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An Industrial Perspective on FAIR Data and Machine Learning for Materials
Presented 3-5 June 2020 @ FAIR Data Infrastructure and Materials Genomics, organized by FAIR Data Infrastructure for Physics, Chemistry, Materials Science, and Astronomy e.V.
Conference Presentations
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Reduced Costs and Increased Supply Resilience
Learn how Citrine's Artificial Intelligence platform can be used to reduce costs and increase supply chain resilience in specialty chemicals.
Case Studies, Polymers and Plastics, All
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Reduce Costs, Increase Resilience with AI – Applications in Formulations
Learn how global leaders in specialty chemicals use the Citrine Platform to develop formulations that meet customer performance requirements with lower-cost ingredients, and adapt existing formulations when faced with...
The Business Case, Webinars, All Webinars
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Increased Customer Responsiveness
Learn how Citrine's Artificial Intelligence platform improves customer responsiveness in the formulations space.
Case Studies, Polymers and Plastics, All
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Better tailor-made formulations, quicker
Learn how Citrine's Artificial Intelligence platform enabled a 21% improvement in polymer mechanical properties in just 6 months. 
Case Studies, Polymers and Plastics, All
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Data-Driven Optimization in Additive Manufacturing
Learn how ADAPT and its partners used the Citrine Platform to create a new, additively manufactured door hinge for MRAP light tactical vehicles.
Case Studies, Metals and Glass, All
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Design space visualization for guiding investments in biodegradable and sustainably sourced materials
In many materials development projects, scientists and research heads make decisions to guide the project direction. For example, scientists may decide which processing steps to use, what elements to...
Papers By Citrine
Assessing the Frontier: Active Learning, Model Accuracy, and Multi objective Materials Discovery and Optimization
Discovering novel materials can be greatly accelerated by iterative machine learning-informed proposal of candidates—active learning. However, standard global error metrics for model quality are not predictive of discovery performance,...
Papers By Citrine
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Machine Learning accelerates research
Learn how data-driven assessments of research portfolio drive R&D strategy at glass manufacturer.
Case Studies, Metals and Glass, All
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New class of thermoelectric materials discovered
Learn how machine learning model made available to research community discovered new classes of thermoelectrics and helped thousands of projects.
Case Studies, Functional Materials, All