Computational catalysis and machine learning communities have made considerable progress in developing machine learning models for catalyst discovery and design. Yet, a general machine learning potential that spans the chemical space of catalysis is still out of reach. A significant hurdle is obtaining access to training data across a wide range of materials. One important class of materials where data is lacking are oxides, which inhibits models from studying the Oxygen Evolution Reaction and oxide electrocatalysis more generally. To address this we developed the Open Catalyst 2022(OC22) dataset, consisting of 62,521 Density Functional Theory (DFT) relaxations ( 9,884,504 single point calculations) across a range of oxide materials, coverages, and adsorbates (*H, *O, *N, *C, *OOH, *OH, *OH2, *O2, *CO).

Laser machining is a highly flexible non-contact manufacturing technique that has been employed widely across academia and industry. Due to nonlinear interactions between light and matter, simulation methods are extremely crucial, as they help enhance the machining quality by offering comprehension of the inter-relationships between the laser processing parameters. On the other hand, experimental processing parameter optimization recommends a systematic, and consequently time-consuming, investigation over the available processing parameter space. An intelligent strategy is to employ machine learning (ML) techniques to capture the relationship between picosecond laser machining parameters for finding proper parameter combinations to create the desired cuts on industrial-grade alumina ceramic with deep, smooth and defect-free patterns. Laser parameters such as beam amplitude and frequency, scanner passing speed and the number of passes over the surface, as well as the vertical distance of the scanner from the sample surface, are used for predicting the depth, top width, and bottom width of the engraved channels using ML models. Owing to the complex correlation between laser parameters, it is shown that Neural Networks (NN) are the most efficient in predicting the outputs. Equipped with an ML model that captures the interconnection between laser parameters and the engraved channel dimensions, one can predict the required input parameters to achieve a target channel geometry. This strategy significantly reduces the cost and effort of experimental laser machining during the development phase, without compromising accuracy or performance. The developed techniques can be applied to a wide range of ceramic laser machining processes.

Concrete, as the most widely used construction material, is inextricably connected with human development. Despite conceptual and methodological progress in concrete science, concrete formulation for target properties remains a challenging task due to the ever-increasing complexity of cementitious systems. With the ability to tackle complex tasks autonomously, machine learning (ML) has demonstrated its transformative potential in concrete research. Given the rapid adoption of ML for concrete mixture design, there is a need to understand methodological limitations and formulate best practices in this emerging computational field. Here, we review the areas in which ML has positively impacted concrete science, followed by a comprehensive discussion of the implementation, application, and interpretation of ML algorithms. We conclude by outlining future directions for the concrete community to fully exploit the capabilities of ML models.

No matter how conscientious the consumer, by the time the material gets to the end of the conveyor belt at the recycling plant, most plastics end up mixed together. Due to the rather rudimentary sorting techniques in use, only a small percentage of the plastic we try to recycle ends up getting recycled. "The ordinary consumer, with the best intentions--and also the correct procedure--puts everything in the plastic bin. We get it all," said Mogens Hinge, an associate professor in the department of biological and chemical engineering and process and materials engineering at Denmark's Aarhus University, and co-author of the paper "Plastic classification via in-line hyperspectral camera analysis and unsupervised machine learning," which appeared in Vibrational Spectroscopy this year. "Now we have a problem: we can wash it, but we can't unmix it. And plastic is not just plastic."

Macro-operators (macros) are a well-known technique for enhancing performance of planning engines by providing "short-cuts" in the state space. Existing macro learning systems usually generate macros by considering most frequent action sequences in training plans. Unfortunately, frequent action sequences might not capture meaningful activities as a whole, leading to a limited beneficial impact for the planning process. In this paper, inspired by resource locking in critical sections in parallel computing, we propose a technique that generates macros able to capture whole activities in which limited resources (e.g., a robotic hand, or a truck) are used. Specifically, such a Critical Section macro starts by locking the resource (e.g., grabbing an object), continues by using the resource (e.g., manipulating the object) and finishes by releasing the resource (e.g., dropping the object). Hence, such a macro bridges states in which the resource is locked and cannot be used. We also introduce versions of Critical Section macros dealing with multiple resources and phased locks. Usefulness of macros is evaluated using a range of state-of-the-art planners, and a large number of benchmarks from the deterministic and learning tracks of recent editions of the International Planning Competition.

