Latest Techniques in Material Synthesis

After five years of work, I’m excited to finally share our new paper on a new method for upcycling titanium-based scrap material into a new useable alloy through a method we call compositional steering. This work showcases a wonderful ongoing relationship between the Office and Naval Research (ONR) and NASA JPL on technology that has dual-use for both the Navy and NASA. As we try to establish a sustainable presence in space, NASA will need technologies that can take feedstock in various forms (mostly Ti and Al, but possibly contaminated with regolith), and convert them into new alloys with useable properties. Similarly, the Navy needs to start preparing for a world where pure metals, like titanium, are scarce and new methods are needed to create unmanned submersibles from waste streams, or to perform in-theatre repairs. In this work, me and my co-authors develop a method for compositional steering and then apply it to a specific use-case of bulk metallic glass. We start with a scrap Ti alloy that was contaminated with oxygen and carbon during manufacturing and was off-composition and deemed scrap. By studying phase diagrams and the literature, we experimentally demonstrate that we can add only 25% mass of new elements strategically and convert the scrap material into a new bulk glass former that can be produced into parts up to 3 mm thick. The method we demonstrate has broad applications when coupled with machine learning and computational materials science, where unknown compositions of scrap materials can be steered towards the closest “useable” alloy with the least amount of additives. We further demonstrated our technique by taking scrap turnings of titanium, steel and aluminum from the JPL machine shop garbage cans and remelting into alloys with unique properties, such as a beta titanium alloy and a bulk metallic glass. We are looking forward to partnering with industry and the computational materials science community to start developing new methods for sustainable metallurgy by taking advantage of waste streams, like turnings or used additive manufacturing powder. My collaborators here are the incredible Punnathat Bordeenithikasem, Miguel de Brito Costa, Melanie Buziak, Thomas Freeman, and Anthony Botros, all working in the JPL metallurgy lab funded by ONR. With so much turmoil happening right now with government funding, I wanted to highlight what I consider to be a critical relationship between a national lab and a military funding organization on issues of importance to national security. These are great relationships that should be fostered. Our work was highlighted as an Editor’s Choice and will appear later in a special issue of sustainable metallurgy. We are grateful to ONR for ongoing funding in this area. https://lnkd.in/gmxeFiXT

🚀 Exciting News from NC State! Our research team has developed a groundbreaking laser technique to create ultra-high temperature ceramics, such as hafnium carbide (HfC), more efficiently and with less energy. This innovation has significant impacts for industries requiring materials that can withstand extreme heat, such as aerospace and nuclear energy. Traditional methods involve heating materials in furnaces at temperatures above 2,200°C, which is time-consuming and energy-intensive. Our new approach uses a 120-watt laser to sinter a liquid polymer precursor in an inert environment, transforming it into solid ceramic without the need for such extreme conditions. This technique offers two main applications: 1. Coating: Applying ultra-high temperature ceramic coatings to materials like carbon composites. 2. 3D Printing: Creating complex ceramic structures layer by layer, enabling more versatile and precise manufacturing. This advancement not only streamlines the production process but also opens new possibilities for designing components that can endure extreme environments. For more details, read the full article here: https://lnkd.in/eE2Wh2TR #Innovation #MaterialsScience #NCStateResearch #AdvancedManufacturing

CARBENE POLYMERIZATION FROM THE CATALYZED DECOMPOSITION OF DIAZO COMPOUNDS Carbene polymerization, initiated by the catalyzed decomposition of diazo compounds, offers a distinct non-petroleum-based route for synthesizing highly functionalized polyolefins using single-carbon building blocks is critical for synthesis of high molecular weight and stereoregular polycarbenes, and integrated copolymerization methods. Functional polyolefins are polymeric materials that valued for enhanced surface properties, including miscibility, adhesion, and printability. Traditional methods for synthesizing these polymers involve radical and coordination polymerization of vinyl monomers (C2 polymerization), but these methods have limitations, including challenges in controlling polymer microstructures and the incompatibility of polar vinyl monomers with transition metal catalysts. In order to overcome these pitfalls, carbene polymerization offers a promising alternative approach for producing highly functionalized polyolefins, where each main-chain carbon atom carries a functional group (C1 polymerization). The resulting high-density functional polyolefins exhibit unique aggregation morphologies and unexpected material properties, such as thermotropic/lyotropic liquid crystallinity and rapid thermal, pH, and fluorescent responses. Recent work by Ihara's group at Ehime University explored innovative methods for polymer synthesis with an unprecedented structure, using diazocarbonyl compounds as monomers. Over the last two decades, carbene polymerization has been the subject of extensive research, primarily focusing on α-carbonyl diazo compounds (diazoacetates and diazo ketones) and transition metal catalysts, based on Copper, Palladium, and Rhodium. Compared to the highly explosive diazoalkanes, diazo carbonyl compounds offer greater stability and safety for large-scale synthesis, and serve as versatile carbene precursors. Ihara and colleagues reported several palladium-based initiating systems, which predominantly yielded atactic polymers with low molecular weights. Through careful design, certain [(NHC)Pd/borate] and [(π-allyl)PdCl/borate] systems have been shown to produce higher molecular weight polymers (Mn ∼ 20,000 Da) with improved stereotacticity. Two primary carbene polymerization mechanisms have been proposed: "carbene insertion polymerization" and "carbene radical polymerization". Ihara's group found that palladium (Pd)-based initiator can polymerize diazoacetate, resulting in polymers with a carbon-carbon (C–C) backbone where each carbon atom is bonded to an alkoxycarbonyl (ester) group, termed C1 polymerization. This can be resulted in high molecular weight polymers synthesis (MW > 50000) with enhanced acidity and much higher melting points, up to 130 °C, compared to their vinyl polymer counterparts and functional groups quantitative incorporation. #https://lnkd.in/e9kKQDn3

