Home / Platform / Citrine VirtualLab

Citrine | VirtualLab

A powerful AI system that lets your researchers find better results more quickly, by conducting virtual experiments before they go into the lab

What it is:
Citrine VirtualLab (CVL) refers to a set of generative AI tools that allow companies to rapidly identify chemical formulations and materials that are likely to match a set of pre-defined specifications.
Why this matters to our customers:
We have designed Citrine VirtualLab to apply cutting-edge machine learning techniques to the materials and chemicals domain. With the Citrine Platform, you are always in control. Our approach to AI is to make it as easy as possible to develop and review proposed solutions, so your teams can focus on finding results that work for your business.

Working with Small Data Sets
The Citrine Platform was designed from the outset to work well on the small, sparse data that materials companies usually have. We’ve tackled projects with fewer than 30 initial data points.

Using Your Experts’ Domain Knowledge
A graphical model enables researchers to see how components fit together and facilitates the integration of expert domain knowledge. The machine learning models fill in gaps in knowledge, not in relearning the basics.

Reusable Components
Once built, each component can be used as part of other AI and machine learning projects in your company, contributing to a library of codified domain knowledge that researchers across the team can use.

Sequential Learning Workflows
The Citrine Platform powers the next generation of experiment design. Using iterative, proprietary calculations that factor in uncertainty, you can efficiently and systematically see how each of your desired parameters might perform.

What it replaces:
Typically, chemists and product engineers have relied on intuition and lengthy physical experimentation to create new features. CVL uses artificial intelligence to virtually combine thousands of possible combinations of ingredients and processes to identify promising combinations with the features that your customers want. CVL enables you to capture the greatest value from your own intellectual property.
How it works:
At the simplest level, you create a new AI model with checkboxes to choose the inputs you’ll consider, and a drop-down box to choose the property you want the model to predict. You can also use our graphical AI model builder to add expressions, processors, and even configurable features on chemical formula data (featurizers).
You can use sliders to select upper and lower bounds for ingredient and processing parameters.
no code search space definition
The CVL AI engine rapidly synthesizes hundreds or thousands of experiments based on your inputs, and presents you with easy-to-use visualizations to quickly review your results and generate value more quickly. With our graphical AI Model builder, for example, the team can see how pieces of the model fit together.
Our “Feature Importance” list lets you see which inputs are having the biggest impact on the predictions of the model. You can use this to clarify your approach and reweight inputs as necessary.
CVL shows you results with visualizations that are easy to understand and assess. Candidate materials can be filtered, color-coded, labeled, and compared. Clear charts show how candidates compare to targets and to existing materials. Researchers can use these charts to communicate progress and collaborate within a team and across your company.
The Citrine Platform includes a Python API as you roll out AI for materials and chemicals across your organization. The API lets Python users structure, reuse, automate and share interaction pathways, enabling efficient deployment of AI at scale.
Learn more:

Case Study

Machine Learning accelerates Materials Development

Machine Learning Accelerates Materials Development

Learn how Citrine’s platform supported Panasonic as it developed new organic semiconductors.

Case Study

Picture of 3D paining Aluminum Alloy Powder developed using AI by HRL laboratories

First-to-market High Strength 3D Printable Aluminum Alloy

Explore how HRL reduced development time from years to days using the Citrine Platform.

Case Study

Rapidly Screen Polymers Using AI

Discover how CVM screened 2500+ polymers in only 5 months. Researchers now know the most promising 10 polymers for a given target.

See for yourself: