The Novel Materials Discovery (NOMAD) Laboratory maintains the largest Repository for input and output files of all important computational materials science codes. From its open-access data it builds several Big-Data Services helping to advance materials science and engineering. NOMAD Scope and Overview Data is the raw material for the 21st century.
The NOMAD Laboratory A European Centre of Excellence Matthias Kristian Arndt Bode Ciaran Clissmann Jose Maria Alessandro Scheffler, FHI Thygesen Leibniz Comp. pintail Ltd. Cela, BSC, De Vita King’s MPS, Berlin Tech. U., Lyngby Ctr, Garching Dublin Barcelona Col. London Angel Rubio Claudia Draxl MPI MPSD, Humboldt U, Hamburg Berlin Risto Nieminen Stefan Heinzel Daan Frenkel Kimmo Koski Francesc Illas Aalto U. Helsinki MPS Comp. & U. Cambridge CSC – IT Center U. of Barcelona Data, Garching Helsinki
https://youtu.be/yawM2ThVlGw
Main Services Offered by The NOMAD Repository • Uploading interfaces: Curl, FTP, Python • Supporting the most important codes in computational materials • Structure calculations in data sets (folders) • Share privately with collaborators • Share anonymously during peer review • Open Access Sharing: DOI support, to link from o publication to data https://repository.nomad-coe.eu/ DOI support, to link from data to o publication https://youtu.be/UcnHGokl2Nc • Guaranteed storage for 10 years
The NOMAD Archive 90% of the VASP files are from AFLOWlib and OQMD
NOMAD Encyclopedia: How Does It Work, What Does It Offer Electronic Structure Structure Thermal Properties Methodology
Advanced Graphics
BIG-DATA ANALYTICS We develop and implement methods that identify correlations and structure in big data of materials. This will enable scientists and engineers to decide which materials are useful for specific applications or which new materials should be the focus of future studies.
BIG-DATA ANALYTICS We develop and implement methods that identify correlations and structure in big data of Querying and visualizing the content of the NOMAD Archive materials. This will enable scientists and engineers to decide which materials are useful for Developed by Fawzi Mohamed, Luca Ghiringhelli, and Matthias Scheffler specific applications or which new materials should be the focus of future studies. Crystal structure prediction (probably the most fundamental and important challenge in materials science) • Predicting energy differences between crystal structures Developed by Angelo Ziletti, Emre Ahmetcik, Runhai Ouyang, Ankit Kariryaa, Fawzi Mohamed, Luca Ghiringhelli, and Matthias Scheffler • Discovering simple descriptors for crystal-structure classification Developed by Mario Boley, Bryan Goldsmith, Ankit Kariryaa, and Luca Ghiringhelli • Building structure maps for crystal-structure classification Developed by Angelo Ziletti, Ankit Kariryaa, Fawzi Mohamed, Luca Ghiringhelli, and Matthias Scheffler Predicting ground-states of alloys (convex hull construction) • Ground-state of binary alloys Developed by Santiago Rigamonti, Maria Troppenz and Claudia Draxl • Assessing the crystal-structure stability for a material under different
The Novel Materials Discovery (NOMAD) Laboratory maintains the largest Repository for input and output files of all important computational materials science codes. From its open-access data it builds several Big-Data Services helping to advance materials science and engineering. NOMAD Scope and Overview Data is the raw material for the 21st century.
NOMAD Repository - save and share your data • Go to nomad-coe.eu , select NOMAD Repository • Can you find materials you’re working on? • Upload yours! • Metadata is added automatically (but you can also add comments)
Data Analytics - suggested tutorials • Go to nomad-coe.eu , select Big-Data Analytics , and launch a tutorial • Register or sign in with your temporary account • Querying and visualizing the content of the NOMAD Archive • Interactively look for calculations in the NOMAD Archive. Possible to continue with the tutorial below to apply state-of-the-art machine learning techniques to the query results to find structural similarities in the data. • On-the-fly data analysis for the NOMAD Archive • Evaluating the (dis)similarity of crystalline, disordered, and molecular compounds • Explore the same machine learning method as above, but on a curated dataset. • Discovering simple descriptors for crystal-structure classification • Apply subgroup discovery data mining technique on a prepared dataset advanced. • Analyzing and estimating error bars from high-accuracy references • Compare and estimate errors in density functional theory calculations.
NOMAD - Take home messages • Data sharing and preservation • Discoverability • Collaboration • Ease of use (wrt general repo) • DMP requirements • Data analysis • ”data wrangling ” • Notebooks are good • Use existing workflows and libraries • Scalable platform https://www.nomad-coe.eu/the-project/outreach/nomad-summer • Team up H2020 NOMAD This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 676580.
Recommend
More recommend
