StoRIES Project’s Post

#AI | 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗔𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗲𝘀 𝗗𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝘆 𝗼𝗳 𝗦𝗼𝗹𝗮𝗿-𝗰𝗲𝗹𝗹 𝗣𝗲𝗿𝗼𝘃𝘀𝗸𝗶𝘁𝗲𝘀 | Researchers at EPFL, led by Haiyuan Wang and Alfredo Pasquarello, have leveraged machine learning to identify 14 new materials for solar cells, setting the stage for more efficient and cost-effective solar technology. The team developed an advanced dataset of band-gap values for perovskite materials through hybrid functionals, which accurately account for the material's electronic polarization properties. This approach significantly improved the accuracy of band-gap predictions over standard methods. Supported by the Swiss National Science Foundation SNSF and the National Natural Science Foundation of China, this research holds the potential to lower solar technology costs and speed up its adoption globally. 👉 Learn more >> https://lnkd.in/gxmmffHh 👉 Original publication >> https://lnkd.in/gvP8kZKr 🇨🇭 Follow #ScienceSwitzerland for the latest news and emerging trends on Swiss science, technology, education, and innovation >> www.swissinnovation.org Follow us >> Science-Switzerland #Science | #Education | #Research | #Innovation

Bulk defects have a critical impact on the efficiency of silicon solar cells. By identifying the specific parameters of these defects, namely their energy levels and capture cross-sections, researchers can devise strategies to mitigate their effects. It is often assumed that bulk defects are single-level defects following Shockley-Read-Hall recombination statistics. However, two-level or multi-level defects are very common. At present, it is challenging to distinguish between single-level and two-level defects and to extract the parameters of a two-level defect. In his first-ever journal paper, Sijin Wang presents a machine learning (ML)-based approach to distinguish between one- and two-level defects with an accuracy above 90%. If a defect is identified as a two-level defect, his second ML algorithm can extract its defect parameters. Despite the high wafer quality, bulk defects remain a challenge, especially for new device structures that rely on high effective lifetimes and when changes in dopants are considered. Sijin’s research is highly valuable for these investigations and for studies that correlate bulk defects with the long-term reliability of photovoltaic systems. Well done, Sijin Wang! A great collaboration between Sijin Wang, Yan Zhu, Brendan Wright, and Yoann Buratti. https://lnkd.in/g8H-DwWi Check the ACDC Photovoltaic Research Group website: https://lnkd.in/dbXG6wc

A group of materials scientists have developed a new, dynamic database of hundreds of solid-state electrolytes to which they have applied artificial intelligence techniques that are already steering research in better directions. Read more: https://lnkd.in/dWEW6uJH #Batteries #electrolytes #StatNano #NBIC #nanotechnology Tohoku University.

🤓 Exciting News! 🎉 I'm thrilled to share that my article, "Artificial Intelligence Powered Optimization of Photovoltaic Systems: Evaluating Maximum Power Point Tracking Approaches for Optimal Performance in Variable Environmental Conditions", has been published by Springer Nature in the Process Integration and Optimization for Sustainability journal! 📚 A big thank you to Springer Nature for providing this platform to share our research and for recognizing our work's significance in advancing the photovoltaic systems optimization field. 🌞 You can read the full article here: ✅ it's available in Springer Link: 🌐 https://lnkd.in/eidKptJA ✅ it's available also on ResearchGate: 🌐 https://lnkd.in/eCeHepmT DOI: 10.1007/s41660-024-00430-6 📖 I'm looking forward to continuing this journey of exploration and innovation and am already excited about the next research adventure. 🛤️ #Research #Sustainability #PhotovoltaicSystems #ArtificialIntelligence #RenewableEnergy #Optimization #SpringerNature

Breaking barriers in solar technology! ☀️ With the help of artificial intelligence, engineers are paving the way for solar panels made from a remarkable "miracle material." Explore the fascinating details behind this innovation in our #MaterialsMonday article. Check it out here 👉 https://lnkd.in/e7MQdRg5 #GreenEnergy #AI #MaterialsScience

