Understanding and controlling the atomistic-level reactions governing the formation of the solid-electrolyte interphase (SEI) is crucial for the viability of next-generation solid state batteries. However, challenges persist due to difficulties in experimentally characterizing buried interfaces and limits in simulation speed and accuracy. We conduct large-scale explicit reactive simulations with quantum accuracy for a symmetric battery cell, {\symcell}, enabled by active learning and deep equivariant neural network interatomic potentials. To automatically characterize the coupled reactions and interdiffusion at the interface, we formulate and use unsupervised classification techniques based on clustering in the space of local atomic environments. Our analysis reveals the formation of a previously unreported crystalline disordered phase, Li$_2$S$_{0.72}$P$_{0.14}$Cl$_{0.14}$, in the SEI, that evaded previous predictions based purely on thermodynamics, underscoring the importance of explicit modeling of full reaction and transport kinetics. Our simulations agree with and explain experimental observations of the SEI formations and elucidate the Li creep mechanisms, critical to dendrite initiation, characterized by significant Li motion along the interface. Our approach is to crease a digital twin from first principles, without adjustable parameters fitted to experiment. As such, it offers capabilities to gain insights into atomistic dynamics governing complex heterogeneous processes in solid-state synthesis and electrochemistry.

To help combat climate change, many car manufacturers are racing to add more electric vehicles in their lineups. But to convince prospective buyers, manufacturers need to improve how far these cars can go on a single charge. Figuring out how to make extremely powerful but lightweight batteries. Typically, however, it takes decades for scientists to thoroughly test new battery materials, says Pablo Leon, an MIT graduate student in materials science. To accelerate this process, Leon is developing a machine-learning tool for scientists to automate one of the most time-consuming, yet key, steps in evaluating battery materials.

Nissan Motor Co. is betting that its experience pioneering lithium-ion batteries for electric vehicles over a decade ago will give it an upper hand in producing a new battery type that, despite being new and still relatively unproven, is considered by some as key to unlocking the future potential of EVs. Nissan is producing prototype solid-state battery cells -- which replace the electrical current-conducting liquid found in conventional batteries with a solid substance -- at a facility resembling a pop-up lab inside its research grounds near its Yokohama headquarters. The Japanese automaker plans to bring the new type of batteries to market by fiscal year 2028, readying a pilot plant for them ahead of that around 2024. If they can be manufactured, solid-state batteries would unlock cheaper, safer and faster-charging EVs, according to automotive executives and battery experts. Using different material combinations, Nissan predicts it will eventually be able to produce a solid-state battery pack that costs $65 (¥8,063) per kilowatt-hour -- a level at which analysts say EVs could reach price parity with gasoline-engine cars.

To discover materials for better batteries, researchers must wade through a vast field of candidates. New research demonstrates a machine learning technique that could more quickly surface ones with the most desirable properties. The study could accelerate designs for solid-state batteries, a promising next-generation technology that has the potential to store more energy than lithium-ion batteries without the flammability concerns. However, solid-state batteries encounter problems when materials within the cell interact with each other in ways that degrade performance. Researchers from the National Renewable Energy Laboratory (NREL), the Colorado School of Mines, and the University of Illinois demonstrated a machine learning method that can accurately predict the properties of inorganic compounds.

Volkswagen is lifting the lid, ever so slightly, of its future electric car plans. Project Trinity is VW's next-generation of electric car technology, similar to the MEB platform that currently underpins the all-electric ID.3 and ID.4, but cheaper to build, able to support a greater range of vehicles, and crucially - from VW's point of view - able to undertake more of the driving for you. Project Trinity is distantly related to Audi's Project Artemis, in that both are focused as much on the software that controls the car, and that communicates with you the driver (or is owner, even user, now a better word?) and other road users. "Trinity is going to be a time machine," said VW brand boss Dr Ralf Brandstatter. "Trinity gives people time, and takes away the stress. So, after a long motorway drive for example, they are relaxed when they arrive at their destination. "You switch on the system when you enter the motorway, and then the system will take over, and let you know when you need to leave the motorway.

Which technologies have the greatest potential to transform the world over the next decade? Here's what developers really think about AWS, Microsoft Azure, and Google Cloud Platform providers lack adequate support resources for developers. Research and advisory firm Lux Research set out to find the answer, applying its in-house data analysis platform and the expertise of its global technical team to identify and rank the 18 most transformative technologies. The firm's newly released "18 for 2018" report covers everything "from current rock stars of innovation to hidden gem technologies." At the top of the list of potentially transformative technologies is machine learning and deep neural networks.

Which technologies have the greatest potential to transform the world over the next decade? The National Football League is teaming up with Sleep Number to help its players use big data and machine learning to improve their sleep and boost performance. Research and advisory firm Lux Research set out to find the answer, applying its in-house data analysis platform and the expertise of its global technical team to identify and rank the 18 most transformative technologies. The firm's newly released "18 for 2018" report covers everything "from current rock stars of innovation to hidden gem technologies." At the top of the list of potentially transformative technologies is machine learning and deep neural networks.

As technology tries to maintain its dizzying ascent, one dead weight has kept its altitude in check: the battery. Our chips keep getting faster and our data rates keep climbing, but at the end of the day--or worse, by mid-afternoon--those power meters on our screens inevitably turn to red. Every great device, gadget, electric car, and robot would be even greater if batteries didn't suck so badly. Despite a steady flow of rumors that transformative breakthroughs are just around the corner, progress has moved at the pace of a tar flow. Steven Levy is Backchannel's founder and Editor in Chief. Sign up to get Backchannel's weekly newsletter.