Newswise -- According to a release issued earlier this month by the Los Alamos National Laboratory (LANL), researchers have developed a machine learning approach called transfer learning that lets them model novel materials by learning from data collected about millions of other compounds. The new approach can be applied to new molecules in milliseconds, enabling research into a far greater number of compounds over much longer timescales. The new technique, called ANI-1ccx potential, promises to advance the capabilities of researchers in many fields and improve the accuracy of machine learning-based potentials in future studies of metal alloys and detonation physics. "Our quantum mechanical calculations to create ANI-1ccx potential were conducted over two years with time split on the Comet supercomputer at the San Diego Supercomputer Center and the Badger supercomputer at LANL," said Olexandr Isayev, paper author and a pharmacy professor at the University of North Carolina at Chapel Hill. "We chose these two supercomputers to train our neural networks as there are few machines that can run these – due to the high memory and core requirements."

This report on global Drone Analytics market is a detailed research study that helps provides answers and pertinent questions with respect to the emerging trends and growth opportunities in this particular industry. It helps identify each of the prominent barriers to growth, apart from identifying the trends within various application segments of the global market. The global Drone Analytics market size was 2.3 million US$ and it is expected to reach 5.6 million US$ by the end of 2025, with a CAGR of 10.4% during 2019-2025. Based on industry, the drone analytics market has been segmented into agriculture & forestry, construction, insurance, mining & quarrying, utility, telecommunication, oil & gas, transportation, scientific research, and others. The construction segment is projected to grow at the highest CAGR during the forecast period.

Over the past decade, rapid advancements have made it possible for AI systems to do things we once only dreamed about. However, much of the hype around AI and machine learning tends to focus on its potential for business, productivity, and profits. Perhaps there should be more spotlight on how we can use AI for good. AI has the power to tackle many of the biggest problems on the planet and could make a huge impact on sustainability, our environment, and even humanity itself. As you'll see from the real-life examples in this post, robots, and humans are already showing they can be an incredible team.

The chemical and allied industries face such challenges as ready access to reliable energy supplies, waste reduction, water conservation, and energy efficiency. Organic electrosynthesis--an electricity-driven, energy-efficient process that can easily integrate with renewable energy sources--could help solve them. A team at the NYU Tandon School of Engineering reported that in its search to develop an innovative, environmentally friendly process to make adiponitrile (ADN) - the main precursor to nylon 6, 6--it found a way to greatly improve the efficiency of organic electrosynthesis. The researchers credited their success in part to what they believe is the first use of artificial intelligence to optimize an electrochemical process. Miguel Modestino, a professor of chemical and biomolecular engineering, and doctoral student Daniela Blanco tweaked how electrical current is delivered to catalytic electrodes and then applied artificial intelligence (AI) to further optimize the reaction.

In the face of growing needs for water and energy, a fundamental understanding of the environmental impacts of human activities becomes critical for managing water and energy resources, remedying water pollution, and making regulatory policy wisely. Among activities that impact the environment, oil and gas production, wastewater transport, and urbanization are included. In addition to the occurrence of anthropogenic contamination, the presence of some contaminants (e.g., methane, salt, and sulfate) of natural origin is not uncommon. Therefore, scientists sometimes find it difficult to identify the sources of contaminants in the coupled natural and human systems. In this paper, we propose a technique to simultaneously conduct source detection and prediction, which outperforms other approaches in the interdisciplinary case study of the identification of potential groundwater contamination within a region of high-density shale gas development.

Parametric shape optimization aims at minimizing an objective function f(x) where x are CAD parameters. This task is difficult when f is the output of an expensive-to-evaluate numerical simulator and the number of CAD parameters is large. Most often, the set of all considered CAD shapes resides in a manifold of lower effective dimension in which it is preferable to build the surrogate model and perform the optimization. In this work, we uncover the manifold through a high-dimensional shape mapping and build a new coordinate system made of eigenshapes. The surrogate model is learned in the space of eigenshapes: a regularized likelihood maximization provides the most relevant dimensions for the output. The final surrogate model is detailed (anisotropic) with respect to the most sensitive eigenshapes and rough (isotropic) in the remaining dimensions. Last, the optimization is carried out with a focus on the critical dimensions, the remaining ones being coarsely optimized through a random embedding and the manifold being accounted for through a replication strategy. At low budgets, the methodology leads to a more accurate model and a faster optimization than the classical approach of directly working with the CAD parameters.

