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What is Nanoinformatics?

Working Definition

Nanoinformatics is the science and practice of determining which information is relevant to the nanoscale science and engineering community, and then developing and implementing effective mechanisms for collecting, validating, storing, sharing, analyzing, modeling, and applying that information.

  • Nanoinformatics is necessary for intelligent development and comparative characterization of nanomaterials, for design and use of optimized nanodevices and nanosystems, for development of advanced instrumentation and manufacturing processes, and for assurance of occupational and environmental safety and health.
  • Nanoinformatics also involves the utilization of networked communication tools to launch and support efficient communities of practice.
  • Nanoinformatics also fosters efficient scientific discovery through data mining and machine learning.

Nanoinformatics Background

Speaking broadly, informatics is the application of information and computer science methods for collecting, analyzing, and applying information. “X-informatics” has become the default descriptor for the application of such methods to a set of problems within a specific field or discipline, such as bioinformatics in biology or ecoinformatics in ecology. Similarly, computational science is the use of high-powered computing and sophisticated algorithms to posit and solve problems. “Computational X” refers to the use of computational methods within a specific field or discipline (e.g., computational astronomy or computational geology). Nanotechnology is the umbrella term for science, engineering, and technology at the nanoscale (approximately 1 to 100 nanometers). Nanotechnology encompasses synthesizing, imaging, measuring, modeling, and manipulating matter at the nanoscale in order to understand and control materials properties. Thus, nanoinformatics is a systematic methodology to collect, organize, validate, store, share, model, and analyze data involved with nanotechnology processes and materials for the purpose of extracting useful information relevant to the nanoscale science and engineering communities. Computational nanoscience, conceived as falling within the broader term nanoinformatics, includes the development and application of the critical tools needed for simulations, computations, and predictive modeling of nanomaterials, nanoscale devices, and nanosystems.

In the last two decades, large-scale data explorations have begun to combine data-driven experimental and computational science with informatics methods utilizing massive computing networks, cutting-edge information science tools, and social networking technologies; these projects herald the beginning of the age of e-Science[1]. Popular examples of e-Science such as the Human Genome Project and the Sloan Digital Sky Survey demonstrate how computational sophistication and the coordination of domain expertise can be harmonized to address grand scientific challenges. Nanoinformatics--the application of the e-science paradigm to nanoscale science and engineering--targets challenges in the application of nanotechnology for the benefit of society.

Vision for Nanoinformatics

Informatics catalyzes the efficiency of scientific and industrial workflows across all nanotechnology domains and areas of application, including fundamental research, product innovation and manufacturing, and EHS practices.

Nanoinformatics for the workflow of research

Implemented at the heart of the research and investigation, nanoinformatics would allow researchers to leverage the findings of other efforts in support of their own investigations and to broaden the impact of their research. The traditional research lifecycle is oriented around pre-and post-publication milestones of information collection, preparation, analysis, and dissemination, all of which have conventionally taken place on distinct and unintegrated platforms, or silos. Developments in computational technology and networked communication allow communities of practice to integrate the silos of different domains and at different stages of investigation to achieve outcomes more efficiently and to deepen their impact throughout the domain.

For example, experimentation can be conducted using high-throughput tools and minimum data standards to capture large-scale, standards-compliant data sets. Using mapping, visualization, and advanced analytical tools, a researcher may uncover important information which points research in new directions. Such cyber-enabled discoveries can quickly advance the exploration and application of systems too complex to be understood solely from first-principles science. Similarly, modeling and simulation efforts, subject to robust validation and verification, can provide information that complements experimental data and motivates subsequent research. Data can be made publicly available prior to or in conjunction with publication through established data repositories and can benefit from the use of standard attribution and identification mechanisms such as digital object identifiers (DOIs) for data sets. Data sets and supplementary files can be mined, along with peer-reviewed literature, for trends and gaps. Semantic search algorithms, federated data, and ontologies all contribute to the discoverability and reuse of data, which seeds future investigations. Increasingly sophisticated tools for networked communication and collaboration tie all of the components of the life cycle together. (See Figure 1.[2]).

Figure 1

The utilization of appropriate nanoinformatics mechanisms throughout the research cycle will ultimately lead to the efficient advancement of nanotechnology research and the commercialization of nanotechnology-enabled products and systems.

Nanoinformatics for the workflow of product innovation and manufacturing

Another area poised to greatly benefit from nanoinformatics is product development and manufacturing. Although distinct from the activities involved in fundamental research, the industrial workflow associated with nanotechnology commercialization shares several common attributes. Data-driven activities in industry accelerate product design, product performance, reliability, manufacturing design, logistics, quality control, safety, and the business model. Indeed, there are many opportunities for productive synergy between the nanoinformatics activities of scholarly research and those of industrial development. In the present day, new technology development relies heavily on the data and information provided by fundamental research activities. Access to more complete data sets than the limited representative examples appearing in the published scientific literature could enable feasibility studies and design activities in industry. Nanoinformatics tools can streamline workflow activities and reduce the time to market. Similarly the use of nanoinformatics tools leading to the identification of data or modeling gaps in industry can drive new fundamental research activities to fill those needs.

From an industrial perspective, the value-added of each process step in a manufacturing chain of activity (the value chain) is better controlled and optimized when it is a data-rich activity. The design and manufacturing of products that contain nanomaterials require specific properties data that affect the overall performance. The raw materials, which in some cases are themselves nanomaterials, should arrive with materials certification data that describe their structure and properties. Each subsequent manufacturing process step has inputs that affect the resulting output structure and properties. Nanoinformatics-based simulation and modeling in combination with metrological process data can enable accurate process control and optimization. By performing a sensitivity analysis using known input distributions, variabilities, and process-property relationships, manufacturing reproducibility can be predicted and design margins can be established, making nanomanufacturing process optimization and adaptable manufacturing viable. In the manufacturing stream there should be, ideally, a stream of process and characterization data providing provenance data for standardization, extensibility, and new manufacturing innovations.

Nanoinformatics for the workflow of EHS practices

Additional data-rich measures performed concurrently with production data activities can support good EHS practices through the entire product lifecycle. Efforts at national laboratories, academic research centers, and forward-looking industrial sites strive to collect data that identify any potential risk of nanomaterials to health or the environment, perform site testing to evaluate exposure potential, and establish best practices for worker safety. Making the results of these efforts available broadly through information repositories will provide pathways for innovative, safe, and sustainable manufacturing of nanotechnology-enabled products.

These envisioned modifications to research, production, and EHS practices are profound. Such broad-based change cannot be implemented immediately; current nanoinformatics tools are nascent at best, and cultural shifts need time to percolate. This roadmap therefore recommends a graded introduction of nanoinformatics techniques and tools over the next ten years, with the following approaches leading to community-wide adoption:

  • stepped roll-out of pilot projects;
  • recurring workshops and community engagement activities;
  • development of data literacy through education; and
  • advocacy at the agency level for nanoinformatics as an essential piece of the nanotechnology research and development enterprise.


  1. Hey T, et. al. eds. (2009) The Fourth Paradigm: Data-Intensive Scientific Discovery. Microsoft Research. 284 pp.
  2. Figure adapted from Gold A. 2007. Cyberinfrastructure, Data, and Libraries Pt. 1. D-Lib Magazine 13 9/10. doi: 10.1045/september20september-gold-pt1

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