Breakthrough Nano-Architected Materials Revolutionize Strength-to-Weight Ratios Researchers at the University of Toronto have created groundbreaking nano-architected materials with a strength comparable to carbon steel and the lightness of Styrofoam. These materials, which combine high strength, low weight, and customizability, have the potential to transform industries such as aerospace and automotive, where lightweight yet durable components are critical. Key Features of the Nano-Architected Materials • Exceptional Strength-to-Weight Ratio: The materials utilize nanoscale geometries to achieve unprecedented performance, leveraging the “smaller is stronger” phenomenon. • Customizable Design: The nanoscale shapes resemble structural patterns, such as triangular bridges, that enhance durability and stiffness while minimizing weight. • Versatility Across Industries: Their application extends to aerospace, automotive, and other fields where maximizing efficiency and reducing material weight are paramount. Addressing Design Challenges with AI • Stress Concentrations: Traditional lattice designs suffer from stress concentrations at sharp corners, leading to early failure. This limits the material’s effectiveness despite its high strength-to-weight ratio. • Machine Learning Solutions: Peter Serles, the lead researcher, highlighted how machine learning algorithms were applied to optimize these nano-lattices. AI models helped identify innovative geometries that minimize stress points and extend material durability. Implications for Aerospace and Automotive These materials can be game-changing for industries where reducing weight while maintaining strength is vital. For aerospace, lighter and stronger components mean increased fuel efficiency and improved performance. In automotive applications, they can reduce energy consumption while ensuring safety and durability. The successful application of machine learning to material science marks a pivotal moment, enabling innovations that were previously limited by traditional design methods. These developments could pave the way for a new generation of high-performance, sustainable materials.

🌍🔍 Revolutionizing materials discovery with Microsoft's MatterGen Innovative breakthroughs often arise from reimagining what's possible, and Microsoft's MatterGen is doing just that for materials discovery. Traditionally, finding new materials has been an exhaustive trial-and-error process, akin to finding a needle in a haystack. MatterGen changes the game by using generative AI to create materials based on specific design requirements, unlocking a universe of possibilities. What makes MatterGen special? MatterGen goes beyond simple screening methods. It generates novel materials from scratch, incorporating complex criteria like mechanical strength and electronic properties. Using a 3D diffusion model, it tweaks the elemental composition and arrangements to deliver cutting-edge compounds tuned for specific needs. Real-world impact: A recent collaboration with the Shenzhen Institutes of Advanced Technology showcased MatterGen's potential. It designed a new material, TaCr₂O₆, aimed at a specific bulk modulus—a measure of compression resistance. While the final product slightly missed its target, the model demonstrated remarkable predictive accuracy, paving the way for advancements in fields like renewable energy and electronics. A Paradigm Shift 🔄: By releasing MatterGen's source code under the MIT license, Microsoft invites researchers worldwide to explore, experiment, and innovate. This openness not only fosters collaboration but also accelerates progress across industries. MatterGen is more than a tool—it's an invitation to reimagine materials science. As we look to the future, the possibilities are as vast as our imagination. How do you envision utilizing MatterGen in your field? Share your thoughts! 🚀 #MaterialsScience #Innovation #AI #Microsoft

