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Creators/Authors contains: "Zhang, C"

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  1. ; ( , International Conference on Machine Learning)
    Free, publicly-accessible full text available January 1, 2023
  2. ; ; ( , Proceedings of the 39th International Conference on Machine Learning (ICML-2022))
    Free, publicly-accessible full text available January 1, 2023
  3. ; ; ; ; ; ; ; ; ; ( , Additive Manufacturing)
    Free, publicly-accessible full text available November 1, 2022
  4. ; ; ; ; ; ; ; ; ; ; et al ( , Marine Geology)
    Free, publicly-accessible full text available October 1, 2022
  5. ; ( , International Conference on Machine Learning)
    Accepted Manuscript Full Text Available
  6. ; ; ; ; ; ; ; ; ; ; et al ( , Classical and Quantum Gravity)
    Free, publicly-accessible full text available September 2, 2022
  7. ; ; ; ; ; ( , IEEE transactions on neural networks and learning systems)
    Full Text Available 
  8. ; ; ; ; ( , Conference on Neural Information Processing Systems)
    Accepted Manuscript Full Text Available
  9. ; ; ; ( , EMNLP (Findings))
    Accepted Manuscript Full Text Available
  10. ; ; ; ; ; ; ; ; ; ; et al ( , Physical review materials)
    We develop an open-access database that provides a large array of datasets specialized for magnetic compounds as well as magnetic clusters. Our focus is on rare-earth-free magnets. Available datasets include (i) crystallography, (ii) thermodynamic properties, such as the formation energy, and (iii) magnetic properties that are essential for magnetic-material design. Our database features a large number of stable and metastable structures discovered through our adaptive genetic algorithm (AGA) searches. Many of these AGA structures have better magnetic properties when compared to those of the existing rare-earth-free magnets and the theoretical structures in other databases. Our database places particular emphasis onmore »site-specific magnetic data, which are obtained by high-throughput first-principles calculations. Such site-resolved data are indispensable for machine-learning modeling. We illustrate how our data-intensive methods promote efficiency of the experimental discovery of new magnetic materials. Our database provides massive datasets that will facilitate an efficient computational screening, machine-learning-assisted design, and the experimental fabrication of new promising magnets.« less
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