Method and System for Development and Optimization of Formulations for Electrochemical Materials and Devices for Clean Energy

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This patent describes a method and system for developing and optimizing formulations, especially for catalyst inks used in clean energy devices, such as fuel cells and batteries. The invention automates the process of creating and improving material formulations by combining automated material processing, measurement, and machine learning algorithms. With this system, raw materials are processed under set parameters, properties of the resulting product are measured, and the system uses machine learning to propose and iteratively refine new parameters, greatly increasing efficiency and accuracy.

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• Development of advanced catalyst inks for fuel cells and electrolyzers
• Optimization of electrode materials for batteries and supercapacitors
• Research and high-throughput screening for new electrochemical materials for energy storage and conversion
• Automated laboratories for materials discovery and parameter optimization
• Quality control and formulation adjustment in industrial manufacturing of clean energy devices

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• Significant reduction in development time and cost for new material formulations (up to 10x–100x faster than traditional methods)
• Elimination of manual trial-and-error approaches and reduction of human error in experimental work
• Enables high-throughput experimentation and rapid convergence to optimal solutions
• Improves reproducibility and consistency in material synthesis and characterization
• Provides a data-driven, machine learning-enhanced workflow that adapts and improves over time with accumulated data
• Integration with automated laboratory and IoT systems for seamless, self-driving experimentation
• Enhanced ability to meet specific target properties for energy materials, leading to more efficient and durable clean energy devices

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Patent Abstract

The invention relates to a method and a system for ascertaining an optimized formulation for a material, more particularly for a catalyst ink. A method for ascertaining an optimized formulation for a material, more particularly a catalyst ink, with at least one target property, comprises processing (12) a plurality of raw materials (1, 2, 3) in a processing unit (22) using at least one defined parameter for producing a product (5), implementing (13) a measurement with a measuring facility (23), with measurement of at least one property of the product (5) produced that provides information regarding a target property, and ascertaining (14) at least one altered parameter for producing a product (5), by means of a machine learning algorithm (28) and using the measured property and the target property.