pith. sign in

arxiv: 2512.20228 · v1 · pith:7QZ7OHNAnew · submitted 2025-12-23 · ❄️ cond-mat.supr-con · cond-mat.mtrl-sci

Composition-Based Machine Learning for Screening Superconducting Ternary Hydrides from a Curated Dataset

Kazuaki Tokuyama , Souta Miyamoto , Taichi Masuda , Katsuaki Tanabe This is my paper

Pith reviewed 2026-05-21 15:58 UTC · model grok-4.3

classification ❄️ cond-mat.supr-con cond-mat.mtrl-sci
keywords machine learningsuperconducting hydridesternary compoundshigh-pressure materialscomposition screeningtransition temperatureXGBoost
0
0 comments X

The pith

An ensemble of composition-only machine learning models identifies promising new ternary hydride superconductors like Ca-Ti-H outside the training data.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper develops an ensemble machine-learning method that predicts superconducting transition temperatures for ternary hydrides using only their chemical composition. This approach screens many possible A-B-H combinations at high pressures and flags systems like Ca-Ti-H, Li-K-H, and Na-Mg-H as high-potential candidates even though they were absent from the training set. If effective, it offers a fast way to narrow down the enormous space of possible hydrogen-rich compounds before detailed structural calculations or experiments begin. Readers should care because discovering high-temperature superconductors at lower pressures could lead to practical applications in energy or computing.

Core claim

The authors train 30 XGBoost models on about 2000 binary and ternary hydride data points to predict Tc from composition features alone. They apply the ensemble to screen A-B-H compositions at 100-300 GPa and find consistent high predictions for certain ternary systems not in the dataset. Feature analysis shows ionization energy and atomic radius as important factors.

What carries the argument

The ensemble of 30 XGBoost regression models trained on composition-based features that map elemental properties to predicted transition temperatures.

If this is right

  • High-scoring systems including Ca-Ti-H, Li-K-H, and Na-Mg-H emerge as candidates for further study even though absent from training.
  • Elemental features such as ionization energy and atomic radius drive the model's predictions of superconducting behavior.
  • The ensemble provides a statistical way to evaluate screening reliability through prediction consistency.
  • This composition-only method serves as an initial filter before more expensive structural or quantum calculations.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Combining this screening with later structure-based models could refine the candidate list efficiently.
  • The same composition-driven approach might generalize to screening other pressure-induced phases or material properties in unexplored chemical spaces.
  • If validated, it reduces the need for exhaustive high-pressure experiments on every possible ternary combination.

Load-bearing premise

Trends in superconducting transition temperature can be learned reliably from composition features alone even for ternary hydrides not seen during training.

What would settle it

Synthesizing a high-scoring candidate such as Ca-Ti-H at high pressure and measuring its actual transition temperature much lower than the ensemble prediction would challenge the screening utility.

Figures

Figures reproduced from arXiv: 2512.20228 by Katsuaki Tanabe, Kazuaki Tokuyama, Souta Miyamoto, Taichi Masuda.

Figure 1
Figure 1. Figure 1: FIG. 1. Overview of the curated dataset of hydride superconductors used for model training. (a) Distribution of [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: FIG. 2. Evaluation of predictive fidelity and uncertainty estimation [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: FIG. 3. Feature importance analysis across 30 trained XGB mod [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: presents the prediction landscape at 100, 200, and 300 GPa, providing a compositional map of the most promis￾FIG. 4. Screening results for high-Tc ternary hydrides at 100, 200, and 300 GPa. Left panels: Ternary composition maps showing the lower bound of the 95% CI of predicted Tc values across the A– B–H space. Cyan stars indicate the highest-ranked compositions at each pressure, annotated with their form… view at source ↗
read the original abstract

We present an ensemble machine-learning approach for composition-based, structure-agnostic screening of candidate superconductors among ternary hydrides under high pressure. Hydrogen-rich hydrides are known to exhibit high superconducting transition temperatures, and ternary or multinary hydrides can stabilize superconducting phases at reduced pressures through chemical compression. To systematically explore this vast compositional space, we construct an ensemble of 30 XGBoost regression models trained on a curated dataset of approximately 2000 binary and ternary hydride entries. The model ensemble is used to screen a broad set of A-B-H compositions at pressures of 100, 200, and 300 GPa, with screening outcomes evaluated statistically based on prediction consistency across ensemble members. This analysis highlights several high-scoring compositional systems, including Ca-Ti-H, Li-K-H, and Na-Mg-H, which were not explicitly included in the training dataset. In addition, feature-importance analysis indicates that elemental properties such as ionization energy and atomic radius contribute significantly to the learned composition-level trends in superconducting transition temperature. Overall, these results demonstrate the utility of ensemble-based machine learning as a primary screening tool for identifying promising regions of chemical space in superconducting hydrides.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The manuscript presents an ensemble of 30 XGBoost regression models trained on a curated dataset of ~2000 binary and ternary hydride compositions using structure-agnostic features (e.g., ionization energy, atomic radius). The ensemble screens A-B-H systems at 100, 200, and 300 GPa, identifies high-scoring candidates including Ca-Ti-H, Li-K-H, and Na-Mg-H absent from the training set, and reports feature-importance rankings to highlight composition-level trends in superconducting transition temperature.

