John E. Pask

Lithium Ion Solvation and Diffusion in Bulk Organic Electrolytes from First-Principles and Classical Reactive Molecular Dynamics

Lithium-ion battery performance is strongly influenced by the ionic conductivity of the electrolyte, which depends on the speed at which Li ions migrate across the cell and relates to their solvation structure. The choice of solvent can greatly impact both the solvation and diffusivity of Li ions. In this work, we used first-principles molecular dynamics to examine the solvation and diffusion of Li ions in the bulk organic solvents ethylene carbonate (EC), ethyl methyl carbonate (EMC), and a mixture of EC and EMC. We found that Li ions are solvated by either carbonyl or ether oxygen atoms of the solvents and sometimes by the PF6(-) anion. Li(+) prefers a tetrahedrally coordinated first solvation shell regardless of which species are involved, with the specific preferred solvation structure dependent on the organic solvent. In addition, we calculated Li diffusion coefficients in each electrolyte, finding slightly larger diffusivities in the linear carbonate EMC compared to the cyclic carbonate EC. The magnitude of the diffusion coefficient correlates with the strength of Li(+) solvation. Corresponding analysis for the PF6(-) anion shows greater diffusivity associated with a weakly bound, poorly defined first solvation shell. These results can be used to aid in the design of new electrolytes to improve Li-ion battery performance.

Half-Metallic Digital Ferromagnetic Heterostructure Composed of a delta-Doped Layer of Mn in Si

We propose and investigate the properties of a digital ferromagnetic heterostructure consisting of a delta-doped layer of Mn in Si, using ab initio electronic-structure methods. We find that (i) ferromagnetic order of the Mn layer is energetically favorable relative to antiferromagnetic, and (ii) the heterostructure is a two-dimensional half-metallic system. The metallic behavior is contributed by three majority-spin bands originating from hybridized Mn-d and nearest-neighbor Si-p states, and the corresponding carriers are responsible for the ferromagnetic order in the Mn layer. The minority-spin channel has a calculated semiconducting gap of 0.25 eV. The band lineup is found to be favorable for retaining the half-metal character to near the Curie temperature. This kind of heterostructure may be of special interest for integration into mature Si technologies for spintronic applications.

Electronic and magnetic properties of zinc blende half-metal superlattices

Zinc blende half-metallic compounds such as CrAs, with large magnetic moments and high Curie temperatures, are promising materials for spintronic applications. We explore layered materials, consisting of alternating layers of zinc blende half-metals, by first principles calculations, and find that superlattices of (CrAs)1(MnAs)1 and (CrAs)2(MnAs)2 are half-metallic with magnetic moments of 7.0μB and 14.0μB per unit cell, respectively. We discuss the nature of the bonding and half-metallicity in these materials and, based on the understanding acquired, develop a simple expression for the magnetic moment in such materials. We explore the range of lattice constants over which half-metallicity is manifested, and suggest corresponding substrates for growth in thin film form.

Anderson acceleration of the Jacobi iterative method

We employ Anderson extrapolation to accelerate the classical Jacobi iterative method for large, sparse linear systems. Specifically, we utilize extrapolation at periodic intervals within the Jacobi iteration to develop the Alternating Anderson-Jacobi (AAJ) method. We verify the accuracy and efficacy of AAJ in a range of test cases, including nonsymmetric systems of equations. We demonstrate that AAJ possesses a favorable scaling with system size that is accompanied by a small prefactor, even in the absence of a preconditioner. In particular, we show that AAJ is able to accelerate the classical Jacobi iteration by over four orders of magnitude, with speed-ups that increase as the system gets larger. Moreover, we find that AAJ significantly outperforms the Generalized Minimal Residual (GMRES) method in the range of problems considered here, with the relative performance again improving with size of the system. Overall, the proposed method represents a simple yet efficient technique that is particularly attractive for large-scale parallel solutions of linear systems of equations.

Periodic Pulay method for robust and efficient convergence acceleration of self-consistent field iterations

Abstract Pulays Direct Inversion in the Iterative Subspace (DIIS) method is one of the most widely used mixing schemes for accelerating the self-consistent solution of electronic structure problems. In this work, we propose a simple generalization of DIIS in which Pulay extrapolation is performed at periodic intervals rather than on every self-consistent field iteration, and linear mixing is performed on all other iterations. We demonstrate through numerical tests on a wide variety of materials systems in the framework of density functional theory that the proposed generalization of Pulays method significantly improves its robustness and efficiency.

