All-solid-state batteries utilizing a ceramic instead of an organic liquid as an electrolyte have the potential to be safer and more energy dense than traditional rechargeable lithium-ion batteries. This emergent energy-storage technology, however, is still critically limited by the performance of the solid electrolyte and its interface with electrodes. Here, we present a review of recent efforts in predictive modeling and materials design for lithium and sodium solid electrolytes using advanced computational approaches. These approaches have enabled the efficient design and discovery of new functional materials with desired properties, such as high alkali ionic conductivity, good phase and electrochemical stability, and low cost, accelerating the development of all-solid-state alkali batteries.

The idea of first-principles methods is to determine the properties of materials by solving the basic equations of quantum mechanics and statistical mechanics. With such an approach, one can, in principle, predict the behavior of novel materials without the need to synthesize them and create a virtual design laboratory. By showing several examples of new electrode materials that have been computationally designed, synthesized, and tested, the impact of first-principles methods in the field of Li battery electrode materials will be demonstrated. A significant advantage of computational property prediction is its scalability, which is currently being implemented into the Materials Genome Project at the Massachusetts Institute of Technology. Using a high-throughput computational environment, coupled to a database of all known inorganic materials, basic information on all known inorganic materials and a large number of novel “designed” materials is being computed. Scalability of high-throughput computing can easily be extended to reach across the complete universe of inorganic compounds, although challenges need to be overcome to further enable the impact of first-principles methods.

The development of modern materials science has led to a growing need to understand the phenomena determining the properties of materials and processes on an atomistic level. The interactions between atoms and electrons are governed by the laws of quantum mechanics; hence, accurate and efficient techniques for solving the basic quantum-mechanical equations for complex many-atom, many-electron systems must be developed. Density functional theory (DFT) marks a decisive breakthrough in these efforts, and in the past decade DFT has had a rapidly growing impact not only on fundamental but also industrial research. This article discusses the fundamental principles of DFT and the highly efficient computational tools that have been developed for its application to complex problems in materials science. Also highlighted are state-of-the-art applications in many areas of materials research, such as structural materials, catalysis and surface science, nanomaterials, and biomaterials and geophysics.

Predicting crystal structure is one of the most fundamental problems in materials science and a key early step in computational materials design. Ab initio simulation methods are a powerful tool for predicting crystal structure, but are too slow to explore the extremely large space of possible structures for new alloys. Here we describe ongoing work on a novel method (Data Mining of Quantum Calculations, or DMQC) that applies data mining techniques to existing ab initio data in order to increase the efficiency of crystal structure prediction for new alloys. We find about a factor of three speedup in ab intio prediction of crystal structures using DMQC as compared to naïve random guessing. This study represents an extension of work done by Curtarolo, et al. [1] to a larger library of data.

Computational modeling is playing an increasingly important role in materials research and design. At the system level, the impact of cell design, electrode thickness, electrode morphology, new packaging techniques, and numerous other factors on battery performance can be predicted with battery simulators based on complex electrochemical transport equations. Such simulation tools have allowed the battery industry to optimize the power and energy density that can be achieved with a given set of electrode and electrolyte materials. At the materials level, first-principles calculations, which can be used to predict properties of previously unknown materials ab initio, have now made it possible to design materials for higher capacity and better stability. The state of the art in computational modeling of rechargeable batteries is reviewed.

A detailed analysis of the formation energies of transition metal hydrides is presented. The hydriding energies are computed for various crystal structures using Density Functional Theory. The process of hydride formation is broken down into three consecutive, hypothetical reactions in order to analyse the different energy contributions, and explain the observed trends. We find that the stability of the host metal is very significant in determining the formation energy, thereby providing a more fundamental justification for Miedema's “law of inverse stability” [1] (the more stable the metal, the less stable the hydride). The conversion of the host metal to the structure formed by the metal ions in the hydride (fcc in most cases) is only significant for metals with a strong bcc preference such as V and Cr - this lowers the driving force for hydride formation. The final contribution is the chemical bonding between the hydrogen and the metal. This is the only contribution that is negative, and hence favourable to hydride formation. We find that it is dominated by the position of the Fermi level in the host metal.