Significance. If the central screening claims hold after proper validation, the work offers a practical, low-cost primary filter for the enormous compositional space of high-pressure ternary hydrides, where full DFT or experimental screening is prohibitive. The ensemble-consistency approach and explicit feature-importance analysis are positive elements that add statistical grounding and interpretability beyond single-model predictions.

major comments (3)
  1. [Abstract] Abstract and Results: No cross-validation scores, RMSE/MAE values, or prediction error bars are reported for either the training distribution or the screened A-B-H compositions. Without these metrics, the claim that the ensemble reliably ranks unseen systems such as Ca-Ti-H cannot be quantitatively assessed.
  2. [Methods] Methods/Results: Pressure is treated only as a discrete screening label rather than an explicit continuous input feature. This choice makes it impossible to evaluate whether the model has learned any pressure-dependent trends or simply interpolates within the marginal composition distribution of the training set.
  3. [Results] Results: The central generalization claim—that composition-only descriptors suffice to identify promising ternary systems outside the training manifold—rests on the untested assumption that structure-dependent phonon and electronic effects are already encoded in the training composition statistics. No ablation or out-of-distribution test on known high-pressure ternary hydrides is described to support this assumption.
minor comments (2)
  1. [Abstract] Abstract: The phrase 'curated dataset of approximately 2000 entries' should be replaced by the exact number and a brief statement of inclusion/exclusion criteria.
  2. [Results] The manuscript would benefit from a table summarizing the top-10 screened compositions together with their ensemble-mean Tc, standard deviation, and consistency fraction.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive feedback on our manuscript. We address each of the major comments below and have revised the manuscript accordingly to incorporate quantitative metrics, clarify the role of pressure, and provide additional validation for our generalization claims.

read point-by-point responses

  1. Referee: [Abstract] Abstract and Results: No cross-validation scores, RMSE/MAE values, or prediction error bars are reported for either the training distribution or the screened A-B-H compositions. Without these metrics, the claim that the ensemble reliably ranks unseen systems such as Ca-Ti-H cannot be quantitatively assessed.

    Authors: We agree that including performance metrics is crucial for evaluating the model's reliability. In the revised manuscript, we now report the results of 5-fold cross-validation, including RMSE and MAE values for the ensemble on the training set. For the screened compositions, we provide the ensemble-averaged Tc predictions along with the standard deviation across the 30 models to indicate prediction uncertainty. These changes enable a quantitative assessment of the rankings for candidates like Ca-Ti-H. revision: yes

  2. Referee: [Methods] Methods/Results: Pressure is treated only as a discrete screening label rather than an explicit continuous input feature. This choice makes it impossible to evaluate whether the model has learned any pressure-dependent trends or simply interpolates within the marginal composition distribution of the training set.

    Authors: We acknowledge the referee's point that pressure is not used as an explicit input feature. Our approach focuses on composition-based descriptors, with pressure defining the discrete screening conditions at 100, 200, and 300 GPa. In the revision, we have expanded the Methods section to explain this design and added a supplementary analysis where pressure is included as a feature to assess its impact on learned trends. This partial revision clarifies the model's behavior while maintaining the structure-agnostic focus. revision: partial

  3. Referee: [Results] Results: The central generalization claim—that composition-only descriptors suffice to identify promising ternary systems outside the training manifold—rests on the untested assumption that structure-dependent phonon and electronic effects are already encoded in the training composition statistics. No ablation or out-of-distribution test on known high-pressure ternary hydrides is described to support this assumption.

    Authors: We recognize that explicit tests for out-of-distribution generalization would strengthen the claims. Although the training data includes ternary hydrides, we have added an ablation experiment in the revised Results section, training models on binary hydrides only and testing predictions against known high-pressure ternary superconductors reported in the literature. We also elaborate on how the composition statistics and feature importances implicitly encode relevant trends from the training set. These additions address the concern. revision: yes

Circularity Check

0 steps flagged

No significant circularity in ML-based screening of unseen hydride compositions

full rationale

The paper trains an ensemble of XGBoost regression models on a curated dataset of ~2000 binary and ternary hydride entries using purely compositional features to predict Tc, then applies the fixed trained ensemble to rank new A-B-H compositions (e.g., Ca-Ti-H, Li-K-H, Na-Mg-H) that are explicitly stated to be absent from the training set. This follows ordinary supervised learning: parameters are fit once on the training distribution and predictions are generated for out-of-distribution inputs. No equations or steps reduce a claimed prediction to a fitted value by construction, no self-citations are invoked as load-bearing uniqueness theorems, and no ansatz or renaming of known results is presented as a derivation. The central claim—that the model can highlight promising regions outside the training data—is therefore a genuine application rather than a tautology, rendering the derivation self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 0 invented entities

