Designing molecules that must satisfy multiple, often conflicting objectives is a central challenge in molecular discovery. The enormous size of chemical space and the cost of high-fidelity simulations have driven the development of machine learning-guided strategies for accelerating design with limited data. Among these, Bayesian optimization (BO) offers a principled framework for sample-efficient search, while generative models provide a mechanism to propose novel, diverse candidates beyond fixed libraries. However, existing methods that couple the two often rely on continuous latent spaces, which introduces both architectural entanglement and scalability challenges. This work introduces an alternative, modular "generate-then-optimize" framework for de novo multi-objective molecular design/discovery. At each iteration, a generative model is used to construct a large, diverse pool of candidate molecules, after which a novel acquisition function, qPMHI (multi-point Probability of Maximum Hypervolume Improvement), is used to optimally select a batch of candidates most likely to induce the largest Pareto front expansion. The key insight is that qPMHI decomposes additively, enabling exact, scalable batch selection via only simple ranking of probabilities that can be easily estimated with Monte Carlo sampling. We benchmark the framework against state-of-the-art latent-space and discrete molecular optimization methods, demonstrating significant improvements across synthetic benchmarks and application-driven tasks. Specifically, in a case study related to sustainable energy storage, we show that our approach quickly uncovers novel, diverse, and high-performing organic (quinone-based) cathode materials for aqueous redox flow battery applications.

A widespread power failure plunged San Francisco into darkness on Saturday night, disrupting traffic citywide and forcing numerous self-driving Waymo taxis to stop abruptly in the middle of streets and intersections. As electricity went out across large portions of the city, traffic signals failed, leaving autonomous vehicles unable to operate as normal. Photos and videos shared by users on X showed Waymo robotaxis frozen in place, backing up traffic and creating hazardous conditions for other drivers. Waymo confirmed on Saturday evening that it had shut down its driverless ride-hailing service throughout San Francisco after footage circulated online showing its vehicles blocking roads during the blackout. "We have temporarily suspended our ride-hailing services in the San Francisco Bay Area due to the widespread power outage," Waymo spokesperson Suzanne Philion said in a statement to several news outlets.

LG TVs add'delete' option for Copilot A San Francisco power outage left Waymo's self-driving cars stranded at intersections Waymo halted its autonomous ride-hailing services in the city in response. Several of Waymo's autonomous vehicles were seen stuck in the middle of San Francisco streets following a significant power outage that took out the city's traffic lights. Waymo responded to the power outage by suspending its ride-hailing services in the city, but images and videos on social media showed the self-driving taxis stopped at intersections with hazard lights on. We have temporarily suspended our ride-hailing services in the San Francisco Bay Area due to the widespread power outage, Suzanne Philion, a spokesperson for Waymo, told Engadget in an email. Our teams are working diligently and in close coordination with city officials, and we are hopeful to bring our services back online soon.

Power outage in San Francisco knocks out electricity to one-third of Pacific Gas and Electric Co. customers, with fire at substation on 8th and Mission streets contributing to blackouts.

Microsoft and Constellation Energy partner to restart Three Mile Island nuclear reactor with $1 billion federal loan to power artificial intelligence operations.

Welcome back to In the Loop, new twice-weekly newsletter about AI. If you're reading this in your browser, why not subscribe to have the next one delivered straight to your inbox? Last month, I interviewed Trump's Energy Secretary Chris Wright for TIME's Person of the Year feature: The Architects of AI . Wright, who came from the private sector, has now staked much of his legacy on AI acceleration. In our interview, he highlighted AI's role in advancing crucial scientific research and downplayed climate risks.

Putting data centres in space isn't going to happen any time soon Could AI's insatiable thirst for colossal data centres be fixed by launching them into space? Tech companies are eyeing low Earth orbit as a potential solution, but researchers say it's unlikely in the near future due to a mountain of difficult and unsolved engineering issues. The huge demand for, and investment in, generative AI products like ChatGPT has created an unprecedented need for computing power, which requires both vast amounts of space and gigawatts of power, equivalent to that used by millions of homes. As a result, data centres are increasingly fuelled by unsustainable sources, like natural gas, with tech companies arguing that renewable power can neither produce the amount of power needed nor the consistency required for reliable use. To solve this, tech CEOs like Elon Musk and Jeff Bezos have suggested launching data centres into orbit, where they could be powered by solar panels with constant access to a higher level of sunlight than on Earth.

What is in the 28-point US plan for Ukraine? 'Ukraine is running out of men, money and time' Can the US get all sides to end the war? Why is Europe opposing Trump's peace plan? Three people, including two crew members of a cargo vessel, were killed in overnight Ukrainian drone attacks on the Russian port of Rostov-on-Don and the town of Bataysk in the country's southern Rostov region, local governor Yury Slyusar said. Russian strikes near Ukraine's Black Sea port of Odesa killed a woman in her car and hit infrastructure.

Inspired by biology, spiking neural networks (SNNs) process information via discrete spikes over time, offering an energy-efficient alternative to the classical computing paradigm and classical artificial neural networks (ANNs). In this work, we analyze the representational power of SNNs by viewing them as sequence-to-sequence processors of spikes, i.e., systems that transform a stream of input spikes into a stream of output spikes. We establish the universal representation property for a natural class of spike train functions. Our results are fully quantitative, constructive, and near-optimal in the number of required weights and neurons. The analysis reveals that SNNs are particularly well-suited to represent functions with few inputs, low temporal complexity, or compositions of such functions. The latter is of particular interest, as it indicates that deep SNNs can efficiently capture composite functions via a modular design. As an application of our results, we discuss spike train classification. Overall, these results contribute to a rigorous foundation for understanding the capabilities and limitations of spike-based neuromorphic systems.

Though the two countries are now in a race to develop atomic technology, China's most advanced reactor was the result of collaboration with American scientists. This April, in a speech given at the Shanghai branch of the Chinese Academy of Sciences, the physicist Xu Hongjie announced a breakthrough. For over a decade, his team had been working on an experimental nuclear reactor that runs on a lava-hot solution of fissile material and molten salt, rather than on solid fuel. The reactor, which went online two years ago, was a feat in itself. It is still the only one of its kind in operation in the world, and has the potential to be both safer and more efficient than the water-cooled nuclear plants that dominate the industry. Now, Xu explained, his team had been able to refuel the reactor without shutting it down, demonstrating a level of mastery over their new system. As dazzling as that was, the timing of Xu's speech also freighted the topic with geopolitical import. Only a few months earlier, DeepSeek, the Chinese artificial-intelligence company, had set alarms ringing through the U.S. tech world when it became clear that the relatively small Chinese startup, operating under U.S. export controls, had created a large language model that rivalled anything devised by the behemoths of Silicon Valley.