Workshop Goals

Establish a community-based approach to filter the data noise and enhance the value of global

information in nanoscience and nanotechnology

Clearly define immediate and projected informatics infrastructure needs of the nanotechnology

community. Our path forward.

The theme of nanoEHS will be used to provide real-world, concrete examples on how informatics can be utilized to advance

our knowledge and guide nanoscience.

Participant Diversity

74 Participants – national and international• Academics (US and abroad)• Government (NNCO, NIEHS, NIOSH, State Agencies, military)• National Laboratories• Industry

Agenda

Four Steps for Community Action

• Engage the community• Inform the interested• Reward the responsive• Understand and incentivize the reluctant

We are traveling a long road.“Tailored” approaches are needed.

Nanoinformatics 2020 Roadmap

4

Nanotechnology Knowledge Infrastructure:Enabling National Leadership in Sustainable Nanomaterial

Design

Dr. Sally TinkleDeputy Director

National Nanotechnology Coordination Officestinkle@nnco.nano.gov

National Nanotechnology Initiative

A Nanotechnology Signature Initiative

Nanotechnology Knowledge Infrastructure (NKI):Enabling National Leadership in Sustainable Nanomaterial Design

• Overarching Goal: To develop the infrastructure to support the transformation of data to knowledge in support of nanotechnology

• National Need: Nanotechnology R&D; Environment, Health & Safety• Collaborating Agencies: CPSC, DOD, DOE, EPA, FDA, NASA, NIH, NIOSH,

NIST, NSF, OSHA

National Nanotechnology Initiative

Thrust 2: Foster an agile

modeling network for multidisciplinary

intellectual collaboration

Thrust 3: Build a sustainable nanotechnology

cyber-toolbox

Thrust 4: Create a robust digital

nanotechnology data and information

infrastructure

Thrust 1: Build a diverse collaborative

community of scientists, engineers,

and technical staff

1. Thrust 1: 2. Build a diverse

collaborative community of

scientists, engineers, and technical staff

National Nanotechnology Initiative

• Build a diverse collaborative community that will:• Train the next generation• Develop methods, tools, and infrastructure

Expected Outcomes:• An integrated and highly skilled nanoinformatics community

to build and sustain nanotechnology-enabled U.S. industries

• Education and training of the next-generation modeling

network to sustain the intellectual infrastructure for future

nanotechnology

Thrust 2: Foster an agile

modeling network for multidisciplinary

intellectual collaboration

National Nanotechnology Initiative

• Modeling network that will:• Provide the nexus

for computation, experiment, and theory

• Be motivated by specific real-world problems for which nanotechnology may provide solutions

• Expected Outcomes:• A robust modeling community that will

develop and validate a library of models and simulations to address the spectrum of nanotechnology questions that will be easily accessible to broader communities and stakeholders.

• A compendium of “lessons learned” from the modeling activities that will inform the nanotechnology community of experiments and models across the scientific disciplines.

• Models that are easily accessible to broader communities and stakeholders, such as communities focusing on other length scales, and with sufficient reliability and validity to design sustainable materials that maximize beneficial properties and minimize potential hazards.

• Shortened development time to achieve similar quality models, due to early and more detailed, frequent, and constructive peer review.

Thrust 3: Build a sustainable nanotechnology

cyber-toolbox

National Nanotechnology Initiative

Expected Outcomes:• A nanotechnology cyber-toolbox that will generate collaboratively developed

models to enable understanding of nanomaterials properties, behavior, and impact on biological and environmental systems, including a suite of theoretical, statistical, and visualization tools that will facilitate the planning, execution, and analysis of experiments.

• A central access point on the NNI website and administered by NNCO for linking existing interdisciplinary cyber-toolbox components to improve user accessibility

• Educational opportunities that will integrate the cyber-toolbox and the information infrastructure into the intellectual framework of nanoscale science and engineering.

• A robust and reliable cyber-toolbox that is• Well maintained• Validated• Advances understanding of

nanomaterial design

Expected Outcomes:• Strategic development of interoperable systems that will enable best practice data

curation, organization, transfer, and sharing.

• Standards and procedures for data management and use that will enable significantly more extensive utilization of the databases.

• Expansion of data pattern recognition, data correlation determination, and data-based prediction capabilities.

• Robust validation procedures and reference data standards.

• Mechanisms for assessing and meeting the evolving needs of participating scientists for data and model acquisition, sharing, and archiving.

Develop an infrastructure that will:• Enable integration and effective use of existing data and

information

• Provide a consistent framework to incorporate new data and information

Thrust 4: Create a robust digital

nanotechnology data and information

infrastructure

National Nanotechnology Initiative

National Nanotechnology Initiative

• Identifying Synergies and Opportunities: What Can We Build Together?

