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What is Citrine?

The Citrine Platform seamlessly combines cutting-edge AI innovations with sophisticated data management frameworks, delivering intuitive user experiences and strong security protocols that adhere to industry regulations, all while being securely hosted in the cloud. It proficiently captures, organizes, and maintains extensive information related to the lifecycle of materials and chemicals, covering everything from procurement to processing and characterization. By reducing the need for redundant experiments, it enables users to quickly retrieve relevant data sets. With its advanced AI capabilities, the Citrine Platform significantly speeds up the discovery of high-performing materials. Its predictive models evaluate materials' performance based on factors such as processing, composition, and synthesis details, guiding users on the most effective experiments to pursue to achieve their goals. Additionally, the Citrine Platform upholds the integrity and confidentiality of your data, domain expertise, and models through rigorous security measures. Backed by ISO27001 certification and thorough documentation, it offers further validation of its dedication to security and best practices. This meticulous approach and commitment to meeting user requirements render the Citrine Platform an essential asset for those in the materials science sector, facilitating innovation and efficiency in research and development processes.

What is Atinary SDLabs Platform?

Atinary's Self-Driving Labs (SDLabs) platform provides a no-code approach for artificial intelligence and machine learning, designed to revolutionize research and development workflows by enabling traditional laboratories to transition from manual experiments to fully autonomous experimentation. This innovative platform bolsters the design and improvement of experiments through a robust closed-loop system that integrates AI-generated hypotheses, predictions, and decision-making processes. Among its key functionalities are multi-objective optimization, effective database management, seamless workflow orchestration, and immediate data analysis. Users can define experimental parameters with specific limitations, allowing machine learning algorithms to guide the subsequent phases of the process, carry out experiments either manually or with robotic assistance, evaluate results, and refresh models with the most recent data, thereby accelerating the journey toward more efficient, cost-effective, and environmentally sustainable products. Furthermore, Atinary provides exclusive algorithms such as Emmental for addressing non-linear constrained optimization challenges, SeMOpt for facilitating transfer learning within Bayesian optimization, and Falcon, all of which significantly boost the platform's capabilities and performance. By utilizing these sophisticated tools, researchers are empowered to enhance their experimental workflows, fostering greater efficiency and driving innovation in their fields. Ultimately, the SDLabs platform represents a transformative shift in how laboratories approach experimentation, paving the way for groundbreaking discoveries.

Media

Media

Integrations Supported

Additional information not provided

Integrations Supported

Additional information not provided

API Availability

API Availability

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Standard Support
Web-Based Support

Training Options

Documentation Hub
Online Training

Training Options

Documentation Hub
On-Site Training

Company Facts

Organization Name

Citrine Informatics

Date Founded

2013

Company Location

United States

Company Website

citrine.io/product/what-is-the-citrine-platform/

Company Facts

Organization Name

Atinary

Date Founded

2019

Company Location

Switzerland

Company Website

atinary.com/solutions/

Categories and Features

Data Management

Not specified

Materials Science

Not specified

Categories and Features

Materials Science

Not specified

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