Phani Motamarri
University of Michigan
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Featured researches published by Phani Motamarri.
Journal of Computational Physics | 2013
Phani Motamarri; Michael R Nowak; Kenneth W. Leiter; Jaroslaw Knap; Vikram Gavini
We present an efficient computational approach to perform real-space electronic structure calculations using an adaptive higher-order finite-element discretization of Kohn-Sham density-functional theory (DFT). To this end, we develop an a priori mesh-adaption technique to construct a close to optimal finite-element discretization of the problem. We further propose an efficient solution strategy for solving the discrete eigenvalue problem by using spectral finite-elements in conjunction with Gauss-Lobatto quadrature, and a Chebyshev acceleration technique for computing the occupied eigenspace. The proposed approach has been observed to provide a staggering 100-200-fold computational advantage over the solution of a generalized eigenvalue problem. Using the proposed solution procedure, we investigate the computational efficiency afforded by higher-order finite-element discretizations of the Kohn-Sham DFT problem. Our studies suggest that staggering computational savings-of the order of 1000-fold-relative to linear finite-elements can be realized, for both all-electron and local pseudopotential calculations, by using higher-order finite-element discretizations. On all the benchmark systems studied, we observe diminishing returns in computational savings beyond the sixth-order for accuracies commensurate with chemical accuracy, suggesting that the hexic spectral-element may be an optimal choice for the finite-element discretization of the Kohn-Sham DFT problem. A comparative study of the computational efficiency of the proposed higher-order finite-element discretizations suggests that the performance of finite-element basis is competing with the plane-wave discretization for non-periodic local pseudopotential calculations, and compares to the Gaussian basis for all-electron calculations to within an order of magnitude. Further, we demonstrate the capability of the proposed approach to compute the electronic structure of a metallic system containing 1688 atoms using modest computational resources, and good scalability of the present implementation up to 192 processors.
Journal of Computational Physics | 2012
Phani Motamarri; Mrinal Iyer; Jaroslaw Knap; Vikram Gavini
In the present work, we study various numerical aspects of higher-order finite-element discretizations of the non-linear saddle-point formulation of orbital-free density-functional theory. We first investigate the robustness of viable solution schemes by analyzing the solvability conditions of the discrete problem. We find that a staggered solution procedure where the potential fields are computed consistently for every trial electron-density is a robust solution procedure for higher-order finite-element discretizations. We next study the convergence properties of higher-order finite-element discretizations of orbital-free density functional theory by considering benchmark problems that include calculations involving both pseudopotential as well as Coulomb singular potential fields. Our numerical studies suggest close to optimal rates of convergence on all benchmark problems for various orders of finite-element approximations considered in the present study. We finally investigate the computational efficiency afforded by various higher-order finite-element discretizations, which constitutes the main aspect of the present work, by measuring the CPU time for the solution of discrete equations on benchmark problems that include large Aluminum clusters. In these studies, we use mesh coarse-graining rates that are derived from error estimates and an a priori knowledge of the asymptotic solution of the far-field electronic fields. Our studies reveal a significant 100-1000 fold computational savings afforded by the use of higher-order finite-element discretization, alongside providing the desired chemical accuracy. We consider this study as a step towards developing a robust and computationally efficient discretization of electronic structure calculations using the finite-element basis.
Physical Review B | 2017
Phani Motamarri; Vikram Gavini; Kaushik Bhattacharya; M. Ortiz
We present a spectrum-splitting approach to conduct all-electron Kohn-Sham density functional theory (DFT) calculations by employing Fermi-operator expansion of the Kohn-Sham Hamiltonian. The proposed approach splits the subspace containing the occupied eigenspace into a core subspace, spanned by the core eigenfunctions, and its complement, the valence subspace, and thereby enables an efficient computation of the Fermi-operator expansion by reducing the expansion to the valence-subspace projected Kohn-Sham Hamiltonian. The key ideas used in our approach are as follows: (i) employ Chebyshev filtering to compute a subspace containing the occupied states followed by a localization procedure to generate nonorthogonal localized functions spanning the Chebyshev-filtered subspace; (ii) compute the Kohn-Sham Hamiltonian projected onto the valence subspace; (iii) employ Fermi-operator expansion in terms of the valence-subspace projected Hamiltonian to compute the density matrix, electron density, and band energy. We demonstrate the accuracy and performance of the method on benchmark materials systems involving silicon nanoclusters up to 1330 electrons, a single gold atom, and a six-atom gold nanocluster. The benchmark studies on silicon nanoclusters revealed a staggering fivefold reduction in the Fermi-operator expansion polynomial degree by using the spectrum-splitting approach for accuracies in the ground-state energies of ∼10^(−4) Ha/atom with respect to reference calculations. Further, numerical investigations on gold suggest that spectrum splitting is indispensable to achieve meaningful accuracies, while employing Fermi-operator expansion.
Physical Review B | 2014
Phani Motamarri; Vikram Gavini
International Journal of Mechanical Sciences | 2012
Phani Motamarri; Srinivasan Suryanarayan
Structural and Multidisciplinary Optimization | 2012
Phani Motamarri; Anand Ramani; Anshul Kaushik
Physical Review B | 2016
Phani Motamarri; Vikram Gavini; Thomas Blesgen
Archive | 2011
Phani Motamarri; Mrinal Iyer; Jaroslaw Knap; Vikram Gavini
Physical Review B | 2018
Phani Motamarri; Vikram Gavini
JOM | 2018
Larry K. Aagesen; J.F. Adams; John E. Allison; W. B. Andrews; Vicente J. Araullo-Peters; T. Berman; Z. Chen; S. Daly; Sambit Das; Stephen DeWitt; S. Ganesan; Krishna Garikipati; Vikram Gavini; A. Githens; Margaret Hedstrom; Z. Huang; H. V. Jagadish; J. W. Jones; J. Luce; Emmanuelle A. Marquis; A. Misra; D. Montiel; Phani Motamarri; A. D. Murphy; Anirudh Raju Natarajan; S. Panwar; Brian Puchala; L. Qi; Siva Shankar Rudraraju; Koki Sagiyama