• Potential areas for collaboration• Understanding physical and chemical properties along the length and time scale• Data and databases of mutual interest• Procedural and technical issues in developing open innovation communities

• e.g., IP and standards, data curation and federation, minimum information requirements

• Shared protocols and best practices

National Nanotechnology Initiative

• Role of the Federal Government in Science: Making Actionable Recommendations

• Inherently governmental research spans federal, private entities• Government to government collaboration for research and policy• Partner in pubic-private partnerships

Nanotechnology Knowledge Infrastructure (NKI):Enabling National Leadership in Sustainable Nanomaterial Design

• Questions for you:• Where did we get it right? Where not?• What’s missing? Add? Others will handle?• Where are the opportunities and synergies?• Next steps?

Agenda

Luncheon Keynote Address

Krishna Rajan cyber-enabled

Luncheon Keynote AddressKrishna Rajan

Luncheon Keynote AddressKrishna Rajan

Luncheon Keynote AddressKrishna Rajan

SESSION 1: Data Lifecycle to Support a Sustainable Cyber-Toolbox

Session 1 Leaders: Jim Hutchison (University of Oregon)

Nanomaterial Expertise/Focus

Raul Cachau (Frederick National Laboratory for Cancer Research)

Victor Maojo (Universidad Politécnica de Madrid) Biomedical Informatics Focus

Michele Ostraat (RTI International)

Raw Data Processed Data

Publication of Findings

Curation of Data

Computational Analysis of Curated

Data

Current Data Lifecycle

Session 1 Outputs

• Recommendation on IDEAL data lifecycle• Identification of data gaps and barriers to sustainable

nanoinformatics

An ideal data lifecycle? General comments

• There isn’t an ideal lifecyle• Different classes of nanomaterials will require unique solutions• Incomplete data, poor sample integrity are challenges • Real raw data, annotations, and define protocols are not available• Materials transformations and batch-to-batch variation require

measurement validation methods• Reference materials are needed• Need a pull (rather push) for the output of informatics effort• Who archives, curates, stewards? No one is responsible

Barriers/solutions/recommendations• Need to bite off chunks of the problem• Push for incremental changes in data assessment and archiving• Define and share protocols• Keep raw data• Generate reference materials to feed the informatics process• Utilize standards to calibrate methods and “normalize” datasets• Tailor datasets to different classes of materials• Develop measurements that address batch-to-batch variation and

nanomaterial interconversions (shelf life, biotransformations)• Examine funding agency or journal requirements to pull data• Establish funded curation efforts – akin to PDB or others

Approaches to making progress• Develop models that are weighted by based upon “strength” of

the data• Focus on islands of data that are being amassed• Develop an incubator approach to capture data, incubate and

advance for use-inspired informatics

• Future workshop: Education and incentives to support an effective and sustained informatics effort

Incubator

• Designed to identify nanoinformatics needs• Collect all data that might be useful• Draw on islands• Establish pull from stakeholder sectors• Spokes of effort tied to solutions – use inspired

Manufacturing

Cancer therapy

EnvironmentalRemediation

Incubator – alldata, protocols

Recommendation on IDEAL data lifecycle

• Distinguish between data and process– Boxes as nouns, arrows as verbs

• Important to understand objectives of why you want this data and what you want the model to do– Decision driven goals – why

• Filtering process – the right data and quality of data• Hypothesis testing

– Endpoint rationale – linkage of model goals w/ objectives – Dual use of data – leveraging

• ID data gaps – what needs to be added/gained

• Data Quality– Inventory of data – access the original data source– Database and publication – include original raw data in addition to figure– Standard descriptors data language– Sources of data – curation (need box between data and repository)– Error and variability applies to arrow to processed data and repository– Validation of data– Raw data box – should include the ‘data product’ that includes negative results

• Expert system to manage knowledge that is linked to data

Raw Data Processed Data

Data Repositories

Computational Analysis of Raw and

Processed Data

ErrorVariability

Inter-laboratory comparisonsModel organism comparisons

nanoSARsPredictive models

Informed study design

Re-Informed Data Objectives

Publication of Findings

Data gaps and barriers• Intellectual property/proprietary information not accessible

– Competition within field– Fund projects to product data – ‘catalyst’

• How to incentivize database development & contribution• Connectivity between databases and models (some are open source)

– Variability in vocabulary/nomenclature– Data format/structures

• Data life needs to include Data retirement – – Not deleted– Add filters – One Solution: score data (Nanomaterial Registry, caNanoLab)

