Engineering and Technologynpj Computational Materials
Bayesian Linear Regression for Accurate and Efficient Atomistic Machine Learning Models
C. V. D. Oord
Discover how C. van der Oord is revolutionizing material property predictions with a groundbreaking Bayesian approach to linear regression that enhances both accuracy and efficiency in atomistic machine learning models, specifically the Accurate and Efficient (ACE) model.
Related Publications
Explore these studies to deepen your understanding
Adjacent work that informs or extends this paper's methodology and findings.
Engineering and Technology
Accurate and efficient molecular dynamics based on machine learning and non von Neumann architecture
P. Mo, C. Li, et al.
Computer Science
The Goldilocks paradigm: comparing classical machine learning, large language models, and few-shot learning for drug discovery applications
S. H. Snyder, P. A. Vignaux, et al.
Psychology
Building machine learning prediction models for well-being using predictors from the exposome and genome in a population cohort
D. H. M. Pelt, P. C. Habets, et al.
Engineering and Technology
An accurate and transferable machine learning interatomic potential for nickel
X. Gong, Z. Li, et al.
