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Machine learning accelerated search for superconductors in B-C-N based compounds and R3Ni2O7-type nickelates

Xiaoying Li, Wenqian Tu, Run Lv, Li'e Liu, Dingfu Shao, Yuping Sun, Wenjian Lu

DOI 10.1103/wn98-9yc7 · Physical Review B

T1

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Abstract

Superconductor research has traditionally depended on experiments and theoretical approaches. However, the rapid advancement of data-driven methods and machine learning (ML) has opened avenues for accelerating superconductor discovery. Here, we integrate ML with density functional theory calculations to efficiently screen conventional B-C-N based superconductors and identify potential high-TC candidates among R3Ni2O7-type bilayer nickelates. We identify 13 binary and ternary B-C-N based superconductors with TC≥10 K, including 3 with TC≥25 K, two structural forms of B2CN (TC=47.6 and 43.7 K) and TiNbN2 (TC=25.3 K). These B-C-N based compounds share a common feature of strong σ bonds, which is key to achieving relatively high TC. Moreover, we propose Tb3Ni2O7 (TC=61.6 K) and Ac3Ni2O7 (TC=70.3 K) as potential high-TC nickelate superconductors under high pressure. Their electronic structures closely resemble those of La3Ni2O7, especially in the hole-type band dominated by Ni 3dz2 orbital character. We also analyze feature importance in the ML results for both conventional and high-TC superconductors. These results advance the search for new superconductors and enhance the fundamental understanding of superconducting mechanisms.

Source-reported materials — not catalogue approval

FormulaReported Tc (K)Pressure (GPa)Type
B2CN

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47.6Pressure not reportedunknown
B2CN

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43.7Pressure not reportedunknown
TiNbN2

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25.3Pressure not reportedunknown
Tb3Ni2O7

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61.630 GPaunknown
Ac3Ni2O7

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70.330 GPaunknown
La3Ni2O7

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80Pressure not reportedunknown
MgB2

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39Pressure not reportedunknown
V2AlN

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Source-occurrence policy only; no material identity or catalogue acceptance is inferred from the formula.

15.9Pressure not reportedunknown
Ta2V3.1Si0.9

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Source-occurrence policy only; no material identity or catalogue acceptance is inferred from the formula.

7.5Pressure not reportedunknown
Nb3P2Se2

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Source-occurrence policy only; no material identity or catalogue acceptance is inferred from the formula.

6.8Pressure not reportedunknown
La2IRu2

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Source-occurrence policy only; no material identity or catalogue acceptance is inferred from the formula.

4.8Pressure not reportedunknown
W3Al2C

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Source-occurrence policy only; no material identity or catalogue acceptance is inferred from the formula.

7.6Pressure not reportedunknown
ZrP1.27Se0.73

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7.1Pressure not reportedunknown
Nb2PSe2

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Source-occurrence policy only; no material identity or catalogue acceptance is inferred from the formula.

6.9Pressure not reportedunknown
InNbS2

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Source-occurrence policy only; no material identity or catalogue acceptance is inferred from the formula.

6Pressure not reportedunknown
TlBi2

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Source-occurrence policy only; no material identity or catalogue acceptance is inferred from the formula.

6.2Pressure not reportedunknown
NbReSi

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Source-occurrence policy only; no material identity or catalogue acceptance is inferred from the formula.

6.5Pressure not reportedunknown
Mo3ReRuC

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Source-occurrence policy only; no material identity or catalogue acceptance is inferred from the formula.

7.7Pressure not reportedunknown
ThRu3Si2

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Source-occurrence policy only; no material identity or catalogue acceptance is inferred from the formula.

3.8Pressure not reportedunknown

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