Bryce Meredig of Citrine Informatics presents the company's materials data platform, Citrination. For academic and government users, this infrastructure is a free and open means to meet data management plan requirements of many federal funding agencies.

1. Citrination: Open Infrastructure for
Ingesting, Storing, & Mining
Materials Data
Bryce Meredig & Greg Mulholland
Citrine Informatics
MRS Fall Meeting
2 December 2015

2. Introduction

3. About Citrine
Data platform for the physical
world—our software aggregates
and mines materials data to aid
R&D, mfg, sales

4. Business Model
We sell enterprise-­scale industrial
deployments of our platform
We don’t charge academic or
government labs for public data
storage or access

5. Bold Assertion #1
Our platform is a one-­line data
management plan for everyone
-­Funding agencies ask you to make data
accessible, but do not specify how
-­Anyone can store public data on our
platform for free, today

6. Bold Assertion #2
Public data should be free
and universally available

7. Bold Assertion #3
-­Funding agencies don’t want an
infrastructure mortgage
Scientists should focus on science,
not IT
-­Proliferation of unconnected data islands
doesn’t serve the community

8. We’re Nice, But Not a Charity
More data make our platform
smarter and more valuable
Users help us, and each other, by
curating and organizing data

9. Statistics on Citrination
Users from >1k institutions
3.1m materials data records
3.2k distinct datasets
150k documents

10. Ingesting Data:
Extraction from Documents

11. Platform Overview
Data extraction
pipeline turns
docs & files
into a structured
database
Structured data
are far more
discoverable,
and also
amenable to
machine learning

12. Data Structure Continuum
Numerical DataDocuments
(most materials-related arXiv papers indexed)
Completely unstructured Highly structured

13. Data Extraction: Text
cell parameter a = MATERIALS PROPERTY
5.82445(1) = NUMERICAL VALUE
angstrom = UNITS

14. Extraction: Images & Tables

15. Extraction: Images & Tables
Image containing data
Underlying x,y data
(actual extraction shown)
machine vision

16. Ingesting Data:
Community Contributions

17. Contributors & Partners

18. Uploading Data

19. Uploading Data
Ingestion is instant if you
create JSON or .csv files
-­see (citrination.com/contributing)
Otherwise, we figure it out!

20. Credit and Provenance
We acknowledge both the contributor (i.e., uploader) and
the published work via the DOI

21. Incentives: Vanity Metrics
Weekly pageviews of the OQMD paper’s page (does not
count individual datum views)—comparable to high-
impact journal metrics!

22. Incentives: Discoverability
Can scientists find your data via Google?

23. Why Upload?
Data management plan
Discoverability & impact
Persist your raw data

24. Ingesting Data:
Case Studies

25. Computational Data: OQMD
J.E. Saal et al., JOM 65, 1501 (2013)

26. Computational Data: Mat. Proj.
A. Jain et al., APL Materials 1, 011002 (2013)
B2O3 DOS

27. Experimental Data: JCAP
A. Shinde et al., J Mat Res 30, 442 (2014)

28. Data Partnerships
https://citrination.com/api/doi/banner/10.1016/j.jallcom.2014.11.091
Implemented an API for
Elsevier:

29. Data Partnerships
Link to Citrination
data will appear
here

30. Data Partnerships

31. Storing Data

32. Data Standards
MIF – Materials Information File:
General JSON schema for
defining materials data
Open standard and open-­source
tools for working with it

33. MIF Sample
Schema available:
http://citrineinformatics.github.io/mif-documentation
{
"sample": {
"material": {
"chemicalFormula": "LiF",
"condition": [
{
“scalar": [
{ "value": "Single crystalline" }
],
"name": "Crystallinity"
}
]…

34. Working with MIF
mifkit – open-­source Python
toolset for working with MIF
Create MIFs in your code, or
import MIFs from Citrination into
your code

35. Programmatic Data Access
# full documentation:
http://citrineinformatics.github.io/api-documentation
# search the entire database
client.search(formula='CrFeSn’)
# filter on values
client.search(formula=‘GaN’, property=‘band gap’,
max_measurement=‘3’)
# search a single data set
client.search(formula='CrFeSn', data_set_id=‘100’)
API usage example:

36. Data Quality and Curation
Our philosophy: We are not arbiters of data quality; instead,
we give the community tools to assess and discuss quality

37. Mining Data

38. Machine Learning: TE Case Study
Sparks, T. D., Gaultois, M. W., Oliynyk, A., Brgoch, J., & Meredig, B. “Data mining
our way to the next generation of thermoelectrics.” Scripta Materialia (2015)
ML-­based web app that predicts
key thermoelectric properties for
any bulk poly material
Heat map of thermal
conductivity predicted by
ML in Ru-­Dy-­Ge system

39. TE Search: All Ternary Systems

40. Industrial Applications
In production at several Fortune
500 corps and smaller co’s:
Coatings
Alloys
Crystallography
Energy materials

41. Releases Every ~2 Weeks
WebGL Crystal Structure
VisualizationsAutocomplete Profile Pages

42. Get Involved
• We’ll store your data today—easy
data management plan template
• Join Citrination newsletter:
bit.ly/1NGNgdb
• Access our public data
• Email me: bryce@citrine.io