报告题目：材料基因组和大数据
报告人：Rika Kobayashi
报告时间：2016年5月19日(周四)9:30
报告地点：校本部E408
邀请人：Jeffrey Reimers 教授，任伟教授
报告摘要:
In 2012 the United States launched the Materials Genome Initiative (MGI) to support the development of computational tools and databases for material characterisation and accelerate the development of advanced materials. MGI institute at Shanghai University is a central contributor to the Chinese Materials Genome Project. The workflow involved in the MGI project [1] can be broken down into:
* High Throughput Computing which involves running programs and method benchmarking. The materials of interest to the MGI are mostly solid-state with properties calculated by associated programs, using classical molecular dynamics (e.g. LAMMPS) or quantum molecular dynamics (e.g. VASP). However, the underlying methodology and their different algorithms mean that these programs often give results of varying accuracy for the same property and it is necessary to compare the results against each other and if possible high accuracy ab initio quantum chemistry methods.
* Big Data which concerns the volume, variety, value, veracity and velocity (the 5Vs) of all the data that is being generated. So far it seems most work in the MGI has been involved with generating and validating the data, with less work being carried out on issues of metadata collection and annotation; data storage; access to metadata catalogues; and exploration and analysis of the data. Colleagues at ANU already have experience in whole-of-lifecycle Big Data expertise from data creation, storage, provenance tracking, publication, discovery, use and citation [2] and it is believed these techniques are applicable and transferrable to the work at the MGI institute.
G. Ceder, "The Materials Genome Project: Materials Design with High-Throughput Computation" Downloaded 8th December 2015 from http://web.ornl.gov/sci/cmsinn/talks/8_ceder.pdf
2. B. Evans, L. Wyborn, T. Pugh. C.Allen, J. Antony, K. Gohar, D. Porter, J. Smillie, C. Trenham, J. Wang, A. Ip, G. Bell, 2015.. "The NCI High Performance Computing and High Performance Data Platform to Support the Analysis of Petascale Environmental Data Collections" in Environmental Software Systems, Infrastructures, Services, Applications, I R. Denzer, R.M. Argent, G Schinak, J.Hrebicek, Eds., FIP AICT 448, pp. 569–577, (2015). http://www.springer.com/gp/book/9783319159935