MineExcellence, which is a leading provider of digital technologies for mining, with a special focus on drill and blast operations, in the controlled use of explosives to break rock for excavation, has established a collaboration with A2I2 to enhance the efficiency and safety of drill and blast processes, using AI and machine learning (ML). The mining industry is increasingly using AI and ML as tools to optimise processes, enhance decision-making, derive value from data, and improve safety. A memorandum of understanding between the two organisations was recently signed by MineExcellence MD Amit Bhandari and Deakin Research Innovations senior manager of commercialisation Greg Pullen. A2I2 head of Translational Research Professor Rajesh Vasa highlighted the importance of this collaboration and the core aim of the research project. "The collaboration between Mine Excellence and A2I2 includes joint research, industry connections and PhD placements. "Our research in this area aims to pioneer new methods and approaches that deliver high impact results to drilling and blasting, making the process safer and more efficient." Initial work has already started through a co-funded PhD student based at A 2I2 and working in collaboration with the industry. MineExcellence will provide its drill and blast domain knowledge as well as its digital platform. AI models will be developed in collaboration with A2I2, with support from drill and blast expert Professor Sushil Bhandari as external supervisor. Vasa said A2I2 has a solid capacity to apply novel AI techniques to the mining sector. "A2I2 has a proven track record of positively impacting society, spanning health, education, and defence technologies.

A trial of Maptek DomainMCF at an underground metals mine has concluded that machine learning will most likely become the preferred modelling …

The AI system is replacing human creativity. DALL•E links users with AI tools to create and share AI architecture. Machine Learning tools ready to use

Autonomous ships just took a small but important step forward. Hyundai's Avikus subsidiary says it has completed the world's first autonomous navigation of a large ship across the ocean. The Prism Courage (pictured) left Freeport in the Gulf of Mexico on May 1st, and used Avikus' AI-powered HiNAS 2.0 system to steer the vessel for half of its roughly 12,427-mile journey to the Boryeong LNG Terminal in South Korea's western Chungcheong Province. The Level 2 self-steering tech was good enough to account for other ships, the weather and differing wave heights. The autonomy spared the crew some work, of course, but it may also have helped the planet. Avikus claims HiNAS' optimal route planning improved the Prism Courage's fuel efficiency by about seven percent, and reduced emissions by five percent.

If business wants to use AI at scale, adhering to the technical guidelines for responsible AI development isn't enough. It must obtain society's explicit approval to deploy the technology. Six years ago, in March 2016, Microsoft Corporation launched an experimental AI-based chatbot, TayTweets, whose Twitter handle was @TayandYou. Tay, an acronym for "thinking about you," mimicked a 19-year-old American girl online, so the digital giant could showcase the speed at which AI can learn when it interacts with human beings. Living up to its description as "AI with zero chill," Tay started off replying cheekily to Twitter users and turning photographs into memes. Some topics were off limits, though; Microsoft had trained Tay not to comment on societal issues such as Black Lives Matter. Soon enough, a group of Twitter users targeted Tay with a barrage of tweets about controversial issues such as the Holocaust and Gamergate. They goaded the chatbot into replying with racist and sexually charged responses, exploiting its repeat-after-me capability. Realizing that Tay was reacting like IBM's Watson, which started using profanity after perusing the online Urban Dictionary, Microsoft was quick to delete the first inflammatory tweets. Less than 16 hours and more than 100,000 tweets later, the digital giant shut down Tay.