A Controlled Vocabulary and Metadata Schema for Materials Science Data Discovery

Computational science, data management and analysis have been key factors in the success of Brookhaven National Laboratory’s scientific programs at the Relativistic Heavy Ion Collider (RHIC), the National Synchrotron Light Source II (NSLS-II), the Center for Functional Nanomaterials (CFN), and in biological, atmospheric, and energy systems science, Lattice Quantum Chromodynamics (LQCD) and Materials Science, as well as our participation in international research collaborations, such as the ATLAS experiment at Europe’s Large Hadron Collider (LHC) and the Belle II experiment at KEK (Japan). The construction of a new data center is an acknowledgement of the increasing demand for computing and storage services at BNL.

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Comparing mathematical and heuristic approaches for scientific data analysis

Artificial intelligence for engineering design analysis and manufacturing ◽

10.1017/s0890060408000048 ◽

2007 ◽

Vol 22 (1) ◽

pp. 53-69 ◽

Cited By ~ 2

Author(s):

Aparna S. Varde ◽

Shuhui Ma ◽

Mohammed Maniruzzaman ◽

David C. Brown ◽

Elke A. Rundensteiner ◽

...

Keyword(s):

Mathematical Models ◽

Data Storage ◽

Materials Science ◽

Scientific Data ◽

Estimation Problem ◽

Predictive Analysis ◽

Heuristic Methods ◽

Science Data ◽

Domain Specific ◽

Computational Estimation

AbstractScientific data is often analyzed in the context of domain-specific problems, for example, failure diagnostics, predictive analysis, and computational estimation. These problems can be solved using approaches such as mathematical models or heuristic methods. In this paper we compare a heuristic approach based on mining stored data with a mathematical approach based on applying state-of-the-art formulae to solve an estimation problem. The goal is to estimate results of scientific experiments given their input conditions. We present a comparative study based on sample space, time complexity, and data storage with respect to a real application in materials science. Performance evaluation with real materials science data is also presented, taking into account accuracy and efficiency. We find that both approaches have their pros and cons in computational estimation. Similar arguments can be applied to other scientific problems such as failure diagnostics and predictive analysis. In the estimation problem in this paper, heuristic methods outperform mathematical models.

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Immersive visualization for materials science data analysis using the Oculus Rift

2015 IEEE International Conference on Big Data (Big Data) ◽

10.1109/bigdata.2015.7364040 ◽

2015 ◽

Cited By ~ 8

Author(s):

Margaret Drouhard ◽

Chad A. Steed ◽

Steven Hahn ◽

Thomas Proffen ◽

Jamison Daniel ◽

...

Keyword(s):

Data Analysis ◽

Materials Science ◽

Science Data ◽

Oculus Rift ◽

Immersive Visualization

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Enhancing the impact of science data toward data discovery and reuse

2015 IEEE/ACIS 14th International Conference on Computer and Information Science (ICIS) ◽

10.1109/icis.2015.7166605 ◽

2015 ◽

Cited By ~ 1

Author(s):

Alan Chappell ◽

Jesse Weaver ◽

Sumit Purohit ◽

William Smith ◽

Karen Schuchardt ◽

...

Keyword(s):

Science Data ◽

Data Discovery ◽

The Impact

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Robust recognition and exploratory analysis of crystal structures via Bayesian deep learning

Nature Communications ◽

10.1038/s41467-021-26511-5 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Andreas Leitherer ◽

Angelo Ziletti ◽

Luca M. Ghiringhelli

Keyword(s):

Deep Learning ◽

Crystal Structures ◽

Metallic Nanoparticles ◽

Materials Science ◽

Structural Data ◽

Structure Identification ◽

Science Data ◽

Major Step ◽

Geometrical Properties ◽

Uncertainty Estimates

AbstractDue to their ability to recognize complex patterns, neural networks can drive a paradigm shift in the analysis of materials science data. Here, we introduce ARISE, a crystal-structure identification method based on Bayesian deep learning. As a major step forward, ARISE is robust to structural noise and can treat more than 100 crystal structures, a number that can be extended on demand. While being trained on ideal structures only, ARISE correctly characterizes strongly perturbed single- and polycrystalline systems, from both synthetic and experimental resources. The probabilistic nature of the Bayesian-deep-learning model allows to obtain principled uncertainty estimates, which are found to be correlated with crystalline order of metallic nanoparticles in electron tomography experiments. Applying unsupervised learning to the internal neural-network representations reveals grain boundaries and (unapparent) structural regions sharing easily interpretable geometrical properties. This work enables the hitherto hindered analysis of noisy atomic structural data from computations or experiments.

