Heating and lighting buildings requires a vast amount of energy: 18% of all global energy consumption, according to the International Energy Agency. Contributing to the problem is the fact that many buildings' HVAC systems are outdated and slow to respond to weather changes, which can lead to severe energy waste. Some scientists and technologists are hoping that AI can solve that problem. At the moment, much attention has been drawn to the energy-intensive nature of AI itself: Microsoft, for instance, acknowledged that its AI development has imperiled their climate goals. But some experts argue that AI can also be part of the solution by helping make large buildings more energy-efficient.

The Artificial Intelligence Ontology (AIO) is a systematization of artificial intelligence (AI) concepts, methodologies, and their interrelations. Developed via manual curation, with the additional assistance of large language models (LLMs), AIO aims to address the rapidly evolving landscape of AI by providing a comprehensive framework that encompasses both technical and ethical aspects of AI technologies. The primary audience for AIO includes AI researchers, developers, and educators seeking standardized terminology and concepts within the AI domain. The ontology is structured around six top-level branches: Networks, Layers, Functions, LLMs, Preprocessing, and Bias, each designed to support the modular composition of AI methods and facilitate a deeper understanding of deep learning architectures and ethical considerations in AI. AIO's development utilized the Ontology Development Kit (ODK) for its creation and maintenance, with its content being dynamically updated through AI-driven curation support. This approach not only ensures the ontology's relevance amidst the fast-paced advancements in AI but also significantly enhances its utility for researchers, developers, and educators by simplifying the integration of new AI concepts and methodologies. The ontology's utility is demonstrated through the annotation of AI methods data in a catalog of AI research publications and the integration into the BioPortal ontology resource, highlighting its potential for cross-disciplinary research.

Researchers at Google DeepMind have used artificial intelligence to predict the structures of more than 2 million new materials, in a breakthrough that could have wide-reaching benefits in sectors such as renewable energy and computing. DeepMind published 381,000 of the 2.2 million crystal structures that it predicts to be most stable. The breakthrough increases the number of known stable materials by a factor of ten. Although the materials will still need to be synthesized and tested, steps which can take months or even years, the latest development is expected to accelerate the discovery of new materials, which will be required for applications such as energy storage, solar cells, and superconductor chips. "While materials play a very critical role in almost any technology, we as humanity know only about a few tens of thousands of stable materials," says Ekin Dogus Cubuk, a Staff Research Scientist at Google Brain, who worked on the DeepMind AI tool, known as Graph Networks for Materials Exploration (GNoME).

Soil and groundwater contamination is a pervasive problem at thousands of locations across the world. Contaminated sites often require decades to remediate or to monitor natural attenuation. Climate change exacerbates the long-term site management problem because extreme precipitation and/or shifts in precipitation/evapotranspiration regimes could re-mobilize contaminants and proliferate affected groundwater. To quickly assess the spatiotemporal variations of groundwater contamination under uncertain climate disturbances, we developed a physics-informed machine learning surrogate model using U-Net enhanced Fourier Neural Operator (U-FNO) to solve Partial Differential Equations (PDEs) of groundwater flow and transport simulations at the site scale.We develop a combined loss function that includes both data-driven factors and physical boundary constraints at multiple spatiotemporal scales. Our U-FNOs can reliably predict the spatiotemporal variations of groundwater flow and contaminant transport properties from 1954 to 2100 with realistic climate projections. In parallel, we develop a convolutional autoencoder combined with online clustering to reduce the dimensionality of the vast historical and projected climate data by quantifying climatic region similarities across the United States. The ML-based unique climate clusters provide climate projections for the surrogate modeling and help return reliable future recharge rate projections immediately without querying large climate datasets. In all, this Multi-scale Digital Twin work can advance the field of environmental remediation under climate change.

Scientists have developed a new machine-learning platform that makes the algorithms that control particle beams and lasers smarter than ever before. Their work could help lead to the development of new and improved particle accelerators that will help scientists unlock the secrets of the subatomic world. Daniele Filippetto and colleagues at the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) developed the setup to automatically compensate for real-time changes to accelerator beams and other components, such as magnets. Their machine learning approach is also better than contemporary beam control systems at both understanding why things fail, and then using physics to formulate a response. A paper describing the research was published late last year in Nature Scientific Reports.

A computer is suspended from the ceiling. Delicate lines and loops of silvery wires and tubes connect gold-colored platforms. It seems to belong in a science-fiction movie, perhaps a steam-punk cousin of HAL in 2001: A Space Odyssey. But as the makers of that 1968 movie imagined computers the size of a spaceship, this technology would have never crossed their minds – a quantum computer. Quantum computers have the potential to solve problems that conventional computers can't.

The U.S. Department of Energy announced $15.1 million for three collaborative research projects at five universities to advance the development of a flexible multi-tiered data and computational infrastructure to support a diverse collection of on-demand scientific data processing tasks and computationally intensive simulations. Scientists from The University of Texas–Austin, the University of Notre Dame, Louisiana State University, and Lawrence Berkeley National Laboratory will address mitigation strategies for gulf coastal flooding events due to extreme weather with artificial intelligence and machine learning techniques that combine experimental data with computer simulations. Scientists from the University of Connecticut and Lawrence Berkeley National Laboratory will couple experimental data with simulations using AI/ML techniques to design, manufacture, and test new materials with uniquely designed properties for potential applications in batteries, sensors, and energy storage. Scientists from the University of Southern California, Argonne National Laboratory, and Lawrence Berkeley National Laboratory will develop AI/ML-based methods to simulate and experimentally verify the performance of large, distributed computing infrastructures.

In a September 2020 essay in Nature Energy, three scientists posed several "grand challenges" -- one of which was to find suitable materials for thermal energy storage devices that could be used in concert with solar energy systems. Fortuitously, Mingda Li -- the Norman C. Rasmussen Assistant Professor of Nuclear Science and Engineering at MIT, who heads the department's Quantum Matter Group -- was already thinking along similar lines. In fact, Li and nine collaborators (from MIT, Lawrence Berkeley National Laboratory, and Argonne National Laboratory) were developing a new methodology, involving a novel machine-learning approach, that would make it faster and easier to identify materials with favorable properties for thermal energy storage and other uses. The results of their investigation appear this month in a paper for Advanced Science. "This is a revolutionary approach that promises to accelerate the design of new functional materials," comments physicist Jaime Fernandez-Baca, a distinguished staff member at Oak Ridge National Laboratory.

Machine learning key to discovery of over 1200 gravitational lenses. Data from the DESI (Dark Energy Spectroscopic Instrument) Legacy Imaging Surveys have revealed over 1200 new gravitational lenses, approximately doubling the number of known lenses. Discovered using machine learning trained on rea

URBANA, Ill. and BERKELEY, Calif., June 23, 2020 – C3.ai Digital Transformation Institute (C3.ai DTI awards for artificial intelligence (AI) techniques to mitigate the COVID-19 pandemic. C3.ai DTI, jointly managed by the University of Illinois at Urbana-Champaign and the University of California, Berkeley, and in partnership with Microsoft Corp., invited researchers in March to take on the challenge of abating COVID-19 and advancing AI-based science and technologies for mitigating future pandemics. After a rigorous peer review process, C3.ai DTI selected 26 research proposals that address COVID-19 across the disciplines of medicine, urban planning, public policy, and computer science, several of which focus on the study of the disease's impact on racial, economic, and healthcare disparities. A total of $5.4 million in cash will be awarded to the following research projects.