Descriptions, in which matter can be coarse grained to arbitrary levels, are necessary to study materials phenomenal simultaneously at various length scales. Attempts to do this in the static regime have already been developed. In this work, we present an approach that leads to dynamics for such coarse-grained models. Renormalization group theory is used to create new local potentials between nodes. Assuming that these potentials give an averaged description of node dynamics, we calculate thermal, mechanical and transport properties. If this method can be sufficiently generalized it may form the basis of a Molecular Dynamics method with time and spatial coarse-graining.

Despite its importance as a cathode material in primary alkaline batteries, the structure of γ-MnO2 is still not well determined. Different authors have suggested that a number of different polymorphs, as well as highly disordered phases, may be present in γ-MnO2. The origin of this structural complexity remains largely unexplained. In this paper we use first principles methods to explore the energetics of the MnO2 system. We find a number of low-energy polymorphs with similar energies, suggesting that relatively small changes in the energetics might influence the stable phases. Using nonzero-temperature models we demonstrate that thermal disorder is not the cause of structural disorder in these materials. However, we then show that point (Ruetschi) defects, even in surprisingly low concentrations, have a dramatic effect on the phase stability. We propose that Ruetschi defects may be the key to some of the structural complexity in γ-MnO2, and that any realistic structural study must take them into account.

The B-site cation ordering in Ba(Zn1/3Nb2/3)O3 was studied using a combination of first-principles energy calculations, a cluster expansion technique, and Monte Carlo simulations. Our calculations indicate that the ground state is a 1:2 ordered hexagonal structure, in contrast to x-ray diffraction observations, but consistent with recent Raman work by Kim et al. The order–disorder transition between the 1:2 ordered phase and the cubic perovskite phase is predicted to occur at approximately 2480 K. This prediction indicates that Ba(Zn1/3Nb2/3)O3 in equilibrium should be fully ordered at all practical temperatures. These results indicate that Ba(Zn1/3Nb2/3)O3, previously considered to be disordered, may be ordered on a local scale, consistent with its good microwave characteristics.

The electrochemical properties of the layered intercalation compound LiCoO2 used as a cathode in Li batteries have been investigated extensively in the past 15 years. Despite this research, little is known about the nature and thermodynamic driving forces for the phase transformations that occur as the Li concentration is varied. In this work, the phase diagram of LixCoO2 is calculated from first principles for x ranging from 0 to 1. Our calculations indicate that there is a tendency for Li ordering at x = 1/2 in agreement with experiment [1]. At low Li concentration, we find that a staged compound is stable in which the Li ions selectively segregate to every other Li plane leaving the remaining Li planes vacant. We find that the two phase region observed at high Li concentration is not due to Li ordering and speculate that it is driven by a metal-insulator transition which occurs at concentrations slightly below x < 1.

We have partially solved the ground state problem of binary alloys on the fcc lattice with pair interactions up to the fourth nearest neighbor distance. Our results extend the study presented by Kanamori and Kakehashi [1], releasing the constraint they imposed on the nearest neighbor correlation. The solution we present increases the number of possible ground state structures by an order of magnitude with respect to previous results. We have applied both the polyhedron and the enumeration method. The latter proved more powerful when including interactions beyond the second nearest neighbor distance.

We present a study of the hexagonal close-packed (hcp) Ising model for binary alloys within the cluster variation approximation. Groundstates of order stabilized by nearest-neighbor (NN) pair, triplet, and tetrahedron interactions were determined using the cluster configuration polyhedron method; no previous hcp ground-state study has considered all of these interactions. We predict physically realizable groundstates with stoichiometries A, AB (3 distinct structures), A2B, A3B, and A4B3. The previously unpredicted A4B3 structure is stabilized by multiatom (i.e. triplet, tetrahedron) interactions, while the others are stabilized by the two NN pair interactions. The Cluster Variation Method (CVM) was used to calculate the finite-temperature phase-equilibria for prototypical binary alloys. We present the first ordering phase diagrams computed with the CVM which contain all relevant groundstates for both isotropic and anisotropic NN pair interactions.

The relationship between oxygen structure, charge transfer (hole count), and oxygen content in the YBa2Cu3Oz superconductor is studied by combining Monte-Carlo simulation with existing electronic structure calculations. The present model proposes an expansion of the hole count in terms of oxygen configuration variables for states of an arbitrary degree of order. Calculations of hole count as a function of oxygen content suggest that oxygen ordering is, at least in part, responsible for the observed plateau structure of Tc vs. oxygen content z.