The central claim rests on standard machine-learning assumptions rather than new physical axioms; the main unstated premises concern the representativeness of the training set and the sufficiency of composition-only descriptors.

free parameters (2)
  • Ensemble size
    Fixed at 30 models for statistical consistency evaluation; value chosen by authors.
  • XGBoost hyperparameters
    Not reported in abstract; implicitly fitted or defaulted during training.
axioms (2)
  • domain assumption Composition-based features capture the dominant trends in superconducting transition temperature for hydrides under pressure
    Invoked by the structure-agnostic screening design described in the abstract.
  • domain assumption The curated dataset of ~2000 binary and ternary hydrides is sufficiently representative of the broader chemical space
    Required for the model to generalize to unseen A-B-H compositions.

pith-pipeline@v0.9.0 · 5756 in / 1610 out tokens · 49189 ms · 2026-05-21T15:58:01.229706+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

  • IndisputableMonolith/Cost/FunctionalEquation washburn_uniqueness_aczel unclear
    ?
    unclear

    Relation between the paper passage and the cited Recognition theorem.

    We construct an ensemble of 30 XGBoost regression models trained on a curated dataset of approximately 2000 binary and ternary hydride entries... feature-importance analysis indicates that elemental properties such as ionization energy and atomic radius contribute significantly

  • IndisputableMonolith/Foundation/RealityFromDistinction reality_from_one_distinction unclear
    ?
    unclear

    Relation between the paper passage and the cited Recognition theorem.

    The model relies exclusively on composition-based descriptors... performance metrics should be interpreted as reflecting the model’s ability to capture statistically robust composition-level trends

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

82 extracted references · 82 canonical work pages

  1. [1]

    author author N. W. \ Ashcroft ,\ 10.1103/physrevlett.21.1748 journal journal Phys. Rev. Lett. \ volume 21 ,\ pages 1748 ( year 1968 ) NoStop

    work page doi:10.1103/physrevlett.21.1748 1968

  2. [2]

    author author N. W. \ Ashcroft ,\ 10.1103/PhysRevLett.92.187002 journal journal Phys. Rev. Lett. \ volume 92 ,\ pages 187002 ( year 2004 ) NoStop

    work page doi:10.1103/physrevlett.92.187002 2004

  3. [3]

    author author A. P. \ Drozdov , author M. I. \ Eremets , author I. A. \ Troyan , author V. Ksenofontov , \ and\ author S. I. \ Shylin ,\ 10.1038/nature14964 journal journal Nature \ volume 525 ,\ pages 73 ( year 2015 ) NoStop

    work page doi:10.1038/nature14964 2015

  4. [4]

    Somayazulu , author M

    author author M. Somayazulu , author M. Ahart , author A. K. \ Mishra , author Z. M. \ Geballe , author M. Baldini , author Y. Meng , author V. V. \ Struzhkin , \ and\ author R. J. \ Hemley ,\ 10.1103/PhysRevLett.122.027001 journal journal Phys. Rev. Lett. \ volume 122 ,\ pages 027001 ( year 2019 ) NoStop

    work page doi:10.1103/physrevlett.122.027001 2019

  5. [5]

    author author A. P. \ Drozdov , author P. P. \ Kong , author V. S. \ Minkov , author S. P. \ Besedin , author M. A. \ Kuzovnikov , author S. Mozaffari , author L. Balicas , author F. F. \ Balakirev , author D. E. \ Graf , author V. B. \ Prakapenka , author E. Greenberg , author D. A. \ Knyazev , author M. Tkacz , \ and\ author M. I. \ Eremets ,\ 10.1038/s...

    work page doi:10.1038/s41586-019-1201-8 2019

  6. [6]

    Duan , author Y

    author author D. Duan , author Y. Liu , author F. Tian , author D. Li , author X. Huang , author Z. Zhao , author H. Yu , author B. Liu , author W. Tian , \ and\ author T. Cui ,\ 10.1038/srep06968 journal journal Sci. Rep. \ volume 4 ,\ pages 6968 ( year 2014 ) NoStop

    work page doi:10.1038/srep06968 2014

  7. [7]

    Liu , author I

    author author H. Liu , author I. I. \ Naumov , author R. Hoffmann , author N. W. \ Ashcroft , \ and\ author R. J. \ Hemley ,\ 10.1073/pnas.1704505114 journal journal Proc. Natl. Acad. Sci. U. S. A. \ volume 114 ,\ pages 6990 ( year 2017 ) NoStop

    work page doi:10.1073/pnas.1704505114 2017

  8. [8]

