Researchers developed a machine learning model using CatBoost to predict disturbances in drone formations with an R² of 83.3%, significantly improving from the previous baseline of 54%.

More information: Janghoon Ock et al, Multimodal language and graph learning of adsorption configuration in catalysis, Nature Machine Intelligence (2024). DOI: 10.1038/s42256-024-00930-7.

Microsoft researchers believe they have found a way to use machine learning to push those limits for small molecules (arXiv 2025, DOI: 10.48550/arXiv.2506.14665).

References 1. Bialonczyk U, Debowska M, Dai L, et al. Balancing accuracy and cost in machine learning models for detecting medial vascular calcification in chronic kidney disease: a pilot study.

A research team led by Professor Takuya Yamamoto and Assistant Professor Ryusaku Matsumoto (Department of Life Science ...