Lawrence Livermore National Laboratory (LLNL) has been selected to lead a project that will receive $4.1 million in funding from the U.S. Department of Energy Advanced Research Projects Agency-Energy (ARPA-E) as part of the Quantum Computing for Computational Chemistry (QC3) program. QC3 seeks to develop and apply quantum algorithms to accelerate simulations of chemistry and materials science to advance commercial energy applications ranging from superconducting power lines, advanced batteries, engineered rare-earth magnets and breakthrough catalytic systems. LLNL will develop quantum and machine learning-accelerated software tools and apply them to discovering ultra-strong, lightweight magnets that are crucial for electronic motors, generators and high-performance information technology. The core innovation is a hybrid classical-quantum algorithm that can accurately predict material performance. The result could have a huge impact on how America uses energy. “Anytime you want to convert energy between electrical forms and mechanical forms, like in wind turbines, electric vehicles or hydro power, you need to have a magnet that mediates that process,” said LLNL scientist and project lead Ilon Joseph. “If we can do much better calculations of magnetic materials science, we can find new kinds of magnetic materials that can power our energy technology.” New magnet materials could circumvent China’s critical material supply chain and offer improvements in terms of weight, strength, robustness and resistance to corrosion. Even slight enhancements could also decrease the resources needed to power artificial intelligence (AI) and information technology (IT). Much of the energy consumption in AI and IT comes from writing and erasing information stored in memory. For MRAM-based chips, which store data using magnetic states, reading and writing requires flipping the magnetization of tiny thin-film magnets. Because AI and IT are predicted to dominate U.S. electricity consumption by the end of the decade, magnetic memory that takes less energy to flip — even