As the UK hosts the first global AI safety summit, Guardian science editor Ian Sample joins Madeleine Finlay to look on the bright side and consider some of the huge benefits AI could bring to science.

This came during a discussion with Venkat Viswanathan about the potential for electric aviation--an exciting prospect as well as a huge challenge, given the steep demands on batteries during flight. In our discussion, Viswanathan said one of the reasons he saw hope for electric aviation is the potential of AI to speed up battery research. In fact, he cofounded a startup called Aionics in 2020 to bring AI into battery development. On stage at ClimateTech, Viswanathan announced a new research partnership that he says could make AI a key force in developing future EV batteries. The deal is between Aionics and Cellforce, a German battery maker that's a subsidiary of Porsche.

If you're on Twitter and follow news about China, you likely have heard a pretty wild rumor recently: that President Xi Jinping was under house arrest and that there was about to be a major power grab in the country. First of all, let's be very clear: this report is false and should not be taken seriously. No credible sources on China have bought it. But it's interesting to dissect how a ridiculous rumor could be elevated and spread so widely that it made it to Twitter's deeply flawed trending list over the weekend, thanks to influencer translation and amplification from accounts based in India. This story is from China Report, MIT Technology Review's new newsletter giving you the inside scoop on what's happening in China.

Using artificial intelligence to analyse vast amounts of data in atomic-scale images, researchers answered long-standing questions about an emerging type of rechargeable battery posing competition to lithium-ion chemistry.

Inside a lab at Stanford University's Precourt Institute for Energy, there are a half dozen refrigerator-sized cabinets designed to kill batteries as fast as they can. Each holds around 100 lithium-ion cells secured in trays that can charge and discharge the batteries dozens of times per day. Ordinarily, the batteries that go into these electrochemical torture chambers would be found inside gadgets or electric vehicles, but when they're put in these hulking machines, they aren't powering anything at all. Instead, energy is dumped in and out of these cells as fast as possible to generate reams of performance data that will teach artificial intelligence how to build a better battery. In 2019, a team of researchers from Stanford, MIT, and the Toyota Research Institute used AI trained on data generated from these machines to predict the performance of lithium-ion batteries over the lifetime of the cells before their performance had started to slip.

SAN RAMON, California – Tesla is working on new battery technology that CEO Elon Musk says will enable the company within the next three years to make sleeker, more affordable cars that can travel dramatically longer distances on a single charge. But the battery breakthroughs that Musk unveiled Tuesday at a highly anticipated event didn't impress investors. They were hoping Tesla's technology would mark an even bigger leap forward and propel the company's soaring stock to even greater heights. Tesla's shares shed more than 6 percent in extended trading after Musk's presentation. That deepened a downturn that began during Tuesday's regular trading session as investors began to brace for a potential letdown.

Washington D.C.: With the help of machine learning and artificial intelligence researchers are accelerating the power of batteries. Researchers at the U.S. Department of Energy's (DOE) Argonne National Laboratory have turned to the power of machine learning and artificial intelligence to dramatically accelerate the process of battery discovery, according to the study published in -- Chemical Science. As described in two new papers, Argonne researchers first created a highly accurate database of roughly 133,000 small organic molecules that could form the basis of battery electrolytes. To do so, they used a computationally intensive model called G4MP2. This collection of molecules, however, represented only a small subset of 166 billion larger molecules that scientists wanted to probe for electrolyte candidates.

With the help of machine learning and artificial intelligence researchers are accelerating the power of batteries. Researchers at the U.S. Department of Energy's (DOE) Argonne National Laboratory have turned to the power of machine learning and artificial intelligence to dramatically accelerate the process of battery discovery, according to the study published in -- Chemical Science. As described in two new papers, Argonne researchers first created a highly accurate database of roughly 133,000 small organic molecules that could form the basis of battery electrolytes. To do so, they used a computationally intensive model called G4MP2. This collection of molecules, however, represented only a small subset of 166 billion larger molecules that scientists wanted to probe for electrolyte candidates.

Washington D.C. [USA], Nov 29 (ANI): With the help of machine learning and artificial intelligence researchers are accelerating the power of batteries.

Instead, researchers at the U.S. Department of Energy's (DOE) Argonne National Laboratory have turned to the power of machine learning and artificial intelligence to dramatically accelerate the process of battery discovery. As described in two new papers, Argonne researchers first created a highly accurate database of roughly 133,000 small organic molecules that could form the basis of battery electrolytes. To do so, they used a computationally intensive model called G4MP2. This collection of molecules, however, represented only a small subset of 166 billion larger molecules that scientists wanted to probe for electrolyte candidates. Because using G4MP2 to resolve each of the 166 billion molecules would have required an impossible amount of computing time and power, the research team used a machine learning algorithm to relate the precisely known structures from the smaller data set to much more coarsely modeled structures from the larger data set.