The discovery of new materials is a lengthy and expensive process that often relies on complex empirical experiments and simulations. To accelerate the discovery of solid electrolytes for batteries, a very active area of research due to the environmental and economic issues surrounding energy storage, researchers from Microsoft and Pacific Northwest National Laboratory have developed an innovative approach that combines AI and high technology. Performance computing (HPC) in the cloud. Their method allowed them to explore a vast chemical space and predict new stable and functional materials, which they then synthesized and characterized experimentally.
Electrolytes are materials that allow ions to be transported between the electrodes of a battery. In lithium-ion batteries, used in a variety of applications from smartphones to electric vehicles, the electrolytes that transport ions between the battery's two electrodes, the anode and cathode, are liquid, potentially flammable or toxic.
Developing solid-state batteries that offer safety, performance, and durability benefits is a key goal of materials scientists. This is also the aim of the ELIAS project, led by Saft and implemented by a consortium of academic and industrial stakeholders, launched in May 2023 and supported by France 2030.
However, it is difficult to find solid electrolytes that meet all required properties such as thermal stability, electrochemical stability, ionic conductivity and compatibility with other battery components.
To address this challenge, researchers from Microsoft and Pacific Northwest National Laboratory used AI models to filter more than 32 million candidates based on stability, band gap, electrochemical stability window and ion diffusivity criteria. Li or Na. These AI models are based on graphical neural networks capable of representing and learning the properties of crystal structures, as Google DeepMind researchers recently demonstrated with GNoME.
They trained them using data from quantum computing and public databases and deployed them on cloud computing resources, reducing the time and cost of materials discovery.
Among the candidates filtered through the AI models, the authors selected the most promising ones to subject them to more precise calculations based on density functional theory (DFT) and ab initio molecular dynamics (AIMD). These calculations, performed on Microsoft's Azure Quantum Elements, which provides access to a cloud-based supercomputer suitable for research in chemistry and materials science, helped confirm the materials' stability and conductivity, as well as other properties such as hardness and Evaluate density and cost. The researchers also eliminated rare or toxic materials, identifying 23 final candidates, including five already known ones.
They then synthesized and characterized the structures and conductivities of their best candidates. Instead of using either lithium (Li) or sodium (Na) ions as conductors, they took an unconventional approach: combining these ions. The introduction of Li instead of Na significantly improved the ionic conductivity in a promising new solid electrolyte material, Na2LiYCl6, compared to the starting material Na3YCl6. Furthermore, the introduction of lithium resulted in a significant reduction in activation energy compared to the starting material, suggesting a more efficient ion diffusion process. This improvement in conductivity and reduction in activation energy is likely due to the presence of Li+ ions in ion transport as well as possible changes in the crystal structure.
This new material opens up perspectives for the design of versatile solid-state batteries. The authors emphasize that integrating AI and HPC in the cloud not only accelerates materials discovery, but also democratizes the discovery process by making computing resources easily accessible and reproducible.
Their work illustrates the potential of AI and HPC in the cloud to transform materials discovery and advance scientific and technological innovation. The authors plan to continue their research by exploring other chemical areas, refining their AI models, and validating their candidates in solid-state battery systems. They hope their approach will inspire other researchers to discover new materials with targeted properties.
Article references:
“Accelerating computational materials discovery.” with artificial intelligence and cloud high-performance computing: on a large scale from screening to experimental validation” arXiv:2401.04070v1 08/01/2024
Authors :
Chi Chen1, Dan Thien Nguyen2, Shannon J. Lee2, Nathan A. Baker1, Ajay S. Karakoti2, Linda Lauw1, Craig Owen3, Karl T. Mueller2, Brian A. Bilodeau1, Vijayakumar Murugesan2, Matthias Troyer1
1 Azure Quantum, Microsoft,
2 Directorate of Physical and Computer Sciences, Pacific Northwest National Laboratory,
3 Microsoft Surface, Microsoft.