Simon Gustavsson

🔔 🔬 Dynamic STEM-EELS: when beam damage becomes an opportunity This study led by Kevin Roccapriore and AtomAI by Maxim Ziatdinov introduces the integration of dynamic computer vision–enabled imaging with electron energy loss spectroscopy (EELS) in scanning transmission electron microscopy (STEM). This approach involves real-time discovery and analysis of atomic structures as they form, allowing us to observe the evolution of material properties at the atomic level, capturing transient states traditional techniques often miss. Rapid object detection and action system enhances the efficiency and accuracy of STEM-EELS by autonomously identifying and targeting only areas of interest. This machine learning (ML)–based approach differs from classical ML in that it must be executed on the fly, not using static data. We apply this technology to V-doped MoS2, uncovering insights into defect formation and evolution under electron beam exposure. This approach opens uncharted avenues for exploring and characterizing materials in dynamic states, offering a pathway to increase our understanding of dynamic phenomena in materials under thermal, chemical, and beam stimuli. https://lnkd.in/eJk9NKVX

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Groq, known for their high-performance AI hardware and software, released two new open-source models specifically designed for tool use: Llama-3-Groq-70B-Tool-Use and Llama-3-Groq-8B-Tool-Use, built with Meta Llama-3. Tools are utilized by AI assistants to respond to user prompts. Typically, a tool is an API call to an external system, like Google search. The results from the tool are then analyzed by the LLM and incorporated into the response. Thus, the LLM's performance in using tools is paramount. These models promise significant advancements in processing speed and efficiency. Llama-3-Groq-70B-Tool-Use boasts 70 billion parameters, while Llama-3-Groq-8B-Tool-Use features 8 billion parameters, both optimized for rapid deployment in various applications. By leveraging Groq’s advanced hardware, these models aim to set a new standard in AI tool use, focusing on enhancing both performance and accessibility.

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Cambridge Scientists Achieve Long-Sought Quantum State Stability in New 2D Material. Researchers at the Cavendish Laboratory have identified spin coherence in atomic defects within Hexagonal Boron Nitride (hBN) under ambient conditions, a rare achievement in quantum materials. The study, published in Nature Materials, highlights that these spins can be controlled with light and have promising implications for future quantum technologies, including sensing and secure communications. The findings also stress the need for further exploration to enhance defect reliability and extend spin storage times, underlining the potential of hBN in advancing quantum technological applications - https://lnkd.in/ghNpCZYT

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Proud of our world-class researchers in systems engineering!   Prof. Gregory Gerling and University of Virginia Systems and Information Engineering PhD Alums Bingxu Li, Ph.D. and Steven Hauser were recently awarded the 2024 IEEE Transactions on Haptics Best Paper Award for their paper "Faster indentation influences skin deformation to reduce tactile discriminability of compliant objects." They, along with Russell Sharpe, were also awarded the runner-up best technical conference paper at the IEEE Haptics Symposium on April 11th for work entitled "A transparent hydraulic actuator to visualize finger pad deformation at programmable material compliances." Link to Article: https://lnkd.in/dfAufney

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In 2018, we published an interesting theoretical study based on DFT calculations on the atom-level evolution of the physical and chemical properties of transition-metal dichalcogenide (TMD) nanoflakes, which is a key step to improve our knowledge of two-dimensional (2D) TMD materials, which can help in the designing of new 2D materials. The calculations were performed for the (MoQ2)n nanoflakes, where Q = S, Se, and Te and n = 1–16. https://lnkd.in/d6KW6jQN

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Check out the combination of SILCS-Hotspots with Network Analysis to identify putative allosteric binding sites.

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