Towards a Content-Based Material Science Discovery Network

2013

Chemical Semantics is a new start-up devoted to bringing the Semantic Web to computational chemistry, with a future goal to cover chemical results of other kinds, including experimental. We have created a prototype test bed that enables computational chemists to publish the results of their computations (for example: structure, energies and wave functions of Ab Initio calculations) on servers (powered by semantic triple stores) holding RDF data. In addition, scientists will be able to search across results produced by themselves and other researchers using SPARQL queries and faceted search built on top of it. The semantics of computational chemistry oriented RDF data is defined by an ontology identified as “GC” (Gainesville Core). Gainesville Core defines concepts such as: theoretical models used in computations, molecular systems (as collections of atoms and molecules), molecular arrangements which describe system evolution in time or in phase space and so on. The poster demonstrat...

IEEE Access

The growing complexity and interdisciplinary nature of Materials Science research demand efficient data management and exchange through structured knowledge representation. Domain-Level Ontologies (DLOs) for Materials Science have emerged as a valuable tool for describing materials properties, processes, and structures, enabling effective data integration, interoperability, and knowledge discovery. However, the harmonization of DLOs, and, more generally, the establishment of fully interoperable multilevel ecosystems, remains a challenge due to various factors, including the diverse landscape of existing ontologies. This work provides, for the first time in literature, a comprehensive overview of the state-ofthe-art of DLOs for Materials Science, reviewing more than 40 DLOs and highlighting their main features and purposes. Furthermore, an alignment methodology including both manual and automated steps, making use of Top-Level Ontologies' (TLO) capability of promoting interoperability, and revolving around the engineering of FAIR standalone entities acting as minimal data pipelines (''bridge concepts''), is presented. A proof of concept is also provided. The primary aspiration of this undertaking is to make a meaningful contribution towards the establishment of a unified ontology framework for Materials Science, facilitating more effective data integration and fostering interoperability across Materials Science subdomains.

2003

The goal of the Collaboratory for the Multi-scale Chemical Sciences (CMCS) [1] is to develop an informatics-based approach to synthesizing multi-scale chemistry information to create knowledge in the chemical sciences. CMCS is using a portal and metadata-aware content store as a base for building a system to support inter-domain knowledge exchange in chemical science. Key aspects of the system include configurable metadata extraction and translation, a core schema for scientific pedigree, and a suite of tools for managing data and metadata and visualizing pedigree relationships between data entries. CMCS metadata is represented using Dublin Core with metadata extensions that are useful to both the chemical science community and the science community in general. CMCS is working with several chemistry groups who are using the system to collaboratively assemble and analyze existing data to derive new chemical knowledge. In this paper we discuss the project's metadata-related requirements, the relevant software infrastructure, core metadata schema, and tools that use the metadata to enhance science.

Integrating materials and manufacturing innovation, 2024

The development and discovery of new materials can be significantly enhanced through the adoption of FAIR (Findable, Accessible, Interoperable, and Reusable) data principles and the establishment of a robust data infrastructure in support of materials informatics. A FAIR data infrastructure and associated best practices empower materials scientists to access and make the most of a wealth of information on materials properties, structures, and behaviors, allowing them to collaborate effectively, and enable data-driven approaches to material discovery. To make data findable, accessible, interoperable, and reusable to materials scientists, we developed and are in the process of expanding a materials data infrastructure to capture, store, and link data to enable a variety of analytics and visualizations. Our infrastructure follows three key architectural design philosophies: (i) capture data across a federated storage layer to minimize the storage footprint and maximize the query performance for each data type, (ii) use a knowledge graph-based data fusion layer to provide a single logical interface above the federated data repositories, and (iii) provide an ensemble of FAIR data access and reuse services atop the knowledge graph to make it easy for materials scientists and other domain experts to explore, use, and derive value from the data. This paper details our architectural approach, open-source technologies used to build the capabilities and services, and describes two applications through which we have successfully demonstrated its use. In the first use case, we created a system to enable additive manufacturing data storage and process parameter optimization with a range of user-friendly visualizations. In the second use case, we created a system for exploring data from cathodic arc deposition experiments to develop a new steam turbine coating material, fusing a combination of materials data with physics-based equations to enable advanced reasoning over the combined knowledge using a natural language chatbot-like user interface.

2021

The NanoMine knowledge graph is being expanded to support a broader range of materials, now including metamaterials, and is now called MaterialsMine. In the process, we have added new knowledge curation and visualization capabilities to Whyis, the framework that MaterialsMine (and NanoMine before it) is built on. This demonstration will show how we use Whyis to support user-provided data uploads that conform to the Dataset Catalog standard, use DOI and ORCiD to populate dataset metadata, curate data files into knowledge graph fragments using Semantic Data Dictionaries, and allow domain scientists to visualize and explore the graph using SPARQL, Vega-Lite, and Data Voyager.

2010

A chemist given a compound would be interested in knowing the experiments performed using the compound, journals containing the compound and also molecular properties of the compound. If there is a way to integrate this data, it would enhance the chemist's knowledge about a given compound. The Object Reuse and Exchange (ORE) specification may provide a solution to this problem. ORE is a model proposed by the digital libraries community to aggregate resources on the web.

Proceedings of the eighth ACM international workshop on Web information and data management - WIDM '06, 2006

Increasingly, scientists are seeking to collaborate and share data among themselves. Such sharing is can be readily done by publishing data on the World-Wide Web. Meaningful querying and searching on such data depends upon the availability of accurate and adequate metadata that describes the data and the sources of the data. In this paper, we outline the architecture of an implemented cyber-infrastructure for chemistry that provides tools for users to upload datasets and their metadata to a database. Our proposal combines a two level metadata system with a centralized database repository and analysis tools to create an effective and capable data sharing infrastructure.

Proceedings - 2010 6th IEEE International Conference on e-Science, eScience 2010, 2010

The oreChem project, funded by Microsoft Research, is investigating the design and deployment of a semanticbased eScience infrastructure for chemistry. The results of the project include the creation of an ontology that provides the basis for describing the entities and relationships for a scientific experiment, and the implementation of a workflow to combine extracted and in situ information from multiple sources, which provides a framework for computational enhancement of the data and querying thereof.