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

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.

References in corpus (25)

• Maximally-localized generalized Wannier functions for composite energy bands
• Machine Learning for Fluid Mechanics
• All-graphene integrated circuits via strain engineering
• By-passing the Kohn-Sham equations with machine learning
• Accelerating Materials Development via Automation, Machine Learning, and High-Performance Computing
• A Physics Informed Neural Network for Time-Dependent Nonlinear and Higher Order Partial Differential Equations
• A Transferable Machine-Learning Model of the Electron Density
• A Chirality-Based Quantum Leap
• SPARC: Accurate and efficient finite-difference formulation and parallel implementation of Density Functional Theory: Extended systems
• Machine Learning the Physical Non-Local Exchange-Correlation Functional of Density-Functional Theory
• SPARC: Accurate and efficient finite-difference formulation and parallel implementation of Density Functional Theory: Isolated clusters
• Periodic Pulay method for robust and efficient convergence acceleration of self-consistent field iterations
• Quantum deep field: data-driven wave function, electron density generation, and atomization energy prediction and extrapolation with machine learning
• DGDFT: A Massively Parallel Method for Large Scale Density Functional Theory Calculations
• Neural-network Kohn-Sham exchange-correlation potential and its out-of-training transferability
• Predicting charge density distribution of materials using a local-environment-based graph convolutional network
• Deep learning the Hohenberg-Kohn maps of Density Functional Theory
• Accelerating Finite-temperature Kohn-Sham Density Functional Theory with Deep Neural Networks
• Electromechanics of Twisted Graphene Nanoribbons
• Cyclic Density Functional Theory : A route to the first principles simulation of bending in nanostructures
• Symmetry-adapted real-space density functional theory for cylindrical geometries: application to large X (X=C, Si, Ge, Sn) nanotubes
• An Interpretable Machine Learning Model for Deformation of Multi-Walled Carbon Nanotubes
• Electronic and optical trends in carbon nanotubes under pure bending
• Density functional theory method for twisted geometries with application to torsional deformations in group-IV nanotubes
• Ab initio framework for systems with helical symmetry: theory, numerical implementation and applications to torsional deformations in nanostructures