Stirling Distillery has two years to figure it out. Breakthroughs, discoveries, and DIY tips sent six days a week. Scotland's smallest whisky distillery also hopes to be one of the most innovative in time for its first batch's debut. But with only around two years until Sterling Distillery's inaugural liquor matures, it remains to be seen if the company can ditch traditional glass bottles for a material associated more with cheap beer than fine whisky--aluminum. Any serious distillery uses glass bottles for the good stuff.

We got a sneak peek inside Found Energy's lab, just as it gears up to supply heat and hydrogen to its first customer. The crushed-up soda can disappears in a cloud of steam and--though it's not visible--hydrogen gas. "I can just keep this reaction going by adding more water," says Peter Godart, squirting some into the steaming beaker. "This is room-temperature water, and it's immediately boiling. Doing this on your stove would be slower than this." Godart is the founder and CEO of Found Energy, a startup in Boston that aims to harness the energy in scraps of aluminum metal to power industrial processes without fossil fuels.

A few months ago, I flew to Houston to visit a small startup called Lunar Resources, which aspires to build the largest telescope in the solar system--not on Earth but on the far side of the moon. Houston is nicknamed Space City; on the ride from the airport, I passed the ballpark where the Astros play, and, outside a McDonald's on East NASA Parkway, I saw a giant sculpture of an astronaut holding French fries. I found Lunar Resources in a boxy building where the company leases square footage from the aerospace contractor Lockheed Martin. Elliot Carol, the C.E.O. and co-founder of Lunar Resources, is thirty-three, with a cherubic face and curly hair speckled with gray. Although he grew up in Connecticut and previously worked as a hedge-fund manager, he was wearing black cowboy boots.

Machine learning (ML) techniques and atomistic modeling have rapidly transformed materials design and discovery. Specifically, generative models can swiftly propose promising materials for targeted applications. However, the predicted properties of materials through the generative models often do not match with calculated properties through ab initio calculations. This discrepancy can arise because the generated coordinates are not fully relaxed, whereas the many properties are derived from relaxed structures. Neural network-based potentials (NNPs) can expedite the process by providing relaxed structures from the initially generated ones. Nevertheless, acquiring data to train NNPs for this purpose can be extremely challenging as it needs to encompass previously unknown structures. This study utilized extended ensemble molecular dynamics (MD) to secure a broad range of liquid- and solid-phase configurations in one of the metallic systems, nickel. Then, we could significantly reduce them through active learning without losing much accuracy. We found that the NNP trained from the distilled data could predict different energy-minimized closed-pack crystal structures even though those structures were not explicitly part of the initial data. Furthermore, the data can be translated to other metallic systems (aluminum and niobium), without repeating the sampling and distillation processes. Our approach to data acquisition and distillation has demonstrated the potential to expedite NNP development and enhance materials design and discovery by integrating generative models.

We present an algorithm for computing melting points by autonomously learning from coexistence simulations in the NPT ensemble. Given the interatomic interaction model, the method makes decisions regarding the number of atoms and temperature at which to conduct simulations, and based on the collected data predicts the melting point along with the uncertainty, which can be systematically improved with more data. We demonstrate how incorporating physical models of the solid-liquid coexistence evolution enhances the algorithm's accuracy and enables optimal decision-making to effectively reduce predictive uncertainty. To validate our approach, we compare the results of 20 melting point calculations from the literature to the results of our calculations, all conducted with same interatomic potentials. Remarkably, we observe significant deviations in about one-third of the cases, underscoring the need for accurate and reliable algorithms for materials property calculations.

X-ray imaging is widely used for non-destructive detection of defects in industrial products on a conveyor belt. Real-time detection requires highly accurate, robust, and fast algorithms to analyze X-ray images. Deep convolutional neural networks (DCNNs) satisfy these requirements if a large amount of labeled data is available. To overcome the challenge of collecting these data, different methods of X-ray image generation can be considered. Depending on the desired level of similarity to real data, various physical effects either should be simulated or can be ignored. X-ray scattering is known to be computationally expensive to simulate, and this effect can heavily influence the accuracy of a generated X-ray image. We propose a methodology for quantitative evaluation of the effect of scattering on defect detection. This methodology compares the accuracy of DCNNs trained on different versions of the same data that include and exclude the scattering signal. We use the Probability of Detection (POD) curves to find the size of the smallest defect that can be detected with a DCNN and evaluate how this size is affected by the choice of training data. We apply the proposed methodology to a model problem of defect detection in cylinders. Our results show that the exclusion of the scattering signal from the training data has the largest effect on the smallest detectable defects. Furthermore, we demonstrate that accurate inspection is more reliant on high-quality training data for images with a high quantity of scattering. We discuss how the presented methodology can be used for other tasks and objects.

One of my favorite projects is also a wonderful use case to analyze if Industrial Cloud is feasible. With my background in the automotive industry and industrial automation, it should be no surprise that this relates to car part manufacturing. After joining AWS re:invent as analyst where I focused on Industrial Machine Learning and Cloud in Manufacturing, I decide to revisit this project and give you an update on this USE CASE. After a very successful pilot project to optimize the process of filling casting machines with liquid aluminum, the team was eager to bring the solution to other facilities. And with that goal in mind, the team also realized that it was necessary to automate the learning process.

Researchers at University of Toronto Engineering and Carnegie Mellon University are using artificial intelligence (AI) to accelerate progress in transforming waste carbon into a commercially valuable product with record efficiency. They leveraged AI to speed up the search for the key material in a new catalyst that converts carbon dioxide (CO2) into ethylene -- a chemical precursor to a wide range of products, from plastics to dish detergent. The resulting electrocatalyst is the most efficient in its class. If run using wind or solar power, the system also provides an efficient way to store electricity from these renewable but intermittent sources. "Using clean electricity to convert CO2 into ethylene, which has a $60 billion global market, can improve the economics of both carbon capture and clean energy storage," says Professor Ted Sargent, one of the senior authors on a new paper published today in Nature.

Using an AI design process, engineers at software company Autodesk and NASA's Jet Propulsion Laboratory came up with a new interplanetary lander concept that could explore distant moons like Europa and Enceladus. Its slim design weighs less than most of the landers that NASA has already sent to other planets and moons. Autodesk announced its new innovative lander design today at the company's conference in Las Vegas -- revealing a spacecraft that looks like a spider woven from metal. The company says the idea to create the vehicle was sparked when Autodesk approached NASA to validate a lander prototype it had been working on. After looking at Autodesk's work, JPL and the company decided to form a design team -- comprised of five engineers from Autodesk and five from JPL -- to come up with a new way to design landers.

A futuristic, spider-like spacecraft could one day discover new insights about the moons of Saturn and Jupiter. NASA and software company Autodesk have unveiled a concept lander that's ultra lightweight and created, in part, thanks to innovative AI software. They say the lander has been expertly designed to be able to handle missions to distant planets. NASA and Autodesk have unveiled a concept lander that's ultra lightweight and created, in part, thanks to innovative AI software. NASA's Jet Propulsion Laboratory is a research and development center founded in 1936.