Soft and transferable pseudopotentials from multi-objective optimization

Ab initio pseudopotentials are a linchpin of modern molecular and condensed matter electronic structure calculations. In this work, we employ multi-objective optimization to maximize pseudopotential softness while maintaining high accuracy and transferability. To accomplish this, we develop a formulation in which softness and accuracy are simultaneously maximized, with accuracy determined by the ability to reproduce all-electron energy differences between Bravais lattice structures, whereupon the resulting Pareto frontier is scanned for the softest pseudopotential that provides the desired accuracy in established transferability tests. We employ an evolutionary algorithm to solve the multi-objective optimization problem and apply it to generate a comprehensive table of optimized norm-conserving Vanderbilt (ONCV) pseudopotentials (https://github.com/SPARC-X/SPMS-psps). We show that the resulting table is softer than existing tables of comparable accuracy, while more accurate than tables of comparable softness. The potentials thus afford the possibility to speed up calculations in a broad range of applications areas while maintaining high accuracy.Comment: 13 pages, 4 figure

Kohn-Sham accuracy from orbital-free density functional theory via Δ\Delta-machine learning

We present a $\Delta$-machine learning model for obtaining Kohn-Sham accuracy from orbital-free density functional theory (DFT) calculations. In particular, we employ a machine learned force field (MLFF) scheme based on the kernel method to capture the difference between Kohn-Sham and orbital-free DFT energies/forces. We implement this model in the context of on-the-fly molecular dynamics simulations, and study its accuracy, performance, and sensitivity to parameters for representative systems. We find that the formalism not only improves the accuracy of Thomas-Fermi-von Weizs{\"a}cker (TFW) orbital-free energies and forces by more than two orders of magnitude, but is also more accurate than MLFFs based solely on Kohn-Sham DFT, while being more efficient and less sensitive to model parameters. We apply the framework to study the structure of molten Al$_{0.88}$Si$_{0.12}$, the results suggesting no aggregation of Si atoms, in agreement with a previous Kohn-Sham study performed at an order of magnitude smaller length and time scales.Comment: 10 pages, 7 figures, 2 table

Genome sequence of an Australian kangaroo, Macropus eugenii, provides insight into the evolution of mammalian reproduction and development.

BACKGROUND: We present the genome sequence of the tammar wallaby, Macropus eugenii, which is a member of the kangaroo family and the first representative of the iconic hopping mammals that symbolize Australia to be sequenced. The tammar has many unusual biological characteristics, including the longest period of embryonic diapause of any mammal, extremely synchronized seasonal breeding and prolonged and sophisticated lactation within a well-defined pouch. Like other marsupials, it gives birth to highly altricial young, and has a small number of very large chromosomes, making it a valuable model for genomics, reproduction and development. RESULTS: The genome has been sequenced to 2 × coverage using Sanger sequencing, enhanced with additional next generation sequencing and the integration of extensive physical and linkage maps to build the genome assembly. We also sequenced the tammar transcriptome across many tissues and developmental time points. Our analyses of these data shed light on mammalian reproduction, development and genome evolution: there is innovation in reproductive and lactational genes, rapid evolution of germ cell genes, and incomplete, locus-specific X inactivation. We also observe novel retrotransposons and a highly rearranged major histocompatibility complex, with many class I genes located outside the complex. Novel microRNAs in the tammar HOX clusters uncover new potential mammalian HOX regulatory elements. CONCLUSIONS: Analyses of these resources enhance our understanding of marsupial gene evolution, identify marsupial-specific conserved non-coding elements and critical genes across a range of biological systems, including reproduction, development and immunity, and provide new insight into marsupial and mammalian biology and genome evolution

Phenotyping of field-grown wheat in the UK highlights contribution of light response of photosynthesis and flag leaf longevity to grain yield

Improving photosynthesis is a major target for increasing crop yields and ensuring food security. Phenotyping of photosynthesis in the field is critical to understand the limits to crop performance in agricultural settings. Yet, detailed phenotyping of photosynthetic traits is relatively scarce in field-grown wheat, with previous studies focusing on narrow germplasm selections. Flag leaf photosynthetic traits, crop development, and yield traits were compared in 64 field-grown wheat cultivars in the UK. Pre-anthesis and post-anthesis photosynthetic traits correlated significantly and positively with grain yield and harvest index (HI). These traits included net CO2 assimilation measured at ambient CO2 concentrations and a range of photosynthetic photon flux densities, and traits associated with the light response of photosynthesis. In most cultivars, photosynthesis decreased post-anthesis compared with pre-anthesis, and this was associated with decreased Rubisco activity and abundance. Heritability of photosynthetic traits suggests that phenotypic variation can be used to inform breeding programmes. Specific cultivars were identified with traits relevant to breeding for increased crop yields in the UK: pre-anthesis photosynthesis, post-anthesis photosynthesis, light response of photosynthesis, and Rubisco amounts. The results indicate that flag leaf longevity and operating photosynthetic activity in the canopy can be further exploited to maximize grain filling in UK bread wheat

Abstracts from the NIHR INVOLVE Conference 2017

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Version 2.0.0 -- SPARC: Simulation Package for Ab-initio Real-space Calculations

SPARC is an accurate, efficient, and scalable real-space electronic structure code for performing ab initio Kohn-Sham density functional theory calculations. Version 2.0.0 of the software provides increased efficiency, and includes spin-orbit coupling, dispersion interactions, and advanced semilocal/hybrid exchange-correlation functionals. These new features further expand the range of physical applications amenable to first principles investigation using SPARC.Comment: 10 pages, 2 figure