Student and faculty research. Evolving sources of energy. Virtual and Microgrids. Innovative technology and infrastructure to extract, store and move electrons and molecules. That was all part of Energy Week at the University of Texas at Austin. I like what Dr. Michael Webber discussed in a panel covering energy progress since the 1970s oil embargo. The United States energy policy in the late 1970s was to hoard and hold oil and natural gas in the event of a major regional conflict. Today, the United States is a major provider of energy to the world as major regional conflicts occur in Europe and the Middle East. An abundance of those energy sources come from Texas. The State is Texas is driving an energy revolution for a variety of reasons to include fortunate geography and geology. I am encouraged that energy policy will evolve from five decades of "segregate and regulate" where nation states can cherry pick, stack the deck and tip the scales to shape the energy landscape to a new paradigm of "innovate and integrate" where academia, consumers, government, industry and the open market operate in real time to drive progress.

Why Materials Science is the Most Important Technology of This Decade: "Think of just about any major challenge we will face over the next decade and materials are at the center of it. To build a new clean energy future, we need more efficient solar panels, wind turbines and batteries. Manufacturers need new materials to create more advanced products. We also need to replace materials subject to supply disruptions, like rare earth elements. Traditionally, developing new materials has been a slow, painstaking process. To find the properties they’re looking for, researchers would often have to test hundreds — or even thousands — of materials one by one. That made materials research prohibitively expensive for most industries. Yet today, we’re in the midst of a materials revolution. Scientists are using powerful simulation techniques, as well as machine learning algorithms, to propel innovation forward at blazing speed and even point them toward possibilities they had never considered. Over the next decade, the rapid advancement in materials science will have a massive impact." Continue reading Greg Satell's latest guest post for Human-Centered Change & Innovation here: https://lnkd.in/gycHqwVQ #innovation #technology #materials #science

It has been shown that machine learning can be a powerful tool to enhance the capabilities of various characterisation methods for photovoltaic (PV) applications. In their detailed review paper, Yoann Buratti, Zubair Abdullah-Vetter, and Gaia Maria Javier summarise the recent progress in this area and present their interesting point of view regarding future directions. Interestingly, they emphasise that the lack of publicly available datasets poses a significant challenge for researchers to benchmark their frameworks and achieve consistent and accurate results. They also suggest that future research should focus on improving model interpretability, balancing speed and accuracy, understanding computational demands, and integrating niche applications into a unified framework. They highlight that industry involvement and interdisciplinary collaboration among experts in solar energy, data science, and engineering are vital in tailoring machine learning solutions and enhancing innovation in addressing various challenges in PV. Read more: https://lnkd.in/gfPMdWrX Check our website: https://lnkd.in/dbXG6wc

⚡ 🌏 🍀 In an era marked by the pressing need to address climate change, innovative solutions in energy management are becoming increasingly crucial. In her thesis, Lola Botman Vlaams AI Onderzoeksprogramma / Flanders AI Research Program , presents three main contributions to the field of energy forecasting, each addressing distinct aspects of individual household and plug electricity consumption. 1️⃣ The first contribution is a novel method for long-term forecasting of individual household electricity usage, demonstrating high accuracy and scalability. 2️⃣ The second introduces a global probabilistic approach for short-term forecasting of individual households, particularly robust to changing patterns. 3️⃣ The third establishes an innovative pipeline for optimizing energy efficiency in office buildings through plug scheduling. Each of these contributions underscores the pivotal role of advanced forecasting in energy management. ❔ But how exactly does energy time series forecasting intertwine with the broader goals of the energy transition? 👉 You are most welcome to join Lola Botman's defense and find out! https://lnkd.in/e5uMCxru Promoter: Bart De Moor KU Leuven

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