An ab initio insight into the structural, physical, thermodynamic and optoelectronic properties of superconducting Heusler-like LiGa2Rh

Mondal, Prianka; Khanom, Shamima; Shahed, Naafis Ahnaf; Hossain, Md. Kamal; Ahmed, Farid

doi:10.1016/j.physc.2022.1354142

Cited by 9 publications

(1 citation statement)

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“…5 , we also display a sample clustering result from the “others” version of conditional SuperDiff conditioned on various compounds. As seen in the plot, we identified two new clusters: , which was generated by conditioning SuperDiff on 33 , and , which was generated by conditioning SuperDiff on 34 . Those and other predicted families will be discussed in more detail below.…”

Section: Resultsmentioning

confidence: 99%

Diffusion models for conditional generation of hypothetical new families of superconductors

Yuan,

Dordevic

2024

Sci Rep

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Effective computational search holds great potential for aiding the discovery of high-temperature superconductors (HTSs), especially given the lack of systematic methods for their discovery. Recent progress has been made in this area with machine learning, especially with deep generative models, which have been able to outperform traditional manual searches at predicting new superconductors within existing superconductor families but have yet to be able to generate completely new families of superconductors. We address this limitation by implementing conditioning—a method to control the generation process—for our generative model and develop SuperDiff, a denoising diffusion probabilistic model with iterative latent variable refinement conditioning for HTS discovery—the first deep generative model for superconductor discovery with conditioning on reference compounds. With SuperDiff, by being able to control the generation process, we were able to computationally generate completely new families of hypothetical superconductors for the very first time. Given that SuperDiff also has relatively fast training and inference times, it has the potential to be a very powerful tool for accelerating the discovery of new superconductors and enhancing our understanding of them.

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