Recently developed machine learning techniques, in association with the Internet of Things (IoT) allow for the implementation of a method of increasing oil production from heavy-oil wells. Steam flood injection, a widely used enhanced oil recovery technique, uses thermal and gravitational potential to mobilize and dilute heavy oil in situ to increase oil production. In contrast to traditional steam flood simulations based on principles of classic physics, we introduce here an approach using cutting-edge machine learning techniques that have the potential to provide a better way to describe the performance of steam flood. We propose a workflow to address a category of time-series data that can be analyzed with supervised machine learning algorithms and IoT. We demonstrate the effectiveness of the technique for forecasting oil production in steam flood scenarios. Moreover, we build an optimization system that recommends an optimal steam allocation plan, and show that it leads to a 3% improvement in oil production. We develop a minimum viable product on a cloud platform that can implement real-time data collection, transfer, and storage, as well as the training and implementation of a cloud-based machine learning model. This workflow also offers an applicable solution to other problems with similar time-series data structures, like predictive maintenance.

In its next major breakthrough, Artificial Intelligence (AI) is defined to interrupt the battery technology distance, by combining the power of predictive intelligence and information analytics to accomplish high-performance and operational reliability. OEMs, battery pack makers, electrical fleet supervisors, and Electric Vehicle (EV) manufacturers will leverage AI, information engineering and machine learning how to remarkably enhance the battery's functionality & acquire much better ROI through all phases of the battery life cycle. With significant development and conscious efforts being led towards sustainable living and authorities pushing for fresh freedom, the worldwide EV market has been valued is estimated to reach 567,299.8 million by 2025, increasing at a CAGR of 22.3percent from 2018 to 2025. The EV uptake indicates a substantial transition in battery production volumes and improved investment in battery technologies, which is critical as EVs are costly and the price of the battery figures to 40 percent of the entire vehicle price. Lithium-ion batteries, that power high-resolution solutions such as EVs, houses & big solar/wind micro-grids, have among the maximum energy densities of almost any battery technology, a comparatively low self-discharge, & needs minimal maintenance.

WASHINGTON, D.C. – Today, the U.S. Department of Energy (DOE) announced a total of $20 million in funding for innovative research and development in artificial intelligence (A.I.) and machine learning. DOE's Office of Electricity has selected eight projects to receive nearly $7 million in total to explore the use of big data, artificial intelligence, and machine learning technologies to improve existing knowledge and discover new insights and tools for better grid operation and management. DOE's Office of Science announced a plan to provide $13 million in total funding for new research aimed at improving A.I. as a tool of scientific investigation and prediction. "Leveraging the power of artificial intelligence will revolutionize every single aspect of our lives and help us address the complex challenges we face today, including the world's most pressing scientific challenges and securing the power grid in our rapidly evolving environment," said U.S. Secretary of Energy Rick Perry. "These two sets of A.I. funding will help ensure continued advancement in the scientific fields and will strengthen the resilience of our Nation's critical energy infrastructure."

I envisioned an old Cadillac with massive Texas longhorns adorning the hood meandering along a dusty road. This road was in Egypt, and Stringfield was behind the wheel, sweat glistening on his brow as he hauled a load of freshly-baked sesame seed bagels. No, I hadn't been experimenting with some designer hallucinogen. But my conversation with him, as happens with particularly captivating sources, conjured evocative concepts and imagery, the kind of stuff that begs to be illustrated in word pictures. Thing is, although his latest enterprise encompassed many of the issues I aimed to address in my most recent feature story in MIT Technology Review -- such as fair labor in the AI industry, data ethics and the future of work -- his background as a former Halliburton executive who became a bagel-making entrepreneur during his time in Cairo as an HR consultant with the oil giant never made it into the story.