NIST Materials Data Repository
The National Institute of Standards and Technology has created a materials science data repository as part of an effort in coordination with the Materials Genome Initiative (MGI) to establish data exchange protocols and mechanisms that will foster data sharing and reuse across a wide community of researchers, with the goal of enhancing the quality of materials data and models. Data present on this system are varied and may originate from within NIST or from the worldwide materials community. Data present on this system may or may not be critically reviewed.
All data in this repository are intended to be public; some data may be a part of invitation-only collections while researchers continue to analyze the data prior to publication, with the goal of making the public data presentable in a meaningful way. Publishable data derived from working data will be made public at the appropriate time (this may coincide with end of a grant or when the data has been accepted for publication).
The NIST Materials Data Repository is open to the research community interested in developing best practices in the management of materials data. We are currently looking into use cases for expansion of the site including, but not limited to, partnerships with other projects focusing on discoverability.
Anyone having an interest in an invitation-only collection should contact the site administrator for more information.
The Al-Co-W system and its binary sub-systems Al-Co, Al-W and Co-W were critically reviewed. The thermodynamic description of the Al-Co-W system including all three binaries was developed considering thermodynamic and ...
Evaluating error with atomistic simulations: the effect of potential and calculation methodology on the modeling of lattice and elastic constants Atomistic simulations using classical interatomic potentials are powerful investigative tools linking atomic structures to dynamic properties and behaviors. It is well known that different interatomic potentials produce ...
(2017-08-18)A computational framework is proposed that enables the integration of experimental and computational data, a variety of user-selected models, and a computer algorithm to direct a design optimization. To demonstrate this ...