Screening novel cathode materials from the Energy-GNoME database using MACE machine learning force field and DFT
Published:Nov 27, 2025 14:36
•1 min read
•ArXiv
Analysis
This article describes a research study that utilizes machine learning and Density Functional Theory (DFT) to identify new cathode materials. The methodology involves screening the Energy-GNoME database, suggesting a computational approach to materials discovery. The use of MACE (Machine-learning Assisted Computational Exploration) force field indicates an effort to improve the efficiency and accuracy of the simulations. The focus on cathode materials suggests a potential application in battery technology.
Key Takeaways
- •The research focuses on identifying new cathode materials.
- •It utilizes machine learning (MACE) and DFT for material screening.
- •The study leverages the Energy-GNoME database.
- •The work aims to improve the efficiency and accuracy of materials discovery through computational methods.
Reference
“”