Chemistrynpj Computational Materials
Machine learning guided high-throughput search of non-oxide garnets
J. Schmidt, H. Wang, et al.
This innovative research, conducted by Jonathan Schmidt, Hai-Chen Wang, Georg Schmidt, and Miguel A. L. Marques, explores the chemical space for new garnet compositions using cutting-edge graph neural networks and high-throughput calculations. Discover the potential of over 600 newly identified ternary garnets related to sulfides, nitrides, and halides, along with their intriguing electronic structures.
Related Publications
Explore these studies to deepen your understanding
Adjacent work that informs or extends this paper's methodology and findings.
Engineering and Technology
Machine learning-guided discovery of ionic polymer electrolytes for lithium metal batteries
K. Li, J. Wang, et al.
Physics
Machine learning using structural representations for discovery of high temperature superconductors
L. Novakovic, A. Salamat, et al.
Physics
Machine learning of high dimensional data on a noisy quantum processor
E. Peters, J. Caldeira, et al.
Medicine and Health
Combining machine learning with high-content imaging to infer ciprofloxacin susceptibility in isolates of *Salmonella Typhimurium*
T. Tran, S. Sridhar, et al.