MIT researchers just solved a major challenge in molecular synthesis Designing molecules that are both functional and synthetically feasible has been a long-standing challenge in drug discovery and materials science. A new AI-driven framework 𝗿𝗲𝗱𝗲𝗳𝗶𝗻𝗲𝘀 𝗺𝗼𝗹𝗲𝗰𝘂𝗹𝗮𝗿 𝗱𝗲𝘀𝗶𝗴𝗻 𝗮𝘀 𝗮 𝗽𝗿𝗼𝗰𝗲𝗱𝘂𝗿𝗮𝗹 𝘀𝘆𝗻𝘁𝗵𝗲𝘀𝗶𝘀 𝗽𝗿𝗼𝗯𝗹𝗲𝗺, optimizing both the structure and the pathway needed to create it. 1. Introduces a 𝗯𝗶-𝗹𝗲𝘃𝗲𝗹 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿k that separates molecular design into syntactic (reaction templates) and semantic (molecular properties) components, enabling more controlled molecule generation. 2. Uses 𝗠𝗮𝗿𝗸𝗼𝘃 𝗖𝗵𝗮𝗶𝗻 𝗠𝗼𝗻𝘁𝗲 𝗖𝗮𝗿𝗹𝗼 (𝗠𝗖𝗠𝗖) 𝘀𝗶𝗺𝘂𝗹𝗮𝘁𝗶𝗼𝗻𝘀 to iteratively refine synthetic pathways, ensuring that designed molecules are feasible to synthesize. 3. Implements a 𝗴𝗲𝗻𝗲𝘁𝗶𝗰 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺 𝗼𝘃𝗲𝗿 𝗺𝗼𝗹𝗲𝗰𝘂𝗹𝗮𝗿 𝗱𝗲𝘀𝗰𝗿𝗶𝗽𝘁𝗼𝗿𝘀 𝗮𝗻𝗱 𝗿𝗲𝗮𝗰𝘁𝗶𝗼𝗻 𝘁𝗲𝗺𝗽𝗹𝗮𝘁𝗲𝘀, optimizing drug-likeness, synthesizability, and functional properties in a single workflow. 4. Outperforms existing methods in 𝗯𝗼𝘁𝗵 𝘀𝘆𝗻𝘁𝗵𝗲𝘀𝗶𝘇𝗮𝗯𝗹𝗲 𝗮𝗻𝗮𝗹𝗼𝗴 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗻𝗼𝘃𝗲𝗹 𝗺𝗼𝗹𝗲𝗰𝘂𝗹𝗲 𝗱𝗲𝘀𝗶𝗴𝗻, producing compounds that balance structural similarity, diversity, and experimental feasibility. Read more: https://lnkd.in/gtp2fE7e Congrats to Michael Sun, Alston Lo, Minghao Guo, Jie Chen, Wojciech Matusik, Connor W. Coley! I post the latest developments in health AI & tips for research – 𝗰𝗼𝗻𝗻𝗲𝗰𝘁 𝘄𝗶𝘁𝗵 𝗺𝗲 𝘁𝗼 𝘀𝘁𝗮𝘆 𝘂𝗽𝗱𝗮𝘁𝗲𝗱! Also, check out my health AI blog here: https://lnkd.in/g3nrQFxW

The same AI models that generate images can now design new materials — And they are going it as a team. Microsoft Research accounced two specialized material AI agents that work together: MatterGen is the brainstormer. It uses diffusion models – similar to the algorithms powering image generation – to design novel molecular structures and predict their fundamental properties. MatterSim is the critic, assessing the physical stability and viability of MatterGen's proposed structures by applying fundamental quantum-mechanical principles. This agentic AI workflow can massivly accelerate the materials discovery timeline compared to the guess and check methods we’re stuck with today. Beyond the speed, there's a deeper insight here relevant to the advancement of AI in science: the power of general machine learning architectures. A General approache is yet again proving highly effective for complex, specialized problems. Here they eliminate the need for intricate, computationally intensive, domain-specific Field Theory. The adaptability that allows these models to excel at tasks from image creation to atomic-scale simulation underscores their potential in material science. We wonder: could a model like this be applied to polymers? Rampi Ramprasad Chiho Kim. Who's all in? who's skeptical? Timothy McGee David Breslauer, PhD Nikolaus Mackay Joanna Pool, PhD, PMP Each week Kir Titievsky 🇺🇦 and I have been diving into new research and applications for AI and material science. Our thesis? AI is shifting new materials from art to science. Follow for more! --- Amazing researchers behind the work: behind the work: Tian Xie, Ziheng LU, Claudio Zeni Robert Pinsler Daniel Zugner, Andrew Fowler, Matthew Horton, Ryota Tomioka, and many more. #ArtificialIntelligence #MaterialsScience #Innovation #DeepTech #MachineLearning #ComputationalChemistry #DigitalTransformation ##Investment #Research #AIinScience #AICoE @