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Citrine | DataManager

Scalable data management for chemicals, materials, ingredients and products.

What it is:

The Citrine DataManager (CDM) enables you to unify and leverage data across your enterprise. It is a chemistry-aware software toolkit to capture, enhance, analyze, visualize and communicate your data in a structured but flexible way. The starting point of your AI journey and the first way that you get value from the Citrine Platform.

How this benefits our customers:

Increased Productivity

  • Structure your data so that it can power AI
  • Reduce repeated experiments by making data findable, comparable and reusable across your business units
  • Enable knowledge transfer through visualizations

Improved Customer Responsiveness

  • Quickly find the product closest to your customer’s requirements
  • Visualize how your product compares to those of your competitors
  • Streamline inventories and work around supply issues
Find a material through searching, filtering and ranking

How it is different from other options:

Unlike legacy data management solutions, Citrine Data Manager (CDM) was specifically designed for product experts wanting to use AI on complex products where materials, chemicals or ingredients are a key part of their competitive advantage.

Product experts need

  • An easy way to digitize data and check its quality
  • A data structure that grows as they learn more about their project, or they want to join datasets along a value chain
  • A common vocabulary, so that teams can speak different languages and use different units, but still work with each other’s data
  • The ability to systematically specify all steps in a material’s processing history, including processing conditions and batch measurements.
  • Intuitive visualizations to communicate their work, learn from others, and assess data quality
  • A secure way to store and share data
  • A place where experimental parameters, formulations and end product measured properties come together to power AI

How it works:

Get data into the Citrine Platform easily

Data can be ingested quickly via CSV, Excel or directly using our API. CDM includes a flexible Python interface to automate data ingestion.

Data ingestion

CDM streamlines importing data from common instrument files, e.g. HPLC. Citrine Professional Services can help you get almost any data AI-ready. Our team of experts uses a combination of manual and programmatic techniques to clean and structure your data.

Increase and improve data automatically

CDM’s descriptor libraries convert information like chemical formulas and SMILES to relevant features automatically enhancing the dataset that can be used in AI.


Plot chemical property features against your target properties to understand the underlying fundamentals of your project

Check data quality through visualizations

Researchers can inspect the data using visualizations to spot any outliers so they can clean and correct data if needed.

outliers highlighted

Filter data from across your team and analyze its spread

In Citrine's Data Manager, you can easily add a new object, property, relationship, or a whole new dataset as you integrate along the value chain.

Data model that doesn’t turn data away

Our open-source materials data model (Graphical Expression of Materials Data, or GEMD) is at the core of CDM. It enables customers to add and remove properties, relationships and related data sets with ease. This means that unlike, standard relational database it never turns away data.

The platform ensures that data is comparable across a team. While each team member can use the name and units they are used to, a template can join comparable data together and convert units so that all the data can be easily analyzed and visualized and used to power AI. This is particularly useful for companies with business units in different countries.
temperature units

 

Data model that fully captures the context

Each step from procurement to final product is visualized in the material history and made available to AI models. Product experts can learn from these diagrams and can click to uncover details.In this example, each stage is recorded, including, both the specified processing parameters (e.g., 200°C) and the actual measured parameter in that run (e.g., 199°C). The color-coded graphical user interface is easy to use, and users can also use the Python client to add, review, and revise data. This rich data model enables AI access to all aspects of a material’s history that will affect its final properties.

Visualization and reporting

Finally, data can tell a story. Visualization tools and custom reports, enable teams to communicate their progress to managers and customers.

Create, save and share visualizations in a report
See when data is uploaded and where it is used

Information Security:

The data is secure in the platform and access to team data and specific projects is controlled by an authorization system. Learn more about how Citrine take security seriously here.

FAQs

  • What types of data are used in materials informatics?

    Data used in materials informatics can include experimental measurements, computational simulation results, images (such as micrographs), text (such as research articles and datasheets), and other forms of structured and unstructured data related to materials and chemicals.

  • How much data do you need to get started with AI for materials and chemicals?

    While it is true that more data is better, it is more important that the data is diverse, showing failures as well as successes and covering the whole search space. We can start using AI with 12 data points, 30 data points is a solid start, 100 data points is great. (A data point = an experiment (formulation data + processing parameters + final material properties).

  • How does Citrine keep customer data secure?

    Citrine Informatics is ISO 270001 accredited. You can read about data security in detail here. In summary, each customer has their own encrypted instance of the platform, and only they can access their data and their models. Data and models are not shared across customers and even Citrine employees need permission from customers to access their data when helping them on projects.

  • How does materials informatics handle the complexity of materials data?

    Feature engineering is used to distill complex data into manageable and insightful forms. Feature engineering is the process of deciding which bits of information could be useful to a model in predicting the properties of interest and getting them into a format that maximizes a model’s ability to learn. Domain knowledge is crucial for this process. While many features in materials and chemicals can be generated quite easily by converting molecular structures and chemical formulas into more detailed representations, there are often nuances that are best leveraged by letting domain experts (those that know their process better than anyone) break down what’s actually important. It is also key to have systems that can record all the conditions at play during sample preparation and record process steps and their parameters as well as ingredients and their amounts.

  • What is featurization?

    Featurization is the process of transforming raw data into a format that can be used by machine learning models. This involves extracting meaningful features or characteristics from the data that capture the important aspects needed to predict a property of interest. In the context of materials science or chemistry, featurization can involve the process of converting molecular structures into data such as the molecular weight and number of hydrogen bonds, or distilling charts into data representing features such as peak values.

  • What are SMILES and InCHI, and how are they used on the Citrine Platform?

    SMILES (Simplified Molecular Input Line Entry System) and InCHI (International Chemical Identifier) are notations used to represent the structure of chemical molecules in text format. It provides a way to encode molecular structures using short ASCII strings, which makes it convenient for inputting chemical information. In the Citrine Platform these are automatically converted into extra data about the molecule to feed into the AI model. For example, if you know the SMILES string you can automatically calculate the number of hydrogen bonds. The platform automatically calculates 127 different features of each molecule that are relevant across different domains. These include surface properties, reactivity, polarizability etc.

  • What is data ingestion?

    Data ingestion refers to the process of collecting, importing, and processing data for use in a data system or application. In the case of the Citrine Platform this can be done by uploading an excel spreadsheet or by creating data pipelines directly from your data systems.

Learn more:

White Paper

Data Management on the Citrine Platform

Data Management on the Citrine Platform

Understand the data landscape and get tips on data management best practices. Learn how to upload your data to the Citrine Platform and how to prioritize data digitization.

White Paper

Advanced Data Preparation Services

Advanced Data Preparation Services

Short overview of the data preparation services we offer and how they add value to customers.

White Paper

Integration with the Citrine Platform

Integration with the Citrine Platform

Overview of how the Citrine Platform fits in to your wider data ecosystem.

See for yourself: