The World Leader in A.I. Powered Materials Informatics

Citrine is the artificial intelligence platform for materials and chemicals, designed to accelerate the world’s leading material product companies millions of dollars in their development and design processes. The Citrination Platform ingests large-scale data from countless sources to create the world’s largest single database of materials and chemicals information and uses state-of-the-art AI to guide the development and applications matching of next generation advanced materials faster than ever before. This predictive intelligence helps Global 1000 companies hit R&D and manufacturing milestones 2 to 5 times faster, while delivering higher quality engineered products to the market.

Science!

R&D Scientists and Engineers

  • Product Design – Know instantly how candidate materials will behave under any conditions
  • Research – Predict the performance of new materials, chemicals, and formulations, optimizing dozens of properties simultaneously
PM

Production Managers

  • Lifetime – Analyze historical production data to understand how changes in your process affect product aging and failure behavior
  • Yield & Q.A. – Increase yield and manage defects by leveraging patterns in underlying physical data about your products
Growth

Material Sales and Growth Teams

  • New Leads – Tap new markets and use cases by making surprising connections between customer requirements and your products
  • Customer Analytics – Know more than ever before about what potential customers need to see to make their purchasing decision

Our Products

The Citrination platform is the world’s most-used cloud-based materials and chemical data infrastructure, with millions of records and multi-user deployments at national laboratories and Global 1000 manufacturing companies. Our platform continually ingests and understands large-scale data from countless sources, such as research papers, characterization tools, simulations, and existing databases, and uses state-of-the-art machine learning to anticipate the behavior of all materials and products under any relevant conditions.

Citrine is backed by top-tier Silicon Valley venture capital firms, including Eric Schmidt’s fund Innovation Endeavors, Data Collective, Prelude Ventures, AME Cloud Ventures, Morado Ventures, and XSeed Capital.

Citrination and Data Catalogue

  • Open Citrination has ingested over 40 million property-structure-process relationships: the world's largest materials database
  • Open Citrination data constantly grows, with every experiment and simulation run by the community

Materials Data Infrastructure

  • Store and search experimental and computational data about physical systems, ranging from atomistic data on materials to device- or part-scale performance
  • Integrate diverse data from existing databases and infrastructures into a single repository
  • Data accessible via graphical web interface and API
  • Citrine and our user community are developing a constantly-growing set of data importers for a variety of tools and simulations, including DFT codes (VASP, Quantum Espresso), CALPHAD tools (Computherm, Thermocalc), and experimental measurement systems

Artificial Intelligence Engines

  • Citrine's platform trains artificial intelligence systems on ingested materials, device, and product datasets—taking advantage of both data and known underlying physics and chemistry
  • Inverse design: simultaneously recognize the trade-offs between various properties, while identifying complicated trends in the data, to design the right material
  • With each step in the product development cycle, Citrine's platform learns and improves constantly making the development process more predictable and efficient
  • Citrine's developer program allows third-party research groups to integrate analysis software within our platform, for example: COMBO Bayesian optimization package from University of Tokyo

Our Partners

DARPA ARM Adapt AMES
NIST NREL Elsevier Berkeley QuesTek PRISMS

Citrine in Papers

Papers by Citrine Authors

Beyond bulk single crystals: A data format for all materials structure–property–processing relationships

Michel, K., and Meredig, B. (2016)
MRS Bulletin,41(08), 617-623 [invited paper]

Materials Data Infrastructure: A Case Study of the Citrination Platform to Examine Data Import, Storage, and Access

O’Mara, J., Meredig, B., and Michel, K. (2016)
JOM, 68(8), 2031-2034 [invited paper]

Materials science with large-scale data and informatics: Unlocking new opportunities

Hill, J., Mulholland, G., Persson, K., Seshadri, R., Wolverton, C., and Meredig, B. (2016)
MRS Bulletin 41(05), 399-409 [Technical Feature]

Materials informatics across the product lifecycle: Selection, manufacturing, and certification

Mulholland, G.J. and Paradiso, S.P. (2016)
APL Materials [invited paper]

Data mining our way to the next generation of thermoelectrics

Sparks, T. D., Gaultois, M. W., Oliynyk, A., Brgoch, J., and Meredig, B. (2016)
Scripta Materialia, 111, 10-15 [invited paper]

Industrial materials informatics: analyzing large-scale data to solve applied problems in R&D, manufacturing, and supply chain

Meredig, B. (2016)
COSSMS [invited paper, submitted]

Web-based machine learning models for real-time screening of thermoelectric materials properties

Gaultois, M. W., Oliynyk, A. O., Mar, A., Sparks, T. D., Mulholland, G. J., and Meredig, B. (2016)
APL Materials 4(053213) [Featured Article for the Materials Genome special topic issue]

Understanding Thermoelectric Properties from High-Throughput Calculations: Trends, Insights, and Comparisons with Experiment

Chen, W., Pohls, J. H., Hautier, G., Broberg, D., Bajaj, S., Aydemir, U., Gibbs, Z.M., Zhu, H., Asta, M., Snyder, G.J., Meredig, B., White, M.A., Persson, K., and Jain, A. (2016)
Journal of Materials Chemistry C

Papers Mentioning Citrine

Role of materials data science and informatics in accelerated materials innovation

Kalidindi, S. R., Brough, D. B., Li, S., Cecen, A., Blekh, A. L., Congo, F. Y. P., & Campbell, C. (2016)
MRS Bulletin, 41(08), 596-602

The materials innovation ecosystem: A key enabler for the Materials Genome Initiative

McDowell, D. L., & Kalidindi, S. R. (2016)
MRS Bulletin, 41(04), 326-337

Perspective: Interactive material property databases through aggregation of literature data

Seshadri, R., & Sparks, T. D. (2016)
APL Materials,4(5), 053206

New opportunities for materials informatics: Resources and data mining techniques for uncovering hidden relationships

Jain, A., Hautier, G., Ong, S.P., & Persson, K.
Journal of Materials Research 31(08), 977-994

Semi-Supervised Approach to Phase Identification from Combinatorial Sample Diffraction Patterns

Bunn, J. K., Hu, J., & Hattrick-Simpers, J. R. (2016)
JOM, 68(8), 2116-2125

Research Update: The materials genome initiative: Data sharing and the impact of collaborative ab initio databases

Jain, A., Persson, K. A., & Ceder, G. (2016)
APL Materials, 4(5), 053102

Perspective: NanoMine: A material genome approach for polymer nanocomposites analysis and design

Zhao, H., Li, X., Zhang, Y., Schadler, L. S., Chen, W., & Brinson, L. C. (2016)
APL Materials, 4(5), 053204

The Materials Data Facility: Data Services to Advance Materials Science Research

Blaiszik, B., Chard, K., Pruyne, J., Ananthakrishnan, R., Tuecke, S., & Foster, I. (2016)
JOM, 68(8), 2045-2052

Development of solar fuels photoanodes through combinatorial integration of Ni–La–Co–Ce oxide catalysts on BiVO4

Guevarra, D., Shinde, A., Suram, S. K., Sharp, I. D., Toma, F. M., Haber, J. A., & Gregoire, J. M. (2016)
Energy & Environmental Science

Citrine Community

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Public Data in the Citrination Repository

Citrine makes millions of materials structure-process-property relationships available for free to the research community.

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Materials Science Newsletter

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