We introduce the notion of electronic enthalpy for first-principles structural and dynamical calculations of finite systems under pressure. An external pressure field is allowed to act directly on the electronic structure of the system studied via the ground-state minimization of the functional E+PV(q), where V(q) is the quantum volume enclosed by a charge isosurface. The Hellmann-Feynman theorem applies, and assures that the ionic equations of motion follow an isoenthalpic dynamics. No pressurizing medium is explicitly required, while coatings of environmental ions or ligands can be introduced if chemically relevant. We apply this novel approach to the study of group-IV nanoparticles during a shock wave, highlighting the significant differences in the plastic or elastic response of the diamond cage under load, and their potential use as novel nanostructured impact-absorbing materials.

Ab initio molecular dynamics in the generalized gradient approximation to density functional theory and ground-state relaxations are used to study the interaction between molecular hydrogen and the metal-organic framework with formula unit Zn4O(O2C-C6H4-CO2)3. Five symmetrically unique adsorption sites are identified, and calculations indicate that the sites with the strongest interaction with hydrogen are located near the Zn4O clusters. Twenty total adsorption sites are found around each Zn4O cluster, but after 16 of these are populated, the interaction energy at the remaining four sites falls off significantly. The adsorption of hydrogen on the pore walls creates an attractive potential well for hydrogen in the center of the pore. The effect of the framework on the physical structure and electronic structure of the organic linker is calculated, suggesting ways by which the interaction between the framework and hydrogen could be modified.

The ability to predict the crystal structure of a material, given its constituent atoms, is one of the most fundamental problems in materials research. There exist a number of empirical methods which make predictions by clustering existing experimental data, generally using a few simple physical parameters. Although Pettifor maps are perhaps the best known and most successful of these empirical methods, the implementation and assessment of Pettifor maps has not been formalized. Here we propose well-defined algorithms for transforming data from a standard materials crystal structure database into a Pettifor map, using the map to predict the crystal structure for a new system, and assessing the predictive accuracy of the map. We introduce the idea of a candidate crystal structure list, demonstrating that by predicting more than one candidate for a new system the utility of the maps can be enhanced. We assess the accuracy of the maps by testing predictive accuracy using a cross-validation technique on all AB and A3B compounds in the CRYSTMET database. We show that for a new unknown alloy with a stable structure at the stoichiometry of the Pettifor map, a candidate list of five structures will contain the correct crystal structure for the alloy 86% of the time. The algorithms presented here can be used to automate Pettifor maps in materials crystal structure databases, making it possible for users to construct, apply and assess entirely new Pettifor maps quickly and easily.

We show that cluster expansions (CE), previously used to model solid-state materials with binary or ternary configurational disorder, can be extended to the protein design problem. We present a generalized CE framework, in which properties such as energy can be unambiguously expanded in the amino-acid sequence space. The CE coarse grains over nonsequence degrees of freedom (e.g., side-chain conformations) and thereby simplifies the problem of designing proteins, or predicting the compatibility of a sequence with a given structure, by many orders of magnitude. The CE is physically transparent, and can be evaluated through linear regression on the energies of training sequences. We show, as example, that good prediction accuracy is obtained with up to pairwise interactions for a coiled-coil backbone, and that triplet interactions are important in the energetics of a more globular zinc-finger backbone.

The local environment and short-range ordering of Li(Ni0.5Mn0.5)O-2, a potential Li-ion battery positive electrode material obtained via an ion-exchange route from Na(Ni0.5Mn0.5)O-2, were investigated by using a combination of Li-6 Magic Angle Spinning (MAS) NMR spectroscopy and neutron Pair Distribution Function (PDF) analysis, associated with Reverse Monte Carlo (RMC) calculations. Li-6 MAS NMR experiments on Li(Ni0.5Mn0.5)O-2 showed that there are almost no Li ions in the transition metal layers. Neutron diffraction data for the precursor Na(Ni0.5Mn0.5)O-2 indicated that there is no Na/ Ni disorder and that the material is perfectly layered. Neutron PDF analysis of Li(Ni0.5Mn0.5)O-2 and Na(Ni0.5Mn0.5)O-2 revealed differences in the local transition metal arrangements between those present in the ion-exchanged material and its precursor, and those found in the cathode material synthesized directly from hydroxide starting materials. Large clusters of 3456 atoms were built to investigate cation ordering. Reverse Monte Carlo results, for both the Na and Li-containing compounds, showed a non- random distribution of Ni and Mn cations in the transition metal layers: in the first coordination shell, Ni atoms are on average close to more Mn ions than predicted based on a random distribution of these ions in the transition metal layers. Analysis of the number of Ni/Ni, Mn/Mn and Ni/Mn pairs in the second coordination shell revealed that the Ni and Mn cations show a clear preference for ordering in zigzags rather than in chains.

10.1149/1.2509021

Using first-principles density functional theory, we study the effect of particle size and surface structure on the chemisorption energy of OH and O on nanoparticles of Pt. We find that the chemisorption energies of O and OH are strongly affected by the size and structure of the Pt particle varying by up to $1.0\phantom{\rule{0.3em}{0ex}}\mathrm{eV}$ at different adsorption sites and particle sizes.

LiCoO₂thin films have been grown by pulsed laser deposition on stainless steel and SiO₂/Si substrates. The film deposited at 600°C in an oxygen partial pressure of 100mTorr shows an excellent crystallinity, stoichiometry and no impurity phase present. Microstructure and surface morphology of thin films were examined using a scanning electron microscope. The electrochemical properties of the thin films were studied with cyclic voltammetry and galvanostatic charge-discharge techniques in the potential range 3.0-4.2 V. The initial discharge capacity of the LiCoO2 thin films deposited on the stainless steel and SiO₂/Si substrates reached 23 and 27 µAh/cm², respectively.

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.