Citrine Informatics

Canvas Category Software : Information Technology : Chemical

Website | Blog | LinkedIn

Primary Location Redwood City, California, United States

Citrine Informatics was founded in 2013 and since then has been laser focussed on AI for Materials and Chemicals. Working on projects across material types, formulations, specialty, and commodity chemicals; partnering with commercial, government and academic organizations, Citrine has gained a reputation as the world leader in this field. Awards from CB Insights, the World Economic Foundation, and the Clean Tech group, along with Series B funding from Prelude Ventures and Innovation Endeavors amongst others, and 3 technology patents have further secured Citrine’s position.

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Leveraging Data for Growth

📅 Date:

✍️ Author: Tony Maiorana

🔖 Topics: Machine Learning

🏭 Vertical: Chemical

🏢 Organizations: Citrine Informatics

Citrine offers an AI platform designed to enable chemists and materials scientists to develop better products in less time. In big tech companies, data is abundant and there are armies of data scientists to use it primarily because software margins are huge, and these companies have been growing like crazy (maybe not forever). Chemical companies are very different. Data is relatively scarce because experiments take time to conduct, and you need lab space and the people doing the experiments are doing more than just product development. They are supporting the existing business. Citrine essentially allows R&D people to become data scientists through a no-code platform.

Citrine Informatics enables you to not hire a data scientist or two and instead allows someone like me (not a data scientist) to build my own models for whatever system I’m working on. By working on the model yourself, instead of through a data scientist, you can incorporate your expertise directly and iterate quickly. In polymeric products where formulation is essential for product development, like polyurethane foams or waterborne emulsions, I think this approach is the way.

Read more at The Polymerist

Advancements in Predicting the Fatigue Lifetime of Structural Adhesive Joints

📅 Date:

🔖 Topics: Machine Learning, Physics-informed neural network

🏢 Organizations: Citrine Informatics, Siemens, Fraunhofer IFAM

While physics-based models offer the highest accuracy for analyzing these joints, they require meticulous parameter calibration for every new adhesive. For example, consider a fatigue test on a structural adhesive joint with 10 million cycles at a frequency of 10 Hz. These tests are demanding and time-consuming, taking over 10 days to complete. Adding to the challenge is the need for numerous data points to construct a comprehensive fatigue design curve, a fundamental aspect of structural analysis. Given the need to optimize both efficiency and accuracy, engineers and researchers need and pursue innovative solutions.

One path to solution is the integration of Artificial Intelligence (AI) and Machine Learning (ML) into materials science. Recognized for its ability to address complex problems through learning from existing knowledge, AI provides a promising avenue for structural modeling by generating mathematical expressions that capture the interplay of various parameters. We expect that this rationale also applies to the structural modelling of the fatigue behavior of structural adhesive joints, which is the subject of our ongoing research.

This showcase exemplifies our commitment to revolutionizing materials selection and fatigue life prediction for adhesive joints. Leveraging the Citrine Platform [2], we seamlessly apply machine learning methods to integrate experimental datasets with physics-based modeling (based on stress concentration factors). This innovative approach not only significantly elevates the precision of fatigue predictions but also enables the precise selection of optimal adhesives for bonded structures, factoring in various material and geometrical properties, as well as usage conditions.

Read more at Citrine Blog

Closed-loop fully-automated frameworks for accelerating materials discovery

📅 Date:

🔖 Topics: Machine Learning, Materials Science

🏢 Organizations: Citrine Informatics, Carnegie Mellon, MIT

Our work shows that a fully-automated closed-loop framework driven by sequential learning can accelerate the discovery of materials by up to 10-25x (or a reduction in design time by 90-95%) when compared to traditional approaches. We show that such closed-loop frameworks can lead to enormous improvement in researcher productivity in addition to reducing overall project costs. Overall, these findings present a clear value proposition for investing in closed-loop frameworks and sequential learning in materials discovery and design enterprises.

Read more at Citrine Informatics Blog

Citrine Informatics Raises $16M in Series C Financing

📅 Date:

🔖 Topics: Funding Event

🏢 Organizations: Citrine Informatics, Prelude Ventures, Innovation Endeavors

Citrine Informatics, the leading provider of artificial intelligence software for materials, chemicals, and manufactured product development, announced the successful close of a $16 million Series C funding round. The round was led by Prelude Ventures and Innovation Endeavors, with participation from Drive Catalyst (Far Eastern Group), Alumni Ventures, ISAI Cap Venture, Presidio Ventures, and others.

This latest round of funding will be used to further accelerate the growth and development of Citrine’s AI-driven materials and chemical design platform, which is already in use by leading companies across materials, chemicals, formulated products, and manufacturing industries to improve the efficiency and effectiveness of their product development processes.

Read more at Businesswire

Citrine Informatics Raises $20M in Series B Funding

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🏢 Organizations: Citrine Informatics, Prelude Ventures, Innovation Endeavors

Citrine Informatics, the developer of the first materials artificial intelligence (AI) operating platform, announced $20M in Series B funding. The round was co-led by Prelude Ventures and Innovation Endeavors with participation from Moore Strategic Ventures, Next47, and other investors. The financing will help support commercial growth of the Citrine Platform, the company’s core product, as well as the expansion in business development and sales efforts in the United States, Europe, and Asia.

The technological innovation behind the Citrine Platform dramatically reduces the time and cost of materials and chemicals development by combining materials-specific data handling and storage, scalable compute for AI and data processing, along with modular composable AI units optimized to predict materials behavior. The Citrine Platform has helped some of the world’s largest organizations, including chemicals and product companies, hit overall R&D milestones in 50-70% percent of the time originally forecast and has enabled totally new materials product lines.

Read more at Citrine Media

Citrine Informatics Raises $7.6M to Revolutionize Materials-Driven Product Development With AI

📅 Date:

🏢 Organizations: Citrine Informatics, Innovation Endeavors, DCVC, Prelude Ventures

Citrine Informatics, the chemicals and materials artificial intelligence (AI) platform, announced it has closed a $7.6 million Series A led by Eric Schmidt’s Innovation Endeavors, DCVC (Data Collective), and Prelude Ventures. Jerry Yang’s AME Cloud Ventures and XSeed Capital also joined the round. Citrine combines AI with the world’s largest materials database to help bring products to market faster. The company will use the funding to accelerate its early successes with Fortune 1000 customers.

Factories are getting smarter, yet the chemicals and alloys that produce cutting-edge products remain largely unchanged. The consequence of this mismatch is painful delay and cost to manufacturers. According to the National Academy of Sciences, new products can take two decades or more to go from initial design to market, with the intermediate step of developing new materials often taking the longest. Citrine dramatically reduces the time and cost of this process by providing a data-driven approach to predict and optimize materials behavior.

Read more at Citrine Media