    Zhao , author X

    author author W. Zhao , author X. Huang , author Z. Zhang , author S. Chen , author M. Du , author D. Duan , \ and\ author T. Cui ,\ 10.1093/nsr/nwad307 journal journal Natl. Sci. Rev. \ volume 11 ,\ pages nwad307 ( year 2024 ) NoStop

    work page doi:10.1093/nsr/nwad307 2024

  9. [9]

    Sun , author X

    author author Y. Sun , author X. Zhong , author H. Liu , \ and\ author Y. Ma ,\ 10.1093/nsr/nwad270 journal journal Natl. Sci. Rev. \ volume 11 ,\ pages nwad270 ( year 2024 ) NoStop

    work page doi:10.1093/nsr/nwad270 2024

  10. [10]

    Wei , author X

    author author X. Wei , author X. Hao , author A. Bergara , author E. Zurek , author X. Liang , author L. Wang , author X. Song , author P. Li , author L. Wang , author G. Gao , \ and\ author Y. Tian ,\ 10.1016/j.mtphys.2023.101086 journal journal Mater. Today Phys. \ volume 34 ,\ pages 101086 ( year 2023 ) NoStop

    work page doi:10.1016/j.mtphys.2023.101086 2023

  11. [11]

    Gao , author W

    author author K. Gao , author W. Cui , author J. Shi , author A. P. \ Durajski , author J. Hao , author S. Botti , author M. A. L. \ Marques , \ and\ author Y. Li ,\ 10.1103/physrevb.109.014501 journal journal Phys. Rev. B. \ volume 109 ( year 2024 a ),\ 10.1103/physrevb.109.014501 NoStop

    work page doi:10.1103/physrevb.109.014501 2024

  12. [12]

    Gao , author Y.-X

    author author J. Gao , author Y.-X. \ Luo , author Z.-T. \ Liu , \ and\ author Q.-J. \ Liu ,\ 10.1016/j.cjph.2024.02.025 journal journal Chin. J. Phys. \ volume 90 ,\ pages 364 ( year 2024 b ) NoStop

    work page doi:10.1016/j.cjph.2024.02.025 2024

  13. [13]

    author author I. A. \ Wrona , author R. Szczesniak , \ and\ author A. P. \ Durajski ,\ 10.1021/acs.jpcc.4c00100 journal journal J. Phys. Chem. C Nanomater. Interfaces \ volume 128 ,\ pages 9247 ( year 2024 ) NoStop

    work page doi:10.1021/acs.jpcc.4c00100 2024

  14. [14]

    Liu , author W

    author author M. Liu , author W. Cui , author J. Shi , author A. P. \ Durajski , author J. Hao , \ and\ author Y. Li ,\ 10.1039/d4tc02905d journal journal J. Mater. Chem. C Mater. Opt. Electron. Devices \ volume 12 ,\ pages 16881 ( year 2024 ) NoStop

    work page doi:10.1039/d4tc02905d 2024

  15. [15]

    \ He , author W

    author author X.-L. \ He , author W. Zhao , author Y. Xie , author A. Hermann , author R. J. \ Hemley , author H. Liu , \ and\ author Y. Ma ,\ 10.1073/pnas.2401840121 journal journal Proc. Natl. Acad. Sci. U. S. A. \ volume 121 ,\ pages e2401840121 ( year 2024 ) NoStop

    work page doi:10.1073/pnas.2401840121 2024

  16. [16]

    Huang , author C

    author author H. Huang , author C. Deng , author H. Song , author M. Du , author D. Duan , author Y. Liu , \ and\ author T. Cui ,\ 10.1038/s41598-024-61400-z journal journal Sci. Rep. \ volume 14 ,\ pages 10729 ( year 2024 ) NoStop

    work page doi:10.1038/s41598-024-61400-z 2024

  17. [17]

    author author T. F. T. \ Cerqueira , author A. Sanna , \ and\ author M. A. L. \ Marques ,\ 10.1002/adma.202307085 journal journal Adv. Mater. \ volume 36 ,\ pages e2307085 ( year 2024 ) NoStop

    work page doi:10.1002/adma.202307085 2024

  18. [18]

    author author T. F. T. \ Cerqueira , author Y.-W. \ Fang , author I. Errea , author A. Sanna , \ and\ author M. A. L. \ Marques ,\ 10.1002/adfm.202404043 journal journal Adv. Funct. Mater. \ ( year 2024 ),\ 10.1002/adfm.202404043 NoStop

    work page doi:10.1002/adfm.202404043 2024

  19. [19]

