Collaborating Authors


High-speed alloy creation might revolutionize hydrogen's future


A Sandia National Laboratories team of materials scientists and computer scientists, with some international collaborators, have spent more than a year creating 12 new alloys -- and modeling hundreds more -- that demonstrate how machine learning can help accelerate the future of hydrogen energy by making it easier to create hydrogen infrastructure for consumers. Vitalie Stavila, Mark Allendorf, Matthew Witman and Sapan Agarwal are part of the Sandia team that published a paper detailing its approach in conjunction with researchers from Ångström Laboratory in Sweden and Nottingham University in the United Kingdom. "There is a rich history in hydrogen storage research and a database of thermodynamic values describing hydrogen interactions with different materials," Witman said. "With that existing database, an assortment of machine-learning and other computational tools, and state-of-the art experimental capabilities, we assembled an international collaboration group to join forces on this effort. We demonstrated that machine learning techniques could indeed model the physics and chemistry of complex phenomena which occur when hydrogen interacts with metals."

Sustainability and technology go hand in hand


Industrial Revolution 4.0 (IR 4.0) may not often be associated with climate change mitigation, but its use of technologies such as the Internet of Things (IoT), big data, artificial intelligence (AI) and cloud computing can actually play a pivotal role. Smart factories equipped with IR 4.0 capabilities can be more efficient and effective than ever before, ensuring that no energy or materials are wasted, observes Datuk Mohd Abdul Karim Abdullah, CEO of Serba Dinamik Holdings Bhd. Clean energy can also be integrated with IR 4.0 to power various processes and the transport of goods to the final consumer. "Investing in research and development to bring more awareness of how technology can encourage reuse, reduce, recycle and replace principles so that there is effective use of raw materials and energy is important," he says. IR 4.0 creates more efficiency and improves the way businesses are run.

Evaluating uncertainties in electrochemical impedance spectra of solid oxide fuel cells Machine Learning

Electrochemical impedance spectra is a widely used tool for characterization of fuel cells and electrochemical conversion systems in general. When applied to the on-line monitoring in context of in-field applications, the disturbances, drifts and sensor noise may cause severe distortions in the evaluated spectra, especially in the low-frequency part. Failure to account for the random effects can implicate difficulties in interpreting the spectra and misleading diagnostic reasoning. In the literature, this fact has been largely ignored. In this paper, we propose a computationally efficient approach to the quantification of the spectral uncertainty by quantifying the uncertainty of the equivalent circuit model (ECM) parameters by means of the Variational Bayes (VB) approach. To assess the quality of the VB posterior estimates, we compare the results of VB approach with those obtained with the Markov Chain Monte Carlo (MCMC) algorithm. Namely, MCMC algorithm is expected to return accurate posterior distributions, while VB approach provides the approximative distributions. By using simulated and real data we show that VB approach generates approximations, which although slightly over-optimistic, are still pretty close to the more realistic MCMC estimates. A great advantage of the VB method for online monitoring is low computational load, which is several orders of magnitude lighter than that of MCMC. The performance of VB algorithm is demonstrated on a case of ECM parameters estimation in a 6 cell solid-oxide fuel cell stack. The complete numerical implementation for recreating the results can be found at

5G: Using drones to beam signals from the stratosphere


Plans to beam 5G signals to the public via drones that stay airborne for nine days at a time have been announced by two UK firms. They want to use antenna-equipped aircraft powered by hydrogen to deliver high-speed connectivity to wide areas. Stratospheric Platforms and Cambridge Consultants say they could cover the whole of the UK with about 60 drones. But telecoms analysts question whether the economic case for this scheme is quite as simple as it sounds. The Cambridge-based companies say they would run the service in partnership with existing mobile operators. They are already backed by Deutsche Telekom, which hopes to trial the technology in rural southern Germany in 2024.