• Mechanism to ID datagaps– Visualization of data

• New technologies may result in new data/categories– Flexible tools and data models needed

• Recognize value of 1st principles vs. empirical models

SESSION 2: Use of Nanoinformatics for Predictive Modeling

Nathan Baker (Pacific Northwest National Laboratory) Nanomaterial SARS

Yoram Cohen (University of California Los Angeles) Nanomaterial Environmental Fate Modeling

Sharon Gaheen (SAIC-Frederick) Predicting Nanomaterial Biodistribution

Mark Tuominen (National Nanomanufacturing Network) Nanomanufacturing Supported by Informatics

Session 2 Outputs

• Recommendations on nanomaterial description requirements for predictive modeling

• Examples illustrating utility of a Nanoinformatics approach

What are the outputs (goals) of predictive models?

• Where does it go?– Biodistribtion, fate/transport

• What does it do when it gets there?– Transformation in biological system/environment– Action/consequence– Time-dependence?

• How do we know?– Positive/negative controls

Use Case for Predictive Modeling to Support Prediction of Biodistribution of Nanomaterials

What are unique challenges for nano?

• Protocol reproducibility• Testable predictions• In vitro in vivo in silico• Heterogeneity• Data uncertainty• Model integration• Positive/negative controls• Systematic data sets – a nanomaterial that has been systematically

designed so that you can see the effects – systematic characterization wrt design

• Data access/sharing/interoperability• Data comparability/fusion

Examples of success

• Biological description of functionalized buckeyballs

• Los Angeles model of seasonal fate/transport• Nanoemulsion complement activation• Interlaboratory comparison – ToxCast as

example

Use Case for Predictive Modeling to Support Nanomanufacturing

Nanomanufacturing supported by informatics • Predicting properties of [quantum dots] from synthesis control

conditions and the resulting impact on application performance and EHS

1. Dual use of data (leveraging effort) --- for example properties for product design and for EHS

2. "process --> property" relationships - vital to manufacturing (scale up, reproducibility, sustainable design, etc) – first principles vs phenomenological models

3. Ex. quantum dots -- having specific customizable electron energy levels and optical properties due to "quantum confinement" - making them useful for applications such as solar cells, solid-state lighting, imaging dyes, and more. (alt: nanoparticles of silica, ZnO, TiO2, etc)

4. Manufacture (synthesis) - colloidal synthesis (wet process), fabrication (vacuum process), thermal flow reaction (gas phase). Ligands added for solubility/dispersion.

5. EHS - how the same properties impact EHS

Use Case for Predictive Modeling to Support Nanomanufacturing

Nanomanufacturing supported by informatics • Predicting properties of [quantum dots] from synthesis control

conditions and the resulting impact on application performance and EHS• Dual use of data: Areas completely orthogonal? > Identify the

common overlaps and start there. Iterate.• Ex. Average size and distribution

• Models: some first principles, but mostly phenomenological• Statistical design experiments (with high-throughput screening) can

help ID which inputs/properties are important and which are not

modeling modeling

particles &properties

inputs product & EHS

Based on stakeholders with differing perspectives(enable redesign, for example)

Use Case for Predictive Modeling to Support Nanomanufacturing

Continued:• Use models to help optimize manufacturing based on properties, cost,

safety, sustainability• Must collect data according to protocols that “fit” into database

informatics system so that it is useful for subsequent modeling and mining

• In some cases, anonymous sharing of data• Ag nanoparticle example: narrow the focus by identifying what

properties are important and what are not (for tox studies); ID hidden variables(?)

• Sharing of informatics tools for mining• For quantum dots, some modeling/simulations for structure-property

exist

SESSION 3: Nanoinformatics Integration

Jeff Steevens (US Army Engineer Research and Development Center)

Sally Tinkle (National Nanotechnology Coordination Office)

Jeff Morse (National Nanomanufacturing Network)

Stacey Harper (Oregon State University)

Session 3 Outputs

• Recommendations for development of necessary and sufficient standards to catalyze nanoscience and support nanoinformatics approaches and modeling;

• Specific plans for data sharing and informatics integration; – Including the new Signature Initiative on Nanotechnology Knowledge

Management– Including the Nanoinformatics: Principles and Practices book project

(please contact Mark Hoover, Nathan Baker, or Stacey Harper for more information)

• Identification of informatics gaps• Use cases that illustrate the value proposition for informatics

in nanomanufacturing and nanoEHS

Connections with the Materials Genome Initiative

• Data sharing• Minimal info standards• QSPR• Model complexity – integration, multiscale