Latest news with #AIRIInstitute
Yahoo
14-07-2025
- Automotive
- Yahoo
PhD student develops brain-like technology that could solve dangerous issue with electric vehicles: 'Orders of magnitude faster'
New research has potentially found a solution for some of consumers' biggest concerns about electric vehicle adoption: reducing fire hazards and extending battery life. In a new study published in npj Computational Materials, researchers, including a Ph.D. student from Skoltech and AIRI Institute, demonstrated how neural networks can significantly accelerate the discovery of solid electrolyte materials. This advancement could address one of the biggest hurdles in EV battery design: creating batteries that are safer, longer-lasting, and capable of holding more charge while reducing fire risks. Data shows that traditional internal combustion engine vehicles already have a much higher fire hazard risk than EVs. Solid-state batteries are a highly anticipated successor to traditional lithium-ion EV batteries. Instead of using flammable liquid electrolytes, solid-state batteries utilize ceramic or other solid materials to move lithium ions between electrodes. These materials offer greater stability, enabling faster charging, longer ranges, and improved safety. However, most known solid electrolytes do not yet meet all the technical requirements for commercial EVs. Researchers are now using artificial intelligence neural networks to predict new materials with high ionic mobility at speeds far surpassing traditional trial-and-error methods. "We demonstrated that graph neural networks can identify new solid-state battery materials with high ionic mobility and do it orders of magnitude faster than traditional quantum chemistry methods," explained Artem Dembitskiy, the lead author of the study and a Ph.D. student at Skoltech. "Machine learning lets us screen tens of thousands of materials in a fraction of the time." This innovative approach has helped the team identify two promising new protective coatings that could stabilize next-generation batteries and prevent dangerous short circuits. The potential of solid-state batteries is significant: Some automakers estimate they could offer up to 50% more range compared to today's EVs, along with reduced fire risk and longer battery life. This translates into lower long-term maintenance costs and fewer battery replacements. This research builds on previous AI-assisted breakthroughs in EV battery technology, fueling solid-state battery innovations that could enable EVs to last a decade longer than current battery technology. Pairing these high-efficiency EVs with home solar can drive savings even further. By charging at home using solar energy, drivers can lower their electricity bills and easily compare rates on sites like EnergySage. If you were going to purchase an EV, which of these factors would be most important to you? Cost Battery range Power and speed The way it looks Click your choice to see results and speak your mind. Considering an EV as your next car? You could save over $1,500 a year on gas and maintenance as well as receive Inflation Reduction Act tax breaks and credits, up to $7,500 through Sept. 30, while reducing planet-warming pollution and avoiding high gas prices. While these solid-state batteries are not yet ready for mass-market EVs, AI tools like these are helping us get there faster. This breakthrough could enable automakers to reduce their reliance on nonrenewable fuels and create a cleaner, more affordable future for drivers everywhere. Join our free newsletter for weekly updates on the latest innovations improving our lives and shaping our future, and don't miss this cool list of easy ways to help yourself while helping the planet.
