TECH OFFER

AI Technology to Accelerate Chemistry and Materials R&D through Reduction of Experiments

KEY INFORMATION

TECHNOLOGY CATEGORY:
Infocomm - Big Data, Data Analytics, Data Mining & Data Visualisation
Infocomm - Artificial Intelligence
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TECHNOLOGY READINESS LEVEL (TRL):
LOCATION:
France
ID NUMBER:
TO174080

TECHNOLOGY OVERVIEW

The company has developed an advanced artificial intelligence (AI)-based platform to accelerate R&D projects in Chemistry and Materials industries.

The technology is able to learn complex relationships from clients' historical R&D data and produce recommendations for the best experiments to perform, in order to develop products that satisfy requirements and to optimize chemical reactions.

The development of new molecules, materials, formulations or chemical reactions requires many trial-and-error experiments and informed guesswork.

Their AI technology helps clients reduce number of experiments to a minimum, thereby saving time and money (by cutting down on manpower spent and resource wastage).

Adopters of the technology can test their experiment ideas with the company's software before running the experiments in the laboratory. This means that clients can make a decision to run the most promising experiments and save time. This also allows clients to be faster than their competitors to develop products in order to satisfy the needs of their customers.

TECHNOLOGY FEATURES & SPECIFICATIONS

The technology platform learns from clients' R&D data and allows them to:

  • Predict the properties of molecules, materials and formulations,
  • Suggest the most relevant experiments to perform in order to obtain molecules, materials or formulations that meet pre-defined specifications.

The proprietary AI algorithms can learn relationships of any complexity from clients' experiments, even when the underlying physics is not well-understood. It will benefit adopters of the technology even in complex R&D subjects.

The platform leverages both R&D data and domain expertise to allow clients to make more informed decisions for their R&D projects.

The platform is easy to use, even for people who are not trained in machine learning. However, if clients do have machine learning skills, the software allows them to tune the algorithms it uses.

It is continuously updated and upgraded in order to benefit from the latest advances in artificial intelligence.

POTENTIAL APPLICATIONS

Whether clients develop molecules, materials or formulations, the software can accelerate their R&D workflows.

For Materials experiments:

  • Predict materials properties
  • Design materials that possess desired properties
  • Optimize the synthesis and processing of materials

For Molecules experiments:

  • Predict molecules properties
  • Predict the outcome of a reaction
  • Optimize the synthesis of a molecule

For experiments related to development of formulations:

  • Predict formulations properties
  • Design and optimize formulations (composition and process parameters) that possess desired properties

Market Trends & Opportunities

The technology is available for adoption via subscription basis.

The company is also open to co-development opportunities such as:

  • Development of tailored software modules that will extend their base platform in order to perfectly suit organisation-specific use cases and proprietary data
  • Integration of the discovery technologies into existing R&D software ecosystem or digital infrastructures through API calls

The company is also open to explore other collaboration and business arrangements, to enable clients to innovate and accelerate their R&D processes.

Benefits

The company has on-going projects with clients from the industry and academic sectors.

Past studies have demonstrated that their technology can provide for a reduction of 25% to 80% of the number of experiments needed to develop products or chemical reactions.

This technology may help adopters break past their traditional barrier of experimentation and support them in generating new insights and value from their existing data (experiment results, laboratory notebooks, databases, process data, analytical results, etc.), even data from "failed" experiments.

A demo is available upon introduction and request.

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