Citrination:  Open  Infrastructure  for  Ingesting,  Storing,  &  Mining

Materials  Data

Bryce Meredig & Greg MulhollandCitrine Informatics

MRS Fall Meeting2 December 2015

Introduction

About  Citrine

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

Business  ModelWe  sell  enterprise-­scale  industrial  deployments  of  our  platform

We  don’t  charge  academic  or  government  labs  for  public  data  storage  or  access

Bold  Assertion  #1Our  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

Bold  Assertion  #2

Public  data  should  be  free  and  universally  available

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

We’re  Nice,  But  Not  a  CharityMore  data  make  our  platform  smarter  and  more  valuableUsers  help  us,  and  each  other,  by  curating  and  organizing  data

Statistics  on  Citrination

Users  from  >1k  institutions

3.1m  materials  data  records3.2k  distinct  datasets150k  documents

Ingesting  Data:Extraction  from  Documents

Platform Overview

Data extraction pipeline turns docs & files into a structured database

Structured data are far more discoverable, and also amenable to machine learning

Data Structure ContinuumNumerical DataDocuments

(most materials-related arXiv papers indexed)

Completely unstructured Highly structured

Data Extraction: Text

cell parameter a = MATERIALS PROPERTY5.82445(1) = NUMERICAL VALUEangstrom = UNITS

Extraction: Images & Tables

Extraction: Images & Tables

Image containing dataUnderlying x,y data

(actual extraction shown)

machine vision

Ingesting  Data:Community  Contributions

Contributors & Partners

Uploading Data

Uploading  Data

Ingestion  is  instant  if  you  create  JSON  or  .csv  files-­see  (citrination.com/contributing)

Otherwise,  we  figure  it  out!

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

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

Incentives: DiscoverabilityCan scientists find your data via Google?

Why  Upload?

Data  management  planDiscoverability  &  impactPersist  your  raw  data

Ingesting  Data:Case  Studies

Computational Data: OQMD

J.E. Saal et al., JOM 65, 1501 (2013)

Computational Data: Mat. Proj.

A. Jain et al., APL Materials 1, 011002 (2013)

B2O3 DOS

Experimental Data: JCAP

A. Shinde et al., J Mat Res 30, 442 (2014)

Data  Partnerships

https://citrination.com/api/doi/banner/10.1016/j.jallcom.2014.11.091

Implemented  an  API  for  Elsevier:

Data Partnerships

Link to Citrinationdata will appear here

Data Partnerships

Storing  Data

Data  StandardsMIF  – Materials  Information  File:  General  JSON  schema  for  defining  materials  data

Open  standard  and  open-­source  tools  for  working  with  it

MIF  SampleSchema available: http://citrineinformatics.github.io/mif-documentation{ "sample": {

"material": { "chemicalFormula": "LiF", "condition": [ { “scalar": [

{ "value": "Single crystalline" } ],

"name": "Crystallinity" }

]…

Working  with  MIFmifkit – open-­source  Python  toolset  for  working  with  MIFCreate  MIFs  in  your  code,  or  import  MIFs  from  Citrination into  your  code

Programmatic  Data  Access

# full documentation: http://citrineinformatics.github.io/api-documentation# search the entire databaseclient.search(formula='CrFeSn’)# filter on valuesclient.search(formula=‘GaN’, property=‘band gap’, max_measurement=‘3’)# search a single data setclient.search(formula='CrFeSn', data_set_id=‘100’)

API  usage  example:

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

Mining  Data

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

TE Search: All Ternary Systems

Industrial  ApplicationsIn  production  at  several  Fortune  500  corps  and  smaller  co’s:CoatingsAlloysCrystallographyEnergy  materials

Releases Every ~2 WeeksWebGL Crystal Structure

VisualizationsAutocomplete Profile Pages

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