The discovery of multi-functional materials has potential to answer challenges faced across many industries, including energy, healthcare, aerospace and manufacturing. Developing and testing novel combinations has been aided by developments in artificial intelligence (AI) and machine learning (ML), as the modern tools can be deployed to analyze large amounts of data and simulations as opposed to relying on trial-and-error testing.
The Center for Data-Driven Design of Optimized Multifunctional Material Systems (D3OM2S) at Carnegie Mellon University was established in 2019 to explore solutions to the challenges facing materials discovery and has leveraged the expertise of CMU faculty and students alongside researchers from the Air Force Research Laboratory (AFRL). The center brings together experts in materials and artificial intelligence from materials science and engineering, civil and environmental engineering, chemical engineering, mechanical engineering, electrical and computer engineering, and machine learning departments to accelerate the discovery, design, and testing of high‑performance materials.
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“Bringing together materials scientists and AI experts to share language, tools, and intuition is critical to unlock new kinds of functional materials and fabrication processes that traditional approaches would likely miss,” says Michael Bockstaller, professor of materials science and engineering and director of the center.
