Spontaneous spatial fractals: universal contexts and applications

We report on our latest research in the field of spontaneous spatial fractal patterns. New analyses, results and potential applications are reported for nonlinear ring cavities and kaleidoscope laser systems

Computational materials design of crystalline solids

The modelling of materials properties and processes from first principles is becoming sufficiently accurate as to facilitate the design and testing of new systems in silico. Computational materials science is both valuable and increasingly necessary for developing novel functional materials and composites that meet the requirements of next-generation technology. A range of simulation techniques are being developed and applied to problems related to materials for energy generation, storage and conversion including solar cells, nuclear reactors, batteries, fuel cells, and catalytic systems. Such techniques may combine crystal-structure prediction (global optimisation), data mining (materials informatics) and high-throughput screening with elements of machine learning. We explore the development process associated with computational materials design, from setting the requirements and descriptors to the development and testing of new materials. As a case study, we critically review progress in the fields of thermoelectrics and photovoltaics, including the simulation of lattice thermal conductivity and the search for Pb-free hybrid halide perovskites. Finally, a number of universal chemical-design principles are advanced

Research Update: Relativistic origin of slow electron-hole recombination in hybrid halide perovskite solar cells

The hybrid perovskite CH3NH3PbI3 (MAPI) exhibits long minority-carrier lifetimes and diffusion lengths. We show that slow recombination originates from a spin-split indirect-gap. Large internal electric fields act on spin-orbit-coupled band extrema, shifting band-edges to inequivalent wavevectors, making the fundamental gap indirect. From a description of photoluminescence within the quasiparticle self-consistent GW approximation for MAPI, CdTe, and GaAs, we predict carrier lifetime as a function of light intensity and temperature. At operating conditions we find radiative recombination in MAPI is reduced by a factor of more than 350 compared to direct gap behavior. The indirect gap is retained with dynamic disorder

Ionic transport in hybrid lead iodide perovskite solar cells

Solar cells based on organic–inorganic halide perovskites have recently shown rapidly rising power conversion efficiencies, but exhibit unusual behaviour such as current–voltage hysteresis and a low-frequency giant dielectric response. Ionic transport has been suggested to be an important factor contributing to these effects; however, the chemical origin of this transport and the mobile species are unclear. Here, the activation energies for ionic migration in methylammonium lead iodide (CH3NH3PbI3) are derived from first principles, and are compared with kinetic data extracted from the current–voltage response of a perovskite-based solar cell. We identify the microscopic transport mechanisms, and find facile vacancy-assisted migration of iodide ions with an activation energy of 0.6 eV, in good agreement with the kinetic measurements. The results of this combined computational and experimental study suggest that hybrid halide perovskites are mixed ionic–electronic conductors, a finding that has major implications for solar cell device architectures

Chemical Trends in the Lattice Thermal Conductivity of Li(Ni, Mn, Co)O₂ (NMC) Battery Cathodes

While the transport of ions and electrons in conventional Li-ion battery cathode materials is well understood, our knowledge of the phonon (heat) transport is still in its infancy. We present a first-principles theoretical investigation of the chemical trends in the phonon frequency dispersion, mode lifetimes, and thermal conductivity in the series of layered lithium transition-metal oxides Li(NixMnyCoz)O2 (x + y + z = 1). The oxidation and spin states of the transition metal cations are found to strongly influence the structural dynamics. Calculations of the thermal conductivity show that LiCoO2 has highest average conductivity of 45.9 W·m–1·K–1 at T = 300 K and the largest anisotropy, followed by LiMnO2 with 8.9 W·m–1·K–1 and LiNiO2 with 6.0 W·m–1·K–1. The much lower thermal conductivity of LiMnO2 and LiNiO2 is found to be due to 1–2 orders of magnitude shorter phonon lifetimes. We further model the properties of binary and ternary transition metal combinations to examine the possible effects of mixing on the thermal transport. These results serve as a guide to ongoing work on the design of multicomponent battery electrodes with more effective thermal management

Ab initio and homology based prediction of protein domains by recursive neural networks

Background: Proteins, especially larger ones, are often composed of individual evolutionary units, domains, which have their own function and structural fold. Predicting domains is an important intermediate step in protein analyses, including the prediction of protein structures. Results: We describe novel systems for the prediction of protein domain boundaries powered by Recursive Neural Networks. The systems rely on a combination of primary sequence and evolutionary information, predictions of structural features such as secondary structure, solvent accessibility and residue contact maps, and structural templates, both annotated for domains (from the SCOP dataset) and unannotated (from the PDB). We gauge the contribution of contact maps, and PDB and SCOP templates independently and for different ranges of template quality. We find that accurately predicted contact maps are informative for the prediction of domain boundaries, while the same is not true for contact maps predicted ab initio. We also find that gap information from PDB templates is informative, but, not surprisingly, less than SCOP annotations. We test both systems trained on templates of all qualities, and systems trained only on templates of marginal similarity to the query (less than 25% sequence identity). While the first batch of systems produces near perfect predictions in the presence of fair to good templates, the second batch outperforms or match ab initio predictors down to essentially any level of template quality. We test all systems in 5-fold cross-validation on a large non-redundant set of multi-domain and single domain proteins. The final predictors are state-of-the-art, with a template-less prediction boundary recall of 50.8% (precision 38.7%) within ± 20 residues and a single domain recall of 80.3% (precision 78.1%). The SCOP-based predictors achieve a boundary recall of 74% (precision 77.1%) again within ± 20 residues, and classify single domain proteins as such in over 85% of cases, when we allow a mix of bad and good quality templates. If we only allow marginal templates (max 25% sequence identity to the query) the scores remain high, with boundary recall and precision of 59% and 66.3%, and 80% of all single domain proteins predicted correctly. Conclusion: The systems presented here may prove useful in large-scale annotation of protein domains in proteins of unknown structure. The methods are available as public web servers at the address: http://distill.ucd.ie/shandy/ and we plan on running them on a multi-genomic scale and make the results public in the near future.Science Foundation IrelandHealth Research BoardUCD President's Award 2004au, da, sp, ke, ab - kpw2/12/1