Yahoo
14-07-2025
- Automotive
- Yahoo
PhD student develops brain-like technology that could solve dangerous issue with electric vehicles: 'Orders of magnitude faster'
New research has potentially found a solution for some of consumers' biggest concerns about electric vehicle adoption: reducing fire hazards and extending battery life. In a new study published in npj Computational Materials, researchers, including a Ph.D. student from Skoltech and AIRI Institute, demonstrated how neural networks can significantly accelerate the discovery of solid electrolyte materials. This advancement could address one of the biggest hurdles in EV battery design: creating batteries that are safer, longer-lasting, and capable of holding more charge while reducing fire risks. Data shows that traditional internal combustion engine vehicles already have a much higher fire hazard risk than EVs. Solid-state batteries are a highly anticipated successor to traditional lithium-ion EV batteries. Instead of using flammable liquid electrolytes, solid-state batteries utilize ceramic or other solid materials to move lithium ions between electrodes. These materials offer greater stability, enabling faster charging, longer ranges, and improved safety. However, most known solid electrolytes do not yet meet all the technical requirements for commercial EVs. Researchers are now using artificial intelligence neural networks to predict new materials with high ionic mobility at speeds far surpassing traditional trial-and-error methods. "We demonstrated that graph neural networks can identify new solid-state battery materials with high ionic mobility and do it orders of magnitude faster than traditional quantum chemistry methods," explained Artem Dembitskiy, the lead author of the study and a Ph.D. student at Skoltech. "Machine learning lets us screen tens of thousands of materials in a fraction of the time." This innovative approach has helped the team identify two promising new protective coatings that could stabilize next-generation batteries and prevent dangerous short circuits. The potential of solid-state batteries is significant: Some automakers estimate they could offer up to 50% more range compared to today's EVs, along with reduced fire risk and longer battery life. This translates into lower long-term maintenance costs and fewer battery replacements. This research builds on previous AI-assisted breakthroughs in EV battery technology, fueling solid-state battery innovations that could enable EVs to last a decade longer than current battery technology. Pairing these high-efficiency EVs with home solar can drive savings even further. By charging at home using solar energy, drivers can lower their electricity bills and easily compare rates on sites like EnergySage. If you were going to purchase an EV, which of these factors would be most important to you? Cost Battery range Power and speed The way it looks Click your choice to see results and speak your mind. Considering an EV as your next car? You could save over $1,500 a year on gas and maintenance as well as receive Inflation Reduction Act tax breaks and credits, up to $7,500 through Sept. 30, while reducing planet-warming pollution and avoiding high gas prices. While these solid-state batteries are not yet ready for mass-market EVs, AI tools like these are helping us get there faster. This breakthrough could enable automakers to reduce their reliance on nonrenewable fuels and create a cleaner, more affordable future for drivers everywhere. Join our free newsletter for weekly updates on the latest innovations improving our lives and shaping our future, and don't miss this cool list of easy ways to help yourself while helping the planet.
Scoop
07-06-2025
- Automotive
- Scoop
Neural Networks Speed Up Search For Solid-State Battery Materials For Safer Electric Cars With Extended Range
Press Release – Skoltech So far solid-state batteries have not been adopted by carmakers, but EV developers are looking to capitalize on the technology before competitors. The new type of energy storage could improve fire safety and boost EV range by up to 50%. Researchers from Skoltech and AIRI Institute have shown how machine learning can speed up the development of new materials for solid-state lithium-ion batteries. These are an emerging energy storage technology, which could theoretically replace conventional Li-ion batteries in electric vehicles and portable electronics, reducing fire hazards and extending battery life. In the Russian Science Foundation-backed study, published in npj Computational Materials, neural networks proved capable of identifying promising materials for the key component of these advanced batteries — the solid electrolyte — as well as for its protective coatings. Like its conventional counterpart, the solid-state battery incorporates an electrolyte, through which ions carrying the electric charge travel from one electrode to another. While in a conventional battery the electrolyte is a liquid solution, its solid-state analogue, as the name suggests, relies on solid electrolytes, such as ceramics, to conduct lithium ions. So far solid-state batteries have not been adopted by carmakers, but EV developers are looking to capitalize on the technology before competitors. The new type of energy storage could improve fire safety and boost EV range by up to 50%. The problem is that none of the currently available solid electrolytes meets all the technical requirements. So the search for new materials continues. 'We demonstrated that graph neural networks can identify new solid-state battery materials with high ionic mobility and do it orders of magnitude faster than traditional quantum chemistry methods. This could speed up the development of new battery materials, as we showed by predicting a number of protective coatings for solid-state battery electrolytes,' commented the lead author of the study, Artem Dembitskiy, a PhD student of Skoltech's Materials Science and Engineering program, a research intern at Skoltech Energy, and a junior research scientist at AIRI Institute. Study co-author, Assistant Professor Dmitry Aksyonov from Skoltech Energy explained the role of protective coatings: 'The metallic lithium of the anode is a strong reducing agent, so almost all existing electrolytes undergo reduction in contact with it. The cathode material is a strong oxidizing agent. When oxidized or reduced, electrolytes lose their structural integrity, which can degrade performance or even cause a short circuit. You can avoid this by introducing two protective coatings that are stable in contact with the anode and the electrolyte and the cathode and the electrolyte.' Machine learning algorithms make it possible to accelerate the calculation of ionic conductivity, a key property both for electrolytes and for protective coatings. It is among the most computationally challenging characteristics calculated in screening the candidate materials. For protective coatings, the list of properties that are checked at various stages of material selection includes thermodynamic stability, electronic conductivity, electrochemical stability, compatibility with electrode and electrolyte materials, ionic conductivity, and so on. Such screening happens in stages and gradually narrows down the list of perhaps tens of thousands of initial options to just a few materials. The authors of the study used their machine learning-accelerated approach to search for coating materials to protect one of the most promising solid-state battery electrolytes: Li10GeP2S12. The search identified multiple promising coating materials, among them the compounds Li3AlF6 and Li2ZnCl4.
