论文标题
用BCS启发的筛选，密度功能理论和深度学习设计高-TC超导体
Designing High-Tc Superconductors with BCS-inspired Screening, Density Functional Theory and Deep-learning
论文作者
论文摘要
我们开发了一个多步骤工作流程，以发现常规超导体的发现，从Bardeen Cooper Schrieffer启发的1736材料的预筛选开始，该材料具有较高的DEBYE温度和状态的电子密度。接下来，我们对其中1058个进行电子 - 音波耦合计算，以建立BCS超导属性的大型且系统的数据库。使用McMillan-Allen-Dynes公式，我们识别105个具有过渡温度的动态稳定材料，TC> 5K。此外，我们分析了数据集和单个材料的趋势，包括MON，VC，VTE，VTE，KB6，RU3NBC，RU3NBC，V3PT，V3PT，SCN，SCN，LAN2，RUO2，RUO2和TAC。我们证明，深度学习（DL）模型可以比直接的第一原理计算更快地预测超导体性能。值得注意的是，我们发现，通过预测EliAshberg作为中间数量的功能，我们可以改善模型性能与TC的直接DL预测。我们将经过训练的模型应用于晶体学开放数据库和预屏幕前候选者，以进行进一步的DFT计算。
We develop a multi-step workflow for the discovery of conventional superconductors, starting with a Bardeen Cooper Schrieffer inspired pre-screening of 1736 materials with high Debye temperature and electronic density of states. Next, we perform electron-phonon coupling calculations for 1058 of them to establish a large and systematic database of BCS superconducting properties. Using the McMillan-Allen-Dynes formula, we identify 105 dynamically stable materials with transition temperatures, Tc>5 K. Additionally, we analyze trends in our dataset and individual materials including MoN, VC, VTe, KB6, Ru3NbC, V3Pt, ScN, LaN2, RuO2, and TaC. We demonstrate that deep-learning(DL) models can predict superconductor properties faster than direct first principles computations. Notably, we find that by predicting the Eliashberg function as an intermediate quantity, we can improve model performance versus a direct DL prediction of Tc. We apply the trained models on the crystallographic open database and pre-screen candidates for further DFT calculations.
