Using the GrassGro Decision Support Tool to Evaluate the Response in Grazing Systems to Pasture Legume or a Grass Cultivar With Improved Nutritive Value

Decision support tools (DST) based on models of grazing systems allow the evaluation of changes in enterprise management on productivity and profitability. The Grassgro DST (Moore et al., 1997) uses historical weather data on a daily time step to simulate pasture growth and the resultant productivity of either grazing sheep or cattle. Different pasture species are represented within a parameter set that describes the response of pasture species to their environment. Manipulation of these parameters provides a means of evaluating, a priori, the likely responses of livestock production to ‘improved cultivars’. We report the results of simulations conducted within grazing enterprises at three locations in southern Australia: a breeding ewe enterprise at Benalla; a wool-producing enterprise at Hamilton; and a beef breeding enterprise at Corryong

Simulation study of pressure and temperature dependence of the negative thermal expansion in Zn(CN)(2)

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Failure mechanisms of graphene under tension

Recent experiments established pure graphene as the strongest material known to mankind, further invigorating the question of how graphene fails. Using density functional theory, we reveal the mechanisms of mechanical failure of pure graphene under a generic state of tension. One failure mechanism is a novel soft-mode phonon instability of the $K_1$-mode, whereby the graphene sheet undergoes a phase transition and is driven towards isolated benzene rings resulting in a reduction of strength. The other is the usual elastic instability corresponding to a maximum in the stress-strain curve. Our results indicate that finite wave vector soft modes can be the key factor in limiting the strength of monolayer materials

Grid computing and molecular simulations: the vision of the eMinerals Project

This paper discusses a number of aspects of using grid computing methods in support of molecular simulations, with examples drawn from the eMinerals project. A number of components for a useful grid infrastructure are discussed, including the integration of compute and data grids, automatic metadata capture from simulation studies, interoperability of data between simulation codes, management of data and data accessibility, management of jobs and workflow, and tools to support collaboration. Use of a grid infrastructure also brings certain challenges, which are discussed. These include making use of boundless computing resources, the necessary changes, and the need to be able to manage experimentation