We've gone from A to Z to find Amazon's best Prime Day deals on the gear worth owning. Amazon Prime Day is here once again. Amazon's annual Prime Day deals aims to entice us with an endless scroll of "discounts (some real, many fake), hammering away with red slashes, big percentages off, and coupons you can only see after adding to cart. While Prime Day deals aren't what they once were--its success has inspired a massive number of fake deals and attracted obscure brands--there are still some very significant discounts to be found. For the next four days, the WIRED Reviews team will be pooling our hundreds of years of collective expertise to find actual savings on products we have personally tested and approved. Let us absorb the neon signage and "buy now" buttons on your behalf and share the deals worth sharing. We'll keep this list updated frequently for the duration of the sale, which runs from June 23 to June 26. This is our very favorite MagSafe power bank . Wireless and MagSafe charging aren't always the fastest or most efficient, but despite its bulk, this large-capacity bank can top off modern phones once (or maybe a little more than that) without overheating or taking forever. One of the best budget wireless chargers is even more affordable thanks to Prime Day. You can buy fancier, faster wireless chargers, but if you just want a simple option that'll top off your phone, this is worth checking out. It can deliver up to 10 watts, though you'll need to supply your own wall adapter. Want something that can fast charge your phone, juice up your tablet, and even refill your laptop? This generous 25,000-mAh capacity can do it all, but stops shy of the carry-on air travel limit. The maximum output is 165 watts for two devices, but 100 watts for a single device. It has lovely rounded edges, a retractable, flat, 2.3-foot USB-C cable on the top, and a snazzy, durable, braided 1-foot USB-C cable that doubles as a carry loop. This remains one of my favorite Windows laptops, despite the recent price increases. But now, it's unexpectedly dropped to $835 for Prime Day, making it the best laptop Prime Day deal I've found so far. Price aside, though, my favorite feature is the 3:2 aspect ratio screen, which also has a faster 120-Hz refresh rate. It's absolutely gorgeous, and all the extra vertical screen space gives more room to work with. A new version just got announced with a more powerful Snapdragon X2 chip inside, but it's considerably more expensive . Unlike so many Windows laptops around $500, the OmniBook 3 has excellent performance and battery life. And while the touchpad isn't the best, the specs alone make it the very best cheap laptop you can buy. This gaming laptop has a big advantage over many others at this price. But the display, build quality, keyboard, and touchpad are a solid step up over even some of my favorite budget gaming laptops.

We've gone from A to Z to find Amazon's best Prime Day deals on the gear worth owning. Amazon Prime Day is here once again. Amazon's annual Prime Day deals aims to entice us with an endless scroll of "discounts (some real, many fake), hammering away with red slashes, big percentages off, and coupons you can only see after adding to cart. While Prime Day deals aren't what they once were--its success has inspired a massive number of fake deals and attracted obscure brands--there are still some very significant discounts to be found. For the next four days, the WIRED Reviews team will be pooling our hundreds of years of collective expertise to find actual savings on products we have personally tested and approved. Let us absorb the neon signage and "buy now" buttons on your behalf and share the deals worth sharing. We'll keep this list updated frequently for the duration of the sale, which runs from June 23 to June 26. This is our very favorite MagSafe power bank . Wireless and MagSafe charging aren't always the fastest or most efficient, but despite its bulk, this large-capacity bank can top off modern phones once (or maybe a little more than that) without overheating or taking forever. One of the best budget wireless chargers is even more affordable thanks to Prime Day. You can buy fancier, faster wireless chargers, but if you just want a simple option that'll top off your phone, this is worth checking out. It can deliver up to 10 watts, though you'll need to supply your own wall adapter. Want something that can fast charge your phone, juice up your tablet, and even refill your laptop? This generous 25,000-mAh capacity can do it all, but stops shy of the carry-on air travel limit. The maximum output is 165 watts for two devices, but 100 watts for a single device. It has lovely rounded edges, a retractable, flat, 2.3-foot USB-C cable on the top, and a snazzy, durable, braided 1-foot USB-C cable that doubles as a carry loop. This remains one of my favorite Windows laptops, despite the recent price increases. But now, it's unexpectedly dropped to $835 for Prime Day, making it the best laptop Prime Day deal I've found so far. Price aside, though, my favorite feature is the 3:2 aspect ratio screen, which also has a faster 120-Hz refresh rate. It's absolutely gorgeous, and all the extra vertical screen space gives more room to work with. A new version just got announced with a more powerful Snapdragon X2 chip inside, but it's considerably more expensive . Unlike so many Windows laptops around $500, the OmniBook 3 has excellent performance and battery life. And while the touchpad isn't the best, the specs alone make it the very best cheap laptop you can buy.

Power companies are pushing aggressive time-based use pricing. Here's how a regular consumer can benefit. I like to keep my home at a cool and comfortable 68 degrees year-round. This preference would be fine if I lived near the Pacific Ocean, or in a small home, or in a newer home that's insulated with modern mineral wool instead of tissue paper and horsehair. I, however, live in a 2,000-plus-square-foot home built in 1906.

The ability to quickly and accurately compute properties from atomic simulations is critical for advancing a large number of applications in chemistry and materials science including drug discovery, energy storage, and semiconductor manufacturing. To address this need, we present a family of Universal Models for Atoms (UMA), designed to push the frontier of speed, accuracy, and generalization. UMA models are trained on half a billion unique 3D atomic structures (the largest training runs to date) by compiling data across multiple chemical domains, e.g.

