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Brainstorming nanomaterials with a domain-specific chatbot

A researcher has just finished writing a scientific paper. She knows her work could benefit from another perspective. Did she overlook something? Or perhaps there’s an application of her research she hadn’t thought of. A second set of eyes would be great, but even the friendliest of collaborators might not be able to spare the time to read all the required background publications to catch up.

Kevin Yager—leader of the electronic nanomaterials group at the Center for Functional Nanomaterials (CFN), a U.S. Department of Energy (DOE) Office of Science User Facility at DOE’s Brookhaven National Laboratory—has imagined how recent advances in artificial intelligence (AI) and machine learning (ML) could aid scientific brainstorming and ideation. To accomplish this, he has developed a chatbot with knowledge in the kinds of science he’s been engaged in.

Rapid advances in AI and ML have given way to programs that can generate creative text and useful software code. These general-purpose chatbots have recently captured the public imagination. Existing chatbots—based on large, diverse language models—lack detailed knowledge of scientific sub-domains. By leveraging a document-retrieval method, Yager’s bot is knowledgeable in areas of nanomaterial science that other bots are not.

OpenGPT is an open-source alternative to OpenAI’s custom ChatGPTs

OpenGPT is a promising toolkit for building custom chatbots like GPTs, but it is completely open-source and offers even more configuration options. Which means it is also more complicated.

With GPTs, OpenAI introduced the evolution of its plugin concept at Dev Days in November 2023. The AI company is giving end users different tools to create a chatbot tailored to their needs, without having to know how to code a chatbot. OpenAI even plans to give successful GPT creators a share of the revenue from ChatGPT Plus in the future.

When setting up GPTs, users can upload their files, link APIs, assign system prompts, and enable modules for web browsing, DALL-E, and code interpreters.

Model Correctly Predicts High-Temperature Superconducting Properties

A first-principles model accounts for the wide range of critical temperatures (Tcs) for four materials and suggests a parameter that determines Tc in any high-temperature superconductor.

Since the first high-temperature superconducting materials, known as the cuprates, were discovered in 1986, researchers have struggled to explain their properties and to find materials with even higher superconducting transition temperatures (Tcs). One puzzle has been the cuprates’ wide variation in Tc, ranging from below 10 K to above 130 K. Now Masatoshi Imada of Waseda University in Japan and his colleagues have used first-principles calculations to determine the order parameters—which measure the density of superconducting electrons—for four cuprate materials and have predicted the Tcs based on those order parameters [1]. The researchers have also found what they believe is the fundamental parameter that determines Tc in a given material, which they hope will lead to the development of higher-temperature superconductors.

For each material, Imada and his colleagues applied the basic principles of quantum mechanics, focusing on the planes of copper and oxygen atoms that are known to host the superconducting electrons. They used a combination of numerical techniques, including one supplemented by machine learning, and did not require any adjustable parameters.

Elon Musk Hints at Bringing Tesla Bot’s Bulletproof Cybertruck Test to Life in the Coming Year

Tesla has been pushing the boundaries of what’s possible in the automotive industry. A recent demonstration of this is the bulletproof testing of the Cybertruck, conducted using Tesla Bots. This article delves into the details of these tests, the implications for Tesla’s technological advancements, and the potential future of these innovations. Tesla’s Bulletproof Cybertruck Recently, […].

Huawei takes center stage in the China-US AI chip race

In an ironic twist, the Chinese government is turning to Huawei to spearhead the nation’s quest for semiconductor self-reliance.


Andrea Nicolini/iStock.

The sanctions made it so that only those with special permission could produce the chips designed by Huawei. As a result, Huawei faced difficulties in obtaining new chips for the development of more advanced smartphones.

AI-system boosts microgrid efficiency for rapid power outage recovery

During power outages, microgrids leverage local renewable sources like rooftop solar panels and small wind turbines for efficient power restoration.


UC-Santa Cruz.

Addressing this common challenge, a research team from the University of California — Santa Cruz led by assistant professor Yu Zhang is employing innovative methods to enhance power systems’ efficiency, dependability, and robustness. For this, they have devised an artificial intelligence (AI) centered strategy to intelligently manage microgrids intelligently, ensuring effective power restoration in the event of outages.