| Location: | California |
|---|---|
| Posted: | Jul 7, 2025 |
| Due: | Aug 7, 2025 |
| Agency: | ENERGY, DEPARTMENT OF |
| Type of Government: | Federal |
| Category: |
|
| Solicitation No: | IL-13738 |
| Publication URL: | To access bid details, please log in. |
Opportunity:
Lawrence Livermore National Laboratory (LLNL), operated by the Lawrence Livermore National Security (LLNS), LLC under contract no. DE-AC52-07NA27344 (Contract 44) with the U.S. Department of Energy (DOE), is offering the opportunity to enter into a collaboration to further develop and commercialize its Automated High-Throughput Materials Optimization Active-Mixing DIW Platform.
Background:
Polymer materials formulation and optimization has been generally limited to mixing by hand, as polymers have a wide range of viscosities. A high throughput approach is therefore needed to enable faster screening of polymers optimized for targeted applications. There currently exists systems that mix multi-materials with disparate viscosities. If these systems are integrated with an in-situ characterization capability combined with machine learning experimental planning systems, an all-in-one polymer optimization and screening platform could be developed that can facilitate high-throughput materials development.
Description:
The platform has three major components:
(1) active-mixing direct-ink-write
(2) in situ characterization substrates or probes
(3) active learning experimental planning system.
Active-mixing direct ink write (DIW) additive manufacturing enable mixing of materials with highly disparate viscosities, from liquids to pastes. By coupling this mixing system with in-situ characterization methods with machine learning experimental planning systems, LLNL researchers developed an automated platform for materials discovery and optimization.
Machine learning models take the data from the DIW AM prints and use it to dictate the next batch of films to be printed, homing in on the formulation that achieves the best desired property required for the target application. While active mixing DIW has already been developed [Harvard’s U.S. Patent No 10,071,350], the novelty of this LLNL invention is the integrated system of characterization capabilities, machine learning, and experimental planner, making it a closed-loop polymer screening system.
Advantages/Benefits:
Potential Applications:
Development Status:
Current stage of technology development: TRL ☐ 0-2 ☒ 3-5 ☐ 5-9
LLNL has filed for patent protection on this invention.
U.S. Patent Application No. 20240091808 High Throughput Materials Screening published 3/21/2024
U.S. Patent Application No. 20240096454 High Throughput Materials Screening published 3/21/2024
LLNL is seeking industry partners with a demonstrated ability to bring such inventions to the market. Moving critical technology beyond the Laboratory to the commercial world helps our licensees gain a competitive edge in the marketplace. All licensing activities are conducted under policies relating to the strict nondisclosure of company proprietary information.
Please visit the IPO website at https://ipo.llnl.gov/resources for more information on working with LLNL and the industrial partnering and technology transfer process.
Note: THIS IS NOT A PROCUREMENT. Companies interested in commercializing LLNL's Automated High-Throughput Materials Optimization Active-Mixing DIW Platform should provide an electronic OR written statement of interest, which includes the following:
Please provide a complete electronic OR written statement to ensure consideration of your interest in LLNL's Automated High-Throughput Materials Optimization Active-Mixing DIW Platform.
The subject heading in an email response should include the Notice ID and/or the title of LLNL’s Technology/Business Opportunity and directed to the Primary and Secondary Point of Contacts listed below.
Written responses should be directed to:
Lawrence Livermore National Laboratory
Innovation and Partnerships Office
P.O. Box 808, L-779
Livermore, CA 94551-0808
Attention: IL-13738

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