Accurate prediction of ionic conductivity in electrolyte systems is crucial for advancing numerous scientific and technological applications. While significant progress has been made, current research faces two fundamental challenges: (1) the lack of high-quality standardized benchmarks, and (2) inadequate modeling of geometric structure and intermolecular interactions in mixture systems. To address these limitations, we first reorganize and enhance the CALiSol and DiffMix electrolyte datasets by incorporating geometric graph representations of molecules. We then propose GeoMix, a novel geometry-aware framework that preserves Set-SE(3) equivariance--an essential but challenging property for mixture systems. At the heart of GeoMix lies the Geometric Interaction Network (GIN), an equivariant module specifically designed for intermolecular geometric message passing. Comprehensive experiments demonstrate that GeoMix consistently outperforms diverse baselines (including MLPs, GNNs, and geometric GNNs) across both datasets, validating the importance of cross-molecular geometric interactions and equivariant message passing for accurate property prediction. This work not only establishes new benchmarks for electrolyte research but also provides a general geometric learning framework that advances modeling of mixture systems in energy materials, pharmaceutical development, and beyond.

Generative diffusion models have achieved remarkable success in producing high-quality images. However, these models typically operate in continuous intensity spaces, diffusing independently across pixels and color channels. As a result, they are fundamentally ill-suited for applications involving inherently discrete quantities such as particle counts or material units, that are constrained by strict conservation laws like mass conservation, limiting their applicability in scientific workflows. To address this limitation, we propose Discrete Spatial Diffusion (DSD), a framework based on a continuous-time, discrete-state jump stochastic process that operates directly in discrete spatial domains while strictly preserving particle counts in both forward and reverse diffusion processes. By using spatial diffusion to achieve particle conservation, we introduce stochasticity naturally through a discrete formulation. We demonstrate the expressive flexibility of DSD by performing image synthesis, class conditioning, and image inpainting across standard image benchmarks, while exactly conditioning total image intensity. We validate DSD on two challenging scientific applications: porous rock microstructures and lithium-ion battery electrodes, demonstrating its ability to generate structurally realistic samples under strict mass conservation constraints, with quantitative evaluation using state-of-the-art metrics for transport and electrochemical performance.

We study the subclass of potential mean-field games in which the running interaction cost and the terminal target cost are both expressed through reproducing-kernel maximum mean discrepancy (MMD) penalties, and develop a computational framework that exploits this kernel structure. Both costs are estimated from finite-sample empirical distributions using a random Fourier U-statistic representation that is unbiased and has linear cost in the batch size. The drift of the controlled diffusion is parametrized by a neural network and trained via stochastic gradient descent. For this subclass we prove a sample-level almost-sure convergence theorem and an explicit almost-sure rate of convergence, under coupled rate conditions on the penalty parameter, the random-feature count, the sample size, and the optimization tolerance. The framework includes the kernel-MMD-penalty Schrödinger bridge problem as the special case of a vanishing interaction cost. Numerical experiments illustrate the method on the Schrödinger bridge problem in dimensions up to one hundred, and on an electric vehicle charging coordination problem with per-vehicle physical heterogeneity, where an aggregate-demand congestion cost represents price-feedback competition at the population level and the terminal MMD penalty shapes the state-of-charge distribution at the deadline.

The use of residential photovoltaics has increased dramatically in recent years. With battery systems becoming more affordable, the optimal operation of a photovoltaic-battery system can bring significant savings to households. Optimal control requires correct forecasts of underlying parameters, such as photovoltaic power generation, to schedule the battery. While forecasting models have become increasingly accurate due to algorithmic advances and data availability, accuracy is typically measured in generic metrics which might not align with the downstream application. This study proposes a decision-focused learning framework that integrates optimization and prediction by training a Long Short-Term Memory photovoltaic energy forecaster on the downstream optimal scheduling of a battery system. The proposed methodology is compared against a standard two-phase approach. Across a 14-month evaluation period, the decision-focused method reduced average electricity costs across twenty buildings by 3.6% when normalized against performance bounds defined by a perfect forecast and a baseline of no optimization. Critically, this financial improvement was achieved despite the model exhibiting a root mean squared error of 19.9%, significantly higher than the decoupled model's 8.2%. Warm-starting the decision-focused model further improves results, lowering average cost by approximately 8%, while also mitigating the negative impact on statistical accuracy (root mean squared error of 13.7%). The findings are statistically significant at the 0.001 level across the twenty households and for each household individually. These results demonstrate that aligning forecast models with optimization goals is key for achieving cost advantages in PV-battery systems. Future research should replicate these findings on other datasets, alternate forecasting models and alternate optimization algorithms.

Ford and GM are backing away from electric vehicles and moving into the battery storage business. And it all comes back to AI. Automakers make cars--it's in the name. But lately, politics, current events, and Wall Street's latest preoccupation, artificial intelligence, have them looking a lot more like energy companies. The pivot, analysts say, could give US auto manufacturers struggling through a transition to electric vehicles an easier path over the next few years. Whether it works will come down to the same technology that automakers once promised would power the majority of their lineups: batteries .

SoftBank will partner with South Korea's Cosmos Lab and DeltaX to enable mass production of large-scale battery cells from the fiscal year starting next April. SoftBank Group's mobile unit said it plans to begin large-scale battery cell manufacturing at its plant in Sakai, Osaka Prefecture, to address growing power demand for AI services. SoftBank Corp. will partner with South Korea's Cosmos Lab and DeltaX to enable mass production from the fiscal year starting next April, the company said in a statement Monday. The aim is to output energy storage systems at a scale of one gigawatt-hour per year, SoftBank said, which would make it one of the largest facilities in Japan, according to data from BloombergNEF. SoftBank could scale up to a capacity of several GWh, Bloomberg reported last month.