Scoop
07-06-2025
- Science
- Scoop
Neural Networks Speed Up Search For Solid-State Battery Materials For Safer Electric Cars With Extended Range
Researchers from Skoltech and AIRI Institute have shown how machine learning can speed up the development of new materials for solid-state lithium-ion batteries. These are an emerging energy storage technology, which could theoretically replace conventional Li-ion batteries in electric vehicles and portable electronics, reducing fire hazards and extending battery life. In the Russian Science Foundation-backed study, published in npj Computational Materials, neural networks proved capable of identifying promising materials for the key component of these advanced batteries — the solid electrolyte — as well as for its protective coatings. Like its conventional counterpart, the solid-state battery incorporates an electrolyte, through which ions carrying the electric charge travel from one electrode to another. While in a conventional battery the electrolyte is a liquid solution, its solid-state analogue, as the name suggests, relies on solid electrolytes, such as ceramics, to conduct lithium ions. So far solid-state batteries have not been adopted by carmakers, but EV developers are looking to capitalize on the technology before competitors. The new type of energy storage could improve fire safety and boost EV range by up to 50%. The problem is that none of the currently available solid electrolytes meets all the technical requirements. So the search for new materials continues. 'We demonstrated that graph neural networks can identify new solid-state battery materials with high ionic mobility and do it orders of magnitude faster than traditional quantum chemistry methods. This could speed up the development of new battery materials, as we showed by predicting a number of protective coatings for solid-state battery electrolytes,' commented the lead author of the study, Artem Dembitskiy, a PhD student of Skoltech's Materials Science and Engineering program, a research intern at Skoltech Energy, and a junior research scientist at AIRI Institute. Study co-author, Assistant Professor Dmitry Aksyonov from Skoltech Energy explained the role of protective coatings: 'The metallic lithium of the anode is a strong reducing agent, so almost all existing electrolytes undergo reduction in contact with it. The cathode material is a strong oxidizing agent. When oxidized or reduced, electrolytes lose their structural integrity, which can degrade performance or even cause a short circuit. You can avoid this by introducing two protective coatings that are stable in contact with the anode and the electrolyte and the cathode and the electrolyte.' Machine learning algorithms make it possible to accelerate the calculation of ionic conductivity, a key property both for electrolytes and for protective coatings. It is among the most computationally challenging characteristics calculated in screening the candidate materials. For protective coatings, the list of properties that are checked at various stages of material selection includes thermodynamic stability, electronic conductivity, electrochemical stability, compatibility with electrode and electrolyte materials, ionic conductivity, and so on. Such screening happens in stages and gradually narrows down the list of perhaps tens of thousands of initial options to just a few materials. The authors of the study used their machine learning-accelerated approach to search for coating materials to protect one of the most promising solid-state battery electrolytes: Li10GeP2S12. The search identified multiple promising coating materials, among them the compounds Li3AlF6 and Li2ZnCl4.
