Physicsnpj Computational Materials
Machine learning and evolutionary prediction of superhard B-C-N compounds
W. Chen, J. N. Schmidt, et al.
This groundbreaking research, conducted by Wei-Chih Chen, Joanna N. Schmidt, Da Yan, Yogesh K. Vohra, and Cheng-Chien Chen, showcases the power of random forests models to predict superhard materials from chemical formulas. The study reveals that a 1:1 B-N ratio can lead to several dynamically stable superhard compounds, pushing the boundaries of materials science through innovative machine learning techniques.
Related Publications
Explore these studies to deepen your understanding
Adjacent work that informs or extends this paper's methodology and findings.
Medicine and Health
Prediction of mortality risk and duration of hospitalization of COVID-19 patients with chronic comorbidities based on machine learning algorithms
P. Amiri, M. Montazeri, et al.
Engineering and Technology
Fast and accurate machine learning prediction of phonon scattering rates and lattice thermal conductivity
Z. Guo, P. R. Chowdhury, et al.
Medicine and Health
Determining clinical course of diffuse large B-cell lymphoma using targeted transcriptome and machine learning algorithms
M. Albitar, H. Zhang, et al.
Engineering and Technology
Machine learning-enabled forward prediction and inverse design of 4D-printed active plates
X. Sun, L. Yue, et al.
