Data Driven Material Discovery System

Systems and methods for optimizing the formulation of materials are provided. The systems and methods employ a data-driven, iterative approach to derivate optimal material formulations. One portion of the system includes a sample automation system that outputs the material samples to be tested, and a second portion of the system includes an optimization engine that analyzes data extracted from the material samples and generates additional formulations for materials to be printed and tested. This process continues so that optimal material formulations can be determined based on desired mechanical properties of the material to be optimized. The optimization engine can further be capable of predicting results of formulation that have not yet been tested and using those predictions to further drive the next suggested materials to be tested.

Researchers

Wojciech Matusik / Michael Foshey / Timothy Erps / Wan Shou / Mina Lukovic / Klaus Stoll / Bernhard Von Vacano / Hanns-Hagen Goetzke / Mina Konakovic Lukovic

Departments: Dept of Electrical Engineering & Computer Science
Technology Areas: Artificial Intelligence (AI) and Machine Learning (ML) / Industrial Engineering & Automation: Autonomous Systems
Impact Areas: Advanced Materials

  • systems and methods for formulating material in a data-driven manner
    United States of America | Granted | 11,752,700
  • systems and methods for formulating material in a data-driven manner
    United States of America | Published application
  • systems and methods for formulating material in a data-driven manner
    United States of America | Published application

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