These drones could beam down 5G from high in the sky


While countries around the world ponder how to build up secure, reliable, and cost-efficient infrastructure for their future 5G networks, two companies in the UK have come up with a rather unorthodox solution to deliver faster connectivity. Engineering firm Cambridge Consultants and telecommunications company Stratospheric Platforms Limited (SPL) have unveiled a new proof-of-concept, which could see 5G beams broadcast from the skies thanks to antennas fitted onto drones flying some 20,000 meters above the ground. The prototype that has been tested so far is only one eighth of the intended full size, but the companies hope that the final product will come in the form of a three square-meter antenna capable of beaming 5G directly onto areas up to 140 kilometers in diameter. The antennas will be integrated into zero-emission aircraft powered by hydrogen, and capable of carrying the equipment for up to nine days in a row. According to Cambridge Consultants, a fleet of 60 aircrafts equipped with antennas would be enough to blanket the whole of the UK with 5G connectivity, delivering mobile speeds evenly across the country in excess of 100 Gbps.

Facebook deploys its AI to find green energy storage solutions


Our traditional solution to the unpredictable nature of renewable energy sources like solar and wind power has generally been to simply dump the excess wattage back into the local grid or sequester it away in utility-scale batteries. But as more and more of our power generation is created by renewables, their production capacities can potentially outstrip that of the local grid while battery technology can quickly become prohibitively expensive at scale. One alternative is putting that excess power to work driving catalytic reactions. "There are a lot of different ways that we can store the energy," Zack Ulissi, CMU Assistant Professor of Chemical Engineering and Materials Science and Engineering, told Engadget. "The most well known is you take water and you electrolyze it to split it into hydrogen and oxygen. And then you can take that hydrogen and run it into a hydrogen fuel cell."

An Environmentally Sustainable Closed-Loop Supply Chain Network Design under Uncertainty: Application of Optimization Artificial Intelligence

Newly, the rates of energy and material consumption to augment industrial pro-duction are substantially high, thus the environmentally sustainable industrial de-velopment has emerged as the main issue of either developed or developing coun-tries. A novel approach to supply chain management is proposed to maintain economic growth along with environmentally friendly concerns for the design of the supply chain network. In this paper, a new green supply chain design approach has been suggested to maintain the financial virtue accompanying the environ-mental factors that required to be mitigated the negative effect of rapid industrial development on the environment. This approach has been suggested a multi-objective mathematical model minimizing the total costs and CO2 emissions for establishing an environmentally sustainable closed-loop supply chain. Two opti-mization methods are used namely Epsilon Constraint Method, and Genetic Al-gorithm Optimization Method. The results of the two mentioned methods have been compared and illustrated their effectiveness. The outcome of the analysis is approved to verify the accuracy of the proposed model to deal with financial and environmental issues concurrently.

How Machine Learning Can Improve the Efficiency of Fuel Cells?


The world is adapting itself to the digital age changes. We are getting more familiar with the terms of the disruptive technologies that are making it happen. Internet of things (IoT) is one of them. The term was coined by in 1999 by Kevin Ashton, a British technologist. It refers to the connected ecosystem of devices and gadgets, which is benefiting businesses and industries of all types. These devices can be RFID chips, smart devices, or mobile sensors.

AI could help improve performance of lithium-ion batteries and fuel cells


Images from both the cathode and anode samples which show real and algorithm-generated microstructures. Imperial researchers have demonstrated how machine learning could help design lithium-ion batteries and fuel cells with better performance. A new machine learning algorithm allows researchers to explore possible designs for the microstructure of fuel cells and lithium-ion batteries, before running 3D simulations that help researchers make changes to improve performance. Improvements could include making smartphones charge faster, increasing the time between charges for electric vehicles, and increasing the power of hydrogen fuel cells running data centres. The paper is published in npj Computational Materials.

AI could help improve performance of lithium-ion batteries and fuel cells


A new machine learning algorithm allows researchers to explore possible designs for the microstructure of fuel cells and lithium-ion batteries, before running 3-D simulations that help researchers make changes to improve performance. Improvements could include making smartphones charge faster, increasing the time between charges for electric vehicles, and increasing the power of hydrogen fuel cells running data centers. The paper is published today in npj Computational Materials. Fuel cells use clean hydrogen fuel, which can be generated by wind and solar energy, to produce heat and electricity, and lithium-ion batteries, like those found in smartphones, laptops, and electric cars, are a popular type of energy storage. The performance of both is closely related to their microstructure: how the pores (holes) inside their electrodes are shaped and arranged can affect how much power fuel cells can generate, and how quickly batteries charge and discharge. However, because the micrometer-scale pores are so small, their specific shapes and sizes can be difficult to study at a high enough resolution to relate them to overall cell performance.