A First Principles Theory of Nuclear Magnetic Resonance J-Coupling in solid-state systems

A method to calculate NMR J-coupling constants from first principles in extended systems is presented. It is based on density functional theory and is formulated within a planewave-pseudopotential framework. The all-electron properties are recovered using the projector augmented wave approach. The method is validated by comparison with existing quantum chemical calculations of solution-state systems and with experimental data. The approach has been applied to verify measured J-coupling in a silicophosphate structure, Si5O(PO4)6Comment: 9 page

Nonlocal pseudopotentials and magnetic fields

We show how to describe the coupling of electrons to non-uniform magnetic fields in the framework of the widely used norm-conserving pseudopotential appro ximation for electronic structure calculations. Our derivation applies to magnetic fields that are smooth on the scale of the core region. The method is validated by application to the calculation of the magnetic susceptibility of molecules. Our results are compared with high quality all electron quantum chemical results, and another recently proposed formalism.Comment: 4 pages, submitted to Physical Review Letter

An efficient k.p method for calculation of total energy and electronic density of states

An efficient method for calculating the electronic structure in large systems with a fully converged BZ sampling is presented. The method is based on a k.p-like approximation developed in the framework of the density functional perturbation theory. The reliability and efficiency of the method are demostrated in test calculations on Ar and Si supercells

Ab Initio Quality NMR Parameters in Solid-State Materials Using a High-Dimensional Neural-Network Representation.

Nuclear magnetic resonance (NMR) spectroscopy is one of the most powerful experimental tools to probe the local atomic order of a wide range of solid-state compounds. However, due to the complexity of the related spectra, in particular for amorphous materials, their interpretation in terms of structural information is often challenging. These difficulties can be overcome by combining molecular dynamics simulations to generate realistic structural models with an ab initio evaluation of the corresponding chemical shift and quadrupolar coupling tensors. However, due to computational constraints, this approach is limited to relatively small system sizes which, for amorphous materials, prevents an adequate statistical sampling of the distribution of the local environments that is required to quantitatively describe the system. In this work, we present an approach to efficiently and accurately predict the NMR parameters of very large systems. This is achieved by using a high-dimensional neural-network representation of NMR parameters that are calculated using an ab initio formalism. To illustrate the potential of this approach, we applied this neural-network NMR (NN-NMR) method on the (17)O and (29)Si quadrupolar coupling and chemical shift parameters of various crystalline silica polymorphs and silica glasses. This approach is, in principal, general and has the potential to be applied to predict the NMR properties of various materials.This is the author accepted manuscript. The final version is available from ACS via http://dx.doi.org/10.1021/acs.jctc.5b0100

Generalized convex hull construction for materials discovery

High-throughput computational materials searches generate large databases of locally-stable structures. Conventionally, the needle-in-a-haystack search for the few experimentally-synthesizable compounds is performed using a convex hull construction, which identifies structures stabilized by manipulation of a particular thermodynamic constraint (for example pressure or composition) chosen based on prior experimental evidence or intuition. To address the biased nature of this procedure we introduce a generalized convex hull framework. Convex hulls are constructed on data-driven principal coordinates, which represent the full structural diversity of the database. Their coupling to experimentally-realizable constraints hints at the conditions that are most likely to stabilize a given configuration. The probabilistic nature of our framework also addresses the uncertainty stemming from the use of approximate models during database construction, and eliminates redundant structures. The remaining small set of candidates that have a high probability of being synthesizable provide a much needed starting point for the determination of viable synthetic pathways.Comment: Accepted Manuscrip

Ab initio Random Structure Searching

It is essential to know the arrangement of the atoms in a material in order to compute and understand its properties. Searching for stable structures of materials using first-principles electronic structure methods, such as density functional theory (DFT), is a rapidly growing field. Here we describe our simple, elegant and powerful approach to searching for structures with DFT which we call ab initio random structure searching (AIRSS). Applications to discovering structures of solids, point defects, surfaces, and clusters are reviewed. New results for iron clusters on graphene, silicon clusters, polymeric nitrogen, hydrogen-rich lithium hydrides, and boron are presented.Comment: 44 pages, 23 figure

Electron spectroscopy of carbon materials: Experiment and theory

We present a comparative spectroscopic study of carbon as graphite, diamond and C60 using C1s K-edge electron energy-loss spectroscopy (EELS), X-ray emission spectroscopy, and theoretical modelling. The first principles calculations of these spectra are obtained in the local density approximation using a self-consistent Gaussian basis pseudo-potential method. Calculated spectra show excellent agreement with experiment and are able to discriminate not only between various carbon hybridisations but also local variation in environment. Core-hole effects on the calculated spectra are also investigated. For the first time, the EEL spectrum of carbyne is calculated

Hydrogen/silicon complexes in silicon from computational searches

Defects in crystalline silicon consisting of a silicon self-interstitial atom and one, two, three, or four hydrogen atoms are studied within density-functional theory (DFT). We search for low-energy defects by starting from an ensemble of structures in which the atomic positions in the defect region have been randomized. We then relax each structure to a minimum in the energy. We find a new defect consisting of a self-interstitial and one hydrogen atom (denoted by {I,H}) which has a higher symmetry and a lower energy than previously reported structures. We recover the {I,H_2} defect found in previous studies and confirm that it is the most stable such defect. Our best {I,H_3} defect has a slightly different structure and lower energy than the one previously reported, and our lowest energy {I,H_4} defect is different to those of previous studies.Comment: 7 pages, 8 figure