models the interactions among them. Moleformer achieves state-of-the-art on the initial state to relaxed energy prediction of OC20 and is very competitive in QM9 on predicting quantum chemical properties.
  arXiv  Detail & Related papers  (2023-02-02T03:49:57Z)
• Multi-task learning for electronic structure to predict and explore molecular potential energy surfaces [39.228041052681526]
  We refine the OrbNet model to accurately predict energy, forces, and other response properties for molecules. The model is end-to-end differentiable due to the derivation of analytic gradients for all electronic structure terms. It is shown to be transferable across chemical space due to the use of domain-specific features.
  arXiv  Detail & Related papers  (2020-11-05T06:48:46Z)
• A multiconfigurational study of the negatively charged nitrogen-vacancy center in diamond [55.58269472099399]
  Deep defects in wide band gap semiconductors have emerged as leading qubit candidates for realizing quantum sensing and information applications. Here we show that unlike single-particle treatments, the multiconfigurational quantum chemistry methods, traditionally reserved for atoms/molecules, accurately describe the many-body characteristics of the electronic states of these defect centers.
  arXiv  Detail & Related papers  (2020-08-24T01:49:54Z)
• State preparation and measurement in a quantum simulation of the O(3) sigma model [65.01359242860215]
  We show that fixed points of the non-linear O(3) sigma model can be reproduced near a quantum phase transition of a spin model with just two qubits per lattice site. We apply Trotter methods to obtain results for the complexity of adiabatic ground state preparation in both the weak-coupling and quantum-critical regimes. We present and analyze a quantum algorithm based on non-unitary randomized simulation methods.
  arXiv  Detail & Related papers  (2020-06-28T23:44:12Z)
• Graph Neural Network for Hamiltonian-Based Material Property Prediction [56.94118357003096]
  We present and compare several different graph convolution networks that are able to predict the band gap for inorganic materials. The models are developed to incorporate two different features: the information of each orbital itself and the interaction between each other. The results show that our model can get a promising prediction accuracy with cross-validation.
  arXiv  Detail & Related papers  (2020-05-27T13:32:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.