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As artificial intelligence (AI) tools shake up the scientific workflow, Sam Rodriques dreams of a more systemic transformation. His start-up company, FutureHouse in San Francisco, California, aims to build an ‘AI scientist’ that can command the entire research pipeline, from hypothesis generation to paper production.

Today, his team took a step in that direction, releasing what it calls the first true ‘reasoning model’ specifically designed for scientific tasks. The model, called ether0, is a large language model (LLM) that’s purpose-built for chemistry, which it learnt simply by taking a test of around 500,000 questions. Following instructions in plain English, ether0 can spit out formulae for drug-like molecules that satisfy a range of criteria.

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Variable-stiffness electronics are at the forefront of adaptive technology, offering the ability for a single device to transition between rigid and soft modes depending on its use case. Gallium, a metal known for its high rigidity contrast between solid and liquid states, is a promising candidate for such applications. However, its use has been hindered by challenges including high surface tension, low viscosity, and undesirable phase transitions during manufacturing.

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Research provides new insights into how the brain forms habits and explains why they can be so difficult to break. Neuroscientists at the Sainsbury Wellcome Centre (SWC) at UCL have discovered that the brain uses two distinct systems to learn through trial and error. This is the first time a seco

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In the intriguing realm of star-forming galaxies, the key factor isn’t the total amount of gas but rather its strategic distribution within the galaxy.

Researchers at the International Centre for Radio Astronomy Research (ICRAR) made the discovery about galaxies by studying the gas distribution that helps create stars.

Using CSIRO’s ASKAP radio telescope located at Inyarrimanha Ilgari Bundara, the CSIRO Murchison Radio-astronomy Observatory, researchers explored the gas distribution in about 1,000 galaxies as part of the WALLABY survey.

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A University of Nebraska–Lincoln engineering team is another step closer to developing soft robotics and wearable systems that mimic the ability of human and plant skin to detect and self-heal injuries.

Husker engineer Eric Markvicka, along with graduate students Ethan Krings and Patrick McManigal, recently presented a paper at the prestigious IEEE International Conference on Robotics and Automation in Atlanta, Georgia, that sets forth a systems-level approach for a soft robotics technology that can identify damage from a puncture or extreme pressure, pinpoint its location and autonomously initiate self-repair.

The paper was among the 39 of 1,606 submissions selected as an ICRA 2025 Best Paper Award finalist. It was also a finalist for the Best Student Paper Award and in the mechanism and design category.

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A Husker engineering team is another step closer to developing soft robotics and wearable systems that mimic the ability of human and plant skin to detect and self-heal injuries.

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