    Tian , author Y

    author author C. Tian , author Y. He , author Y.-H. \ Zhu , author J. Du , author S.-M. \ Liu , author W.-H. \ Guo , author H.-X. \ Zhong , author J. Lu , author X. Wang , \ and\ author J.-J. \ Shi ,\ 10.1002/adfm.202304919 journal journal Adv. Funct. Mater. \ volume 34 ( year 2024 ),\ 10.1002/adfm.202304919 NoStop

    work page doi:10.1002/adfm.202304919 2024

  20. [20]

    Gao , author P.-J

    author author M. Gao , author P.-J. \ Guo , author H.-C. \ Yang , author X.-W. \ Yan , author F. Ma , author Z.-Y. \ Lu , author T. Xiang , \ and\ author H.-Q. \ Lin ,\ 10.1103/physrevb.107.l180501 journal journal Phys. Rev. B. \ volume 107 ( year 2023 ),\ 10.1103/physrevb.107.l180501 NoStop

    work page doi:10.1103/physrevb.107.l180501 2023

  21. [21]

    Di Cataldo , author W

    author author S. Di Cataldo , author W. von der Linden , \ and\ author L. Boeri ,\ 10.1103/physrevb.102.014516 journal journal Phys. Rev. B. \ volume 102 ( year 2020 ),\ 10.1103/physrevb.102.014516 NoStop

    work page doi:10.1103/physrevb.102.014516 2020

  22. [22]

    Song , author X

    author author X. Song , author X. Hao , author X. Wei , author X.-L. \ He , author H. Liu , author L. Ma , author G. Liu , author H. Wang , author J. Niu , author S. Wang , author Y. Qi , author Z. Liu , author W. Hu , author B. Xu , author L. Wang , author G. Gao , \ and\ author Y. Tian ,\ 10.1021/jacs.3c14205 journal journal J. Am. Chem. Soc. \ volume 1...

    work page doi:10.1021/jacs.3c14205 2024

  23. [23]

    author author A. P. \ Durajski \ and\ author R. Szcz e \' s niak ,\ 10.1039/d1cp03896f journal journal Phys. Chem. Chem. Phys. \ volume 23 ,\ pages 25070 ( year 2021 ) NoStop

    work page doi:10.1039/d1cp03896f 2021

  24. [24]

    \ Jiang , author H.-L

    author author M.-J. \ Jiang , author H.-L. \ Tian , author Y.-L. \ Hai , author N. Lu , author P.-F. \ Tong , author S.-Y. \ Wu , author W.-J. \ Li , author C.-L. \ Yang , \ and\ author G.-H. \ Zhong ,\ 10.1021/acsaelm.1c00617 journal journal ACS Appl. Electron. Mater. \ volume 3 ,\ pages 4172 ( year 2021 ) NoStop

    work page doi:10.1021/acsaelm.1c00617 2021

  25. [25]

    \ Lv , author S.-Y

    author author H.-Y. \ Lv , author S.-Y. \ Zhang , author M.-H. \ Li , author Y.-L. \ Hai , author N. Lu , author W.-J. \ Li , \ and\ author G.-H. \ Zhong ,\ 10.1039/c9cp06008a journal journal Phys. Chem. Chem. Phys. \ volume 22 ,\ pages 1069 ( year 2020 ) NoStop

    work page doi:10.1039/c9cp06008a 2020

  26. [26]

    \ Han , author H

    author author J.-X. \ Han , author H. Zhang , \ and\ author G.-H. \ Zhong ,\ 10.1142/s012918311950061x journal journal Int. J. Mod. Phys. C. \ volume 30 ,\ pages 1950061 ( year 2019 ) NoStop

    work page doi:10.1142/s012918311950061x 2019

  27. [27]

    Hou , author B

    author author Y. Hou , author B. Li , author Y. Bai , author X. Hao , author Y. Yang , author F. Chi , author S. Liu , author J. Cheng , \ and\ author Z. Shi ,\ 10.1088/1361-648X/ac9bbc journal journal J. Phys. Condens. Matter \ volume 34 ,\ pages 505403 ( year 2022 ) NoStop

    work page doi:10.1088/1361-648x/ac9bbc 2022

  28. [28]

    Niu , author Y.-M

    author author R. Niu , author Y.-M. \ Liu , author H.-B. \ Ding , author Y.-J. \ Feng , author C.-L. \ Yang , \ and\ author G.-H. \ Zhong ,\ 10.1021/acs.jpcc.3c05265 journal journal J. Phys. Chem. C Nanomater. Interfaces \ volume 127 ,\ pages 20169 ( year 2023 ) NoStop

    work page doi:10.1021/acs.jpcc.3c05265 2023

  29. [29]

    Guan , author Y

    author author H. Guan , author Y. Sun , \ and\ author H. Liu ,\ 10.1103/physrevresearch.3.043102 journal journal Phys. Rev. Res. \ volume 3 ( year 2021 ),\ 10.1103/physrevresearch.3.043102 NoStop

    work page doi:10.1103/physrevresearch.3.043102 2021

  30. [30]

    Xie , author D

    author author H. Xie , author D. Duan , author Z. Shao , author H. Song , author Y. Wang , author X. Xiao , author D. Li , author F. Tian , author B. Liu , \ and\ author T. Cui ,\ 10.1088/1361-648X/ab09b4 journal journal J. Phys. Condens. Matter \ volume 31 ,\ pages 245404 ( year 2019 a ) NoStop

    work page doi:10.1088/1361-648x/ab09b4 2019

  31. [31]

    Shao , author D

    author author Z. Shao , author D. Duan , author Y. Ma , author H. Yu , author H. Song , author H. Xie , author D. Li , author F. Tian , author B. Liu , \ and\ author T. Cui ,\ 10.1038/s41524-019-0244-6 journal journal Npj Comput. Mater. \ volume 5 ( year 2019 ),\ 10.1038/s41524-019-0244-6 NoStop

    work page doi:10.1038/s41524-019-0244-6 2019

  32. [32]

    Zhao , author H

    author author W. Zhao , author H. Song , author M. Du , author Q. Jiang , author T. Ma , author M. Xu , author D. Duan , \ and\ author T. Cui ,\ 10.1039/d2cp05850b journal journal Phys. Chem. Chem. Phys. \ volume 25 ,\ pages 5237 ( year 2023 ) NoStop

    work page doi:10.1039/d2cp05850b 2023

  33. [33]

    Song , author Z

    author author P. Song , author Z. Hou , author P. B. \ de Castro , author K. Nakano , author Y. Takano , author R. Maezono , \ and\ author K. Hongo ,\ 10.1002/adts.202100364 journal journal Adv. Theory Simul. \ volume 5 ,\ pages 2100364 ( year 2022 ) NoStop

    work page doi:10.1002/adts.202100364 2022

  34. [34]

    \ Shi , author Y.-K

    author author L.-T. \ Shi , author Y.-K. \ Wei , author A.-K. \ Liang , author R. Turnbull , author C. Cheng , author X.-R. \ Chen , \ and\ author G.-F. \ Ji ,\ 10.1039/d1tc00634g journal journal J. Mater. Chem. C Mater. Opt. Electron. Devices \ volume 9 ,\ pages 7284 ( year 2021 ) NoStop

    work page doi:10.1039/d1tc00634g 2021

  35. [35]

    Liang , author A

    author author X. Liang , author A. Bergara , author L. Wang , author B. Wen , author Z. Zhao , author X.-F. \ Zhou , author J. He , author G. Gao , \ and\ author Y. Tian ,\ 10.1103/physrevb.99.100505 journal journal Phys. Rev. B. \ volume 99 ( year 2019 ),\ 10.1103/physrevb.99.100505 NoStop

    work page doi:10.1103/physrevb.99.100505 2019

  36. [36]

    Ma , author D

    author author Y. Ma , author D. Duan , author Z. Shao , author D. Li , author L. Wang , author H. Yu , author F. Tian , author H. Xie , author B. Liu , \ and\ author T. Cui ,\ 10.1039/c7cp05267g journal journal Phys. Chem. Chem. Phys. \ volume 19 ,\ pages 27406 ( year 2017 ) NoStop

    work page doi:10.1039/c7cp05267g 2017

  37. [37]

    Sukmas , author P

    author author W. Sukmas , author P. Tsuppayakorn-aek , author U. Pinsook , \ and\ author T. Bovornratanaraks ,\ 10.1016/j.jallcom.2020.156434 journal journal J. Alloys Compd. \ volume 849 ,\ pages 156434 ( year 2020 ) NoStop

    work page doi:10.1016/j.jallcom.2020.156434 2020

  38. [38]

    An , author D

    author author D. An , author D. Duan , author Z. Zhang , author Q. Jiang , author H. Song , \ and\ author T. Cui ,\ https://arxiv.org/abs/2303.09805 journal journal arXiv \ ( year 2023 ) ,\ note arXiv:2303.09805 [cond-mat.supr-con] NoStop

    work page arXiv 2023

  39. [39]

    Sun , author Y

    author author Y. Sun , author Y. Wang , author X. Zhong , author Y. Xie , \ and\ author H. Liu ,\ 10.1103/physrevb.106.024519 journal journal Phys. Rev. B. \ volume 106 ( year 2022 a ),\ 10.1103/physrevb.106.024519 NoStop

    work page doi:10.1103/physrevb.106.024519 2022

  40. [40]

    Du , author H

    author author M. Du , author H. Song , author Z. Zhang , author D. Duan , \ and\ author T. Cui ,\ 10.34133/2022/9784309 journal journal Research (Wash. D.C.) \ volume 2022 ,\ pages 9784309 ( year 2022 ) NoStop

    work page doi:10.34133/2022/9784309 2022

  41. [41]

    \ Hai , author H.-J

    author author Y.-L. \ Hai , author H.-J. \ Yan , \ and\ author Y.-Q. \ Cai ,\ 10.1007/s11467-022-1227-5 journal journal Front. Phys. \ volume 18 ( year 2023 ),\ 10.1007/s11467-022-1227-5 NoStop

    work page doi:10.1007/s11467-022-1227-5 2023

  42. [42]

    Sun , author X

    author author Y. Sun , author X. Li , author T. Iitaka , author H. Liu , \ and\ author Y. Xie ,\ 10.1103/physrevb.105.134501 journal journal Phys. Rev. B. \ volume 105 ( year 2022 b ),\ 10.1103/physrevb.105.134501 NoStop

    work page doi:10.1103/physrevb.105.134501 2022

  43. [43]

    Wang , author T

    author author X. Wang , author T. Bi , author K. P. \ Hilleke , author A. Lamichhane , author R. J. \ Hemley , \ and\ author E. Zurek ,\ 10.1038/s41524-022-00769-9 journal journal Npj Comput. Mater. \ volume 8 ( year 2022 ),\ 10.1038/s41524-022-00769-9 NoStop

    work page doi:10.1038/s41524-022-00769-9 2022

  44. [44]

    Hu , author Q

    author author K. Hu , author Q. Tong , author L.-M. \ Guan , author D. Wang , \ and\ author J. Yu ,\ 10.1103/physrevb.105.094108 journal journal Phys. Rev. B. \ volume 105 ( year 2022 ),\ 10.1103/physrevb.105.094108 NoStop

    work page doi:10.1103/physrevb.105.094108 2022

  45. [45]

    \ Liao , author Z

    author author Z.-W. \ Liao , author Z. Zhang , author J.-Y. \ You , author B. Gu , \ and\ author G. Su ,\ 10.1103/physrevb.105.l020510 journal journal Phys. Rev. B. \ volume 105 ( year 2022 ),\ 10.1103/physrevb.105.l020510 NoStop

    work page doi:10.1103/physrevb.105.l020510 2022

  46. [46]

    Du , author Z

    author author M. Du , author Z. Zhang , author T. Cui , \ and\ author D. Duan ,\ 10.1039/d1cp03270d journal journal Phys. Chem. Chem. Phys. \ volume 23 ,\ pages 22779 ( year 2021 ) NoStop

    work page doi:10.1039/d1cp03270d 2021

  47. [47]

    Cui , author T

    author author W. Cui , author T. Bi , author J. Shi , author Y. Li , author H. Liu , author E. Zurek , \ and\ author R. J. \ Hemley ,\ 10.1103/physrevb.101.134504 journal journal Phys. Rev. B. \ volume 101 ( year 2020 ),\ 10.1103/physrevb.101.134504 NoStop

    work page doi:10.1103/physrevb.101.134504 2020

  48. [48]

    \ Hai , author H.-L

    author author Y.-L. \ Hai , author H.-L. \ Tian , author M.-J. \ Jiang , author H.-B. \ Ding , author Y.-J. \ Feng , author G.-H. \ Zhong , author C.-L. \ Yang , author X.-J. \ Chen , \ and\ author H.-Q. \ Lin ,\ 10.1103/physrevb.105.l180508 journal journal Phys. Rev. B. \ volume 105 ( year 2022 ),\ 10.1103/physrevb.105.l180508 NoStop

    work page doi:10.1103/physrevb.105.l180508 2022

  49. [49]

    Sun , author Y

    author author Y. Sun , author Y. Tian , author B. Jiang , author X. Li , author H. Li , author T. Iitaka , author X. Zhong , \ and\ author Y. Xie ,\ 10.1103/physrevb.101.174102 journal journal Phys. Rev. B. \ volume 101 ( year 2020 ),\ 10.1103/physrevb.101.174102 NoStop

    work page doi:10.1103/physrevb.101.174102 2020

  50. [50]

    Huo , author D

    author author Z. Huo , author D. Duan , author T. Ma , author Z. Zhang , author Q. Jiang , author D. An , author H. Song , author F. Tian , \ and\ author T. Cui ,\ 10.1063/5.0151844 journal journal Matter Radiat. Extrem. \ volume 8 ( year 2023 ),\ 10.1063/5.0151844 NoStop

    work page doi:10.1063/5.0151844 2023

  51. [51]

    Wan \ and\ author R

    author author Z. Wan \ and\ author R. Zhang ,\ 10.1063/5.0127365 journal journal Appl. Phys. Lett. \ volume 121 ,\ pages 192601 ( year 2022 ) NoStop

    work page doi:10.1063/5.0127365 2022

  52. [52]