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Non-Structured Materials Science Data Sharing Based on Semantic Annotation

Data Science Journal ◽

10.2481/dsj.007-042 ◽

2009 ◽

Vol 8 ◽

pp. 52-61 ◽

Cited By ~ 10

Author(s):

Changjun Hu ◽

Chunping Ouyang ◽

Jinbin Wu ◽

Xiaoming Zhang ◽

Chongchong Zhao

Keyword(s):

Data Sharing ◽

Materials Science ◽

Semantic Annotation ◽

Science Data ◽

Structured Materials

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Tracking materials science data lineage to manage millions of materials experiments and analyses

npj Computational Materials ◽

10.1038/s41524-019-0216-x ◽

2019 ◽

Vol 5 (1) ◽

Cited By ~ 5

Author(s):

Edwin Soedarmadji ◽

Helge S. Stein ◽

Santosh K. Suram ◽

Dan Guevarra ◽

John M. Gregoire

Keyword(s):

Materials Science ◽

Science Data ◽

Data Lineage

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Object-Oriented Heterogeneous Database for Materials Science

Scientific Programming ◽

10.1155/1992/463804 ◽

1992 ◽

Vol 1 (2) ◽

pp. 115-131 ◽

Cited By ~ 2

Author(s):

David Hansen ◽

David Maier ◽

James Stanley ◽

Jonathan Walpole

Keyword(s):

Materials Science ◽

Object Oriented ◽

Physical Property ◽

Data Sources ◽

Heterogeneous Database ◽

Science Data ◽

Scientific Database ◽

External Data ◽

Information Models ◽

Object Oriented Database

As a part of the scientific database research underway at the Oregon Graduate Institute, we are collaborating with materials scientists in the research and development of an extensible modeling and computation environment for materials science. Materials scientists are prolific users of computers for scientific research. Modeling techniques and algorithms are well known and refined, and computerized databases of chemical and physical property data abound. However, applications are typically developed in isolation, using information models specifically tailored for the needs of each application. Furthermore, available computerized databases in the form of CDs and on-line information services are still accessed manually by the scientist in an off-line fashion. Thus researchers are repeatedly constructing and populating new custom databases for each application. The goal of our research is to bridge this gulf between applications and sources of data. We believe that object-oriented technology in general and data-bases in particular, provide powerful tools for transparently bridging the gap between programs and data. An object-oriented database that not only manages data generated by user applications, but also provides access to relevant external data sources can be used to bridge this gap. An object-oriented database for materials science data is described that brings together data from heterogeneous non-object-oriented sources and formats, and presents the user with a single, uniform object-oriented schema that transparently integrates these diverse databases. A unique multilevel architecture is presented that provides a mechanism for efficiently accessing both heterogeneous external data sources and new data stored within the database.

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KIST-NOMAD - a Repository to Manage Large Amounts of Computational Materials Science Data

Korean Journal of Metals and Materials ◽

10.3365/kjmm.2020.58.10.728 ◽

2020 ◽

Vol 58 (10) ◽

pp. 728-739

Author(s):

Samuel Boateng ◽

Kwang Ryeol Lee ◽

Deepika ◽

Haneol Cho ◽

Kyu Hwan Lee ◽

...

Keyword(s):

Large Scale ◽

Materials Science ◽

Application Programming Interface ◽

Scientific Data ◽

Dynamics Simulation ◽

Data Repository ◽

Science Data ◽

Front End ◽

Computational Material ◽

Efficient Option

We introduce the Korea Institute of Science and Technology-Novel Materials Discovery (KISTNOMAD) platform, a materials data repository. We describe its functionality and novel features from an academic viewpoint. It is a data repository designed for computational material science, especially focusing on managing and sharing the results of molecular dynamics simulation results as well as quantum mechanical computations. It consists of three main components: a database, file storage, and web-based front end. The database hosts material properties, which are extracted from the computational results. The front end has a graphical user interface and an open application programming interface, which allow researchers to interact with the system more easily. KIST-NOMAD’s panel displays the searched results on a well-organized and research-oriented web page. All the open access data and files are available for downloading in comma-separated value format as well as zipped archives. This automated extraction function was developed by utilizing database parsers and JSON scripts. KISTNOMAD also has an efficient option to download simulation and computation results on a large-scale. All of the above functions are designed to satisfy academic and research demands, and make highthroughput screening available, while incorporating machine learning for computational material engineering. We finally stress that the repository platform is user-driven and user-friendly. It is clearly designed to follow the modern big-data architecture and re-use principles for scientific data, such as being findable, accessible, and interoperable.

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