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JAMES A WARREN
DIRECTOR, MATERIALS GENOME PROGRAM
MATERIAL MEASUREMENT LABORATORY
NATIONAL INSTITUTE OF STANDARDS AND TECHNOLOGY
EXECUTIVE SECRETARY, NSTC SUBCOMMITTEE ON MGI
MATERIALS
CHALLENGES
MGI FOR SCIDATACON
QUESTIONS
▸ What is your discipline culture with regard to data sharing?
▸ What are the science drivers for building your discipline interoperability
infrastructure?
▸ How is your discipline organized to build interoperability infrastructure?
▸ What are the lessons learned from building your discipline interoperability
infrastructure?
▸ Which part of your discipline interoperability infrastructure could be made
generic for other disciplines?
▸ WHAT IS YOUR DISCIPLINE??
MGI FOR SCIDATACON
WHAT IS THE DISCIPLINE?
▸ Materials Research is about making stuff (transistors, LED,
titanium turbine blades, sporting goods…)
▸ Processing (Recipe)
▸ Structure (Souffle or Mess?)
▸ Properties (Fluffy, Delicious vs. Rocklike Charcoal)
▸ Challenge is to design new materials from existing
knowledge without resorting to raw “Edisonian” repetition.
Models
Data
The MGI Approach
Creating and Capturing Knowledge of Materials
Experiment
Quantum
Nano
Micro
Macro
Simulation
Materials
w/ Targeted Properties
MGI FOR SCIDATACON
WHAT IS YOUR DISCIPLINE CULTURE WITH
RESPECT TO DATA SHARING?
▸ Better than it was 4 years ago!
▸ Usual incentive challenges
▸ Current reward model for high impact pubs
▸ little reward for data/code sharing
▸ Software community better, but commercial codes are a lucrative
business model, so there is a natural tension
▸ Younger researchers seem more inclined to accept the new
paradigm
MGI FOR SCIDATACON
WHAT ARE THE SCIENCE DRIVERS FOR BUILDING YOUR
DISCIPLINE INTEROPERABILITY INFRASTRUCTURE?
▸ High value data from beam lines (neutrons and photons),
and supercomputers)
▸ Need for better, multi-scale models
▸ Opportunities for long-tail “big data” research are intriguing
Astronomy vs. Material Measurement
Measurement Type
Material synthesis and
processing history
Position in the sky
e.g.
e.g.
Measurement Type
Singular data models
Modular data models
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MGI FOR SCIDATACON
WHAT ARE THE LESSONS LEARNED FROM BUILDING YOUR
DISCIPLINE INTEROPERABILITY INFRASTRUCTURE?
▸ Change is hard
▸ Need for tools is self-evident (now)
▸ Standards are risky
▸ The NIST MGI Program is taking a very careful approach to standards for data
representation.
▸ There is a long track record of failure for most of the space
▸ exceptions for highly structured data (such as crystallography and molecular
structure)
▸ Strong belief that this should be top-down (this is counter-intuitive to some)
MGI FOR SCIDATACON
WHICH PART OF YOUR DISCIPLINE INTEROPERABILITY INFRASTRUCTURE
COULD BE MADE GENERIC FOR OTHER DISCIPLINES?
▸ We (a collaboration between several elements of NIST) are
developing
▸ Repositories
▸ A data curation system
▸ A registry infrastructure
▸ Others are building “platforms” to capture research workflows and
data (Materials Commons, ICE)
▸ NLP techniques?