Adaptive local basis set for Kohn–Sham density functional theory in a discontinuous Galerkin framework II: Force, vibration, and molecular dynamics calculations

Recently, we have proposed the adaptive local basis set for electronic structure calculations based on Kohn-Sham density functional theory in a pseudopotential framework. The adaptive local basis set is efficient and systematically improvable for total energy calculations. In this paper, we present the calculation of atomic forces, which can be used for a range of applications such as geometry optimization and molecular dynamics simulation. We demonstrate that, under mild assumptions, the computation of atomic forces can scale nearly linearly with the number of atoms in the system using the adaptive local basis set. We quantify the accuracy of the Hellmann-Feynman forces for a range of physical systems, benchmarked against converged planewave calculations, and find that the adaptive local basis set is efficient for both force and energy calculations, requiring at most a few tens of basis functions per atom to attain accuracy required in practice. Since the adaptive local basis set has implicit dependence on atomic positions, Pulay forces are in general nonzero. However, we find that the Pulay force is numerically small and systematically decreasing with increasing basis completeness, so that the Hellmann-Feynman force is sufficient for basis sizes of a few tens of basis functions per atom. We verify the accuracy of the computed forces in static calculations of quasi-1D and 3D disordered Si systems, vibration calculation of a quasi-1D Si system, and molecular dynamics calculations of H

Chebyshev polynomial filtered subspace iteration in the discontinuous Galerkin method for large-scale electronic structure calculations

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A projected preconditioned conjugate gradient algorithm for computing many extreme eigenpairs of a Hermitian matrix

and liquid Al-Si alloy systems, where we find excellent agreement with independent benchmark results in literature.

Spectral Quadrature method for accurate O(N) electronic structure calculations of metals and insulators

The Discontinuous Galerkin (DG) electronic structure method employs an adaptive local basis (ALB) set to solve the Kohn-Sham equations of density functional theory in a discontinuous Galerkin framework. The adaptive local basis is generated on-the-fly to capture the local material physics and can systematically attain chemical accuracy with only a few tens of degrees of freedom per atom. A central issue for large-scale calculations, however, is the computation of the electron density (and subsequently, ground state properties) from the discretized Hamiltonian in an efficient and scalable manner. We show in this work how Chebyshev polynomial filtered subspace iteration (CheFSI) can be used to address this issue and push the envelope in large-scale materials simulations in a discontinuous Galerkin framework. We describe how the subspace filtering steps can be performed in an efficient and scalable manner using a two-dimensional parallelization scheme, thanks to the orthogonality of the DG basis set and block-sparse structure of the DG Hamiltonian matrix. The on-the-fly nature of the ALB functions requires additional care in carrying out the subspace iterations. We demonstrate the parallel scalability of the DG-CheFSI approach in calculations of large-scale two-dimensional graphene sheets and bulk three-dimensional lithium-ion electrolyte systems. Employing 55 296 computational cores, the time per self-consistent field iteration for a sample of the bulk 3D electrolyte containing 8586 atoms is 90 s, and the time for a graphene sheet containing 11 520 atoms is 75 s.

SQDFT: Spectral Quadrature method for large-scale parallel O(N) Kohn-Sham calculations at high temperature

We present an iterative algorithm for computing an invariant subspace associated with the algebraically smallest eigenvalues of a large sparse or structured Hermitian matrix A. We are interested in the case in which the dimension of the invariant subspace is large (e.g., over several hundreds or thousands) even though it may still be small relative to the dimension of A. These problems arise from, for example, density functional theory (DFT) based electronic structure calculations for complex materials. The key feature of our algorithm is that it performs fewer Rayleigh-Ritz calculations compared to existing algorithms such as the locally optimal block preconditioned conjugate gradient or the Davidson algorithm. It is a block algorithm, and hence can take advantage of efficient BLAS3 operations and be implemented with multiple levels of concurrency. We discuss a number of practical issues that must be addressed in order to implement the algorithm efficiently on a high performance computer.