Superconductivity can be considered among the most exciting discoveries in material science of the 20th century. However, the hard conditions for the synthesis and the difficult characterization, make the statement of new high critical temperature (T_c) complex from the experimental viewpoint and have recently led to several hot controversies in the literature. In this panorama, theory has become a trustworthy diagnosis. Nevertheless, this comes at an extremely high computational cost. A faster alternative would be to find cheap footprints of superconductivity from the electronic structure. Some of the authors have recently shown that a correlation exists between T_c, the networking value [Nature Communications, 12, 5381 (2021)], and the molecularity index [arXiv:2403.07584v1 (2024)]. The networking value reflects the metallicity of the parent compound as a measure of its electron delocalization channels, by means of the Electron Localization Function topology (its bifurcation trees). Instead, the molecularity index quantifies the presence of H₂ molecules within the system. All in all, these two quantities characterize bonding features that are related to high T_c: high metallicity and low molecularity boost high T_c states. However, the quantification or these bonding characteristics was initially made by a visual approach, which is not scalable for high throughput screening. We have developed a new code, TcESTIME, which allows to determine the networking value for a given hydrogen-based compound. In this contribution, we present such code and the underlying periodic algorithms we have developed. As a reference, the estimation of T_c for LaH₁₀ thanks to this new code amounts to 10 CPU minutes in a computer cluster equipped with Intel Xeon 2.4 GHz processor. Given the new potential for screening, we have applied it to a larger set including ternary hydrogen based superconductors, and have proposed new fits to estimate T_c, leading to errors of ca. 33 K. We believe that this contribution settles the bases for an automatic high-throughput screening of hydrogen-based superconductors.

中文翻译：

---

TcESTIME：预测高温氢基超导体


超导性可以被认为是 20 世纪材料科学中最令人兴奋的发现之一。然而，合成的困难条件和困难的表征，从实验的角度来看，新的高临界温度 （T_c） 络合物的说法，最近在文献中引起了一些激烈的争论。在这幅全景图中，理论已成为值得信赖的诊断。然而，这需要花费极高的计算成本。更快的选择是从电子结构中找到廉价的超导足迹。一些作者最近表明，T_c、网络值 [Nature Communications， 12， 5381 （2021）] 和分子指数 [arXiv：2403.07584v1 （2024）] 之间存在相关性。网络值通过电子定位函数拓扑（其分叉树）反映了母化合物的金属丰度，作为其电子离域通道的量度。相反，分子指数量化了系统内 H₂ 分子的存在。总而言之，这两个量表征了与高 T_c 相关的键合特征：高金属丰度和低分子促进了高 T_c 状态。然而，定量或这些键合特性最初是通过视觉方法进行的，该方法无法扩展以进行高通量筛选。我们开发了一种新代码 TcESTIME，它允许确定给定氢基化合物的联网值。在这篇文章中，我们展示了这样的代码和我们开发的底层周期性算法。 作为参考，由于这个新代码，在配备 Intel Xeon 2.4 GHz 处理器的计算机集群中，LaH₁₀ 的 T_c 估计值为 10 CPU 分钟。鉴于新的筛选潜力，我们已将其应用于更大的集合，包括三元氢基超导体，并提出了新的拟合来估计 T_c，导致误差约为 33 K。我们相信，这一贡献为氢基超导体的自动高通量筛选奠定了基础。