Machine Learning Accelerates Materials Development
Learn how Citrine's platform supported Panasonic as it developed new organic semiconductors.
What is Materials and Chemicals Informatics?
The application of data science, materials science, and machine learning to the materials and chemicals space.
What Can Materials Informatics Achieve?
Leveraging AI in material and chemical industries enables you to:
- Discover and develop new materials, chemicals, or formulations to gain market share or penetrate new markets.
- Reduce development time to better serve customers and win on responsiveness.
- Optimize inputs and processes to reduce COGS.
- Guide product development and research portfolio strategy to use resources efficiently and improve your project success rate.
What is Machine Learning?
Machine Learning, a subset of AI, is ideally suited to tackle problems with many inputs (ingredients, raw materials, processing parameters, etc.) and many outputs (target performance properties, cost, sustainability, etc.) which are ubiquitous in our industry.
Data Requirements for Using Machine Learning in Materials Informatics
Data in our industry is a mess. In Citrine’s 10-year history, we have not yet met a customer that is happy with their own data quantity, quality, and organization. The good news is, you need very little data to begin to design new candidate materials or chemicals and drive value with AI. Typical projects begin with tens of data points and we’ve seen successful projects start with fewer than 10 initial data points. Solid AI models will then guide the priority of new experimentation or simulation as well as prioritize the organization of past data. An all-too-common mistake we have seen in our industry is the well-intentioned initiative to “get our data house in order” or “clean up all of our data” prior to applying AI. Don’t boil the ocean. And don’t waste time “cleaning” data until you know 1) what business value doing so will drive and 2) how to effectively prepare such data to be optimally leveraged with AI.
What is Sequential Learning?
Sequential learning in materials informatics is the iterative process of designing candidate materials or chemicals, learning from experimentation or simulation, and adapting to improve further rounds of design. AI models are continually refined based on new data, allowing for efficient adaptation over time. This approach enables researchers to:
- Reduce the time to discover or develop new products by 50-90%
- Make informed decisions by leveraging previous knowledge and data from across the organization, and incorporating new experimental results.
- Adopt a repeatable framework that maximizes efficiency, accelerates discovery, and facilitates the development of novel materials and improved processes
Benefits of Implementing Materials Informatics
When you have a strong technology partner facilitating your research strategy you can make dramatic changes in your organization. For most customers, it means getting to market faster, winning market share, reducing COGS, optimizing product development with a higher project win rate and fewer wasted experiments, improving resilience to supply chain and regulatory disruptions, and onboarding new personnel more quickly and efficiently.
The Citrine Platform makes it possible to use data to further your most ambitious innovation projects as well as your most demanding customers’ (immediate) application engineering challenges.
Case Studies
First to Market – High-strength 3D Printable Aluminum Alloy
Learn how HRL reduced development time from years to days using the Citrine Platform.
Rapidly Screen Polymers Using AI
Learn how Citrine's AI Platform was used to rapidly screen 2500+ polymers in just 5 months. See how a new workflow was developed where researchers can now set a target and immediately receive a list of the top 10 polymers most likely to hit the target.