Computer Sciencenpj Computational Materials
MD-HIT: Machine learning for material property prediction with dataset redundancy control
Q. Li, N. Fu, et al.
Discover how Qin Li, Nihang Fu, Sadman Sadeed Omee, and Jianjun Hu tackle the challenge of redundancy in materials datasets. Their innovative MD-HIT algorithm offers a fresh perspective on machine learning performance evaluations in materials science, ensuring more realistic outcomes in formation energy and band gap predictions.
Related Publications
Explore these studies to deepen your understanding
Adjacent work that informs or extends this paper's methodology and findings.
Engineering and Technology
Crystal Twins: Self-Supervised Learning for Crystalline Material Property Prediction
R. Magar, Y. Wang, et al.
Medicine and Health
Machine learning-based prediction of in-hospital death for patients with takotsubo syndrome: The InterTAK-ML model
O. D. Filippo, V. L. Cammann, et al.
Engineering and Technology
Exploiting redundancy in large materials datasets for efficient machine learning with less data
K. Li, D. Persaud, et al.
Engineering and Technology
Small dataset machine-learning approach for efficient design space exploration: engineering ZnTe-based high-entropy alloys for water splitting
S. V. Oh, S. Yoo, et al.