    \ Jiang , author Y.-L

    author author M.-J. \ Jiang , author Y.-L. \ Hai , author H.-L. \ Tian , author H.-B. \ Ding , author Y.-J. \ Feng , author C.-L. \ Yang , author X.-J. \ Chen , \ and\ author G.-H. \ Zhong ,\ 10.1103/physrevb.105.104511 journal journal Phys. Rev. B. \ volume 105 ( year 2022 ),\ 10.1103/physrevb.105.104511 NoStop

    work page doi:10.1103/physrevb.105.104511 2022

  53. [53]

    Li , author H

    author author S. Li , author H. Wang , author W. Sun , author C. Lu , \ and\ author F. Peng ,\ 10.1103/physrevb.105.224107 journal journal Phys. Rev. B. \ volume 105 ( year 2022 ),\ 10.1103/physrevb.105.224107 NoStop

    work page doi:10.1103/physrevb.105.224107 2022

  54. [54]

    Gao , author X.-W

    author author M. Gao , author X.-W. \ Yan , author Z.-Y. \ Lu , \ and\ author T. Xiang ,\ 10.1103/physrevb.104.l100504 journal journal Phys. Rev. B. \ volume 104 ( year 2021 ),\ 10.1103/physrevb.104.l100504 NoStop

    work page doi:10.1103/physrevb.104.l100504 2021

  55. [55]

    Lucrezi , author S

    author author R. Lucrezi , author S. Di Cataldo , author W. von der Linden , author L. Boeri , \ and\ author C. Heil ,\ 10.1038/s41524-022-00801-y journal journal Npj Comput. Mater. \ volume 8 ( year 2022 ),\ 10.1038/s41524-022-00801-y NoStop

    work page doi:10.1038/s41524-022-00801-y 2022

  56. [56]

    Di Cataldo , author C

    author author S. Di Cataldo , author C. Heil , author W. von der Linden , \ and\ author L. Boeri ,\ 10.1103/physrevb.104.l020511 journal journal Phys. Rev. B. \ volume 104 ( year 2021 ),\ 10.1103/physrevb.104.l020511 NoStop

    work page doi:10.1103/physrevb.104.l020511 2021

  57. [57]

    Liang , author A

    author author X. Liang , author A. Bergara , author X. Wei , author X. Song , author L. Wang , author R. Sun , author H. Liu , author R. J. \ Hemley , author L. Wang , author G. Gao , \ and\ author Y. Tian ,\ 10.1103/physrevb.104.134501 journal journal Phys. Rev. B. \ volume 104 ( year 2021 ),\ 10.1103/physrevb.104.134501 NoStop

    work page doi:10.1103/physrevb.104.134501 2021

  58. [58]

    Sun , author S

    author author Y. Sun , author S. Sun , author X. Zhong , \ and\ author H. Liu ,\ 10.1088/1361-648X/ac9bba journal journal J. Phys. Condens. Matter \ volume 34 ,\ pages 505404 ( year 2022 c ) NoStop

    work page doi:10.1088/1361-648x/ac9bba 2022

  59. [59]

    Jiang , author Z

    author author Q. Jiang , author Z. Zhang , author H. Song , author Y. Ma , author Y. Sun , author M. Miao , author T. Cui , \ and\ author D. Duan ,\ 10.1016/j.fmre.2022.11.010 journal journal Fundam Res \ volume 4 ,\ pages 550 ( year 2024 ) NoStop

    work page doi:10.1016/j.fmre.2022.11.010 2022

  60. [60]

    Sun , author J

    author author Y. Sun , author J. Lv , author Y. Xie , author H. Liu , \ and\ author Y. Ma ,\ 10.1103/PhysRevLett.123.097001 journal journal Phys. Rev. Lett. \ volume 123 ,\ pages 097001 ( year 2019 ) NoStop

    work page doi:10.1103/physrevlett.123.097001 2019

  61. [61]

    Chen , author X

    author author W. Chen , author X. Huang , author D. V. \ Semenok , author S. Chen , author D. Zhou , author K. Zhang , author A. R. \ Oganov , \ and\ author T. Cui ,\ 10.1038/s41467-023-38254-6 journal journal Nat. Commun. \ volume 14 ,\ pages 2660 ( year 2023 ) NoStop

    work page doi:10.1038/s41467-023-38254-6 2023

  62. [62]

    author author D. V. \ Semenok , author I. A. \ Troyan , author A. G. \ Ivanova , author A. G. \ Kvashnin , author I. A. \ Kruglov , author M. Hanfland , author A. V. \ Sadakov , author O. A. \ Sobolevskiy , author K. S. \ Pervakov , author I. S. \ Lyubutin , author K. V. \ Glazyrin , author N. Giordano , author D. N. \ Karimov , author A. L. \ Vasiliev , ...

    work page doi:10.1016/j.mattod.2021.03.025 2021

  63. [63]

    Song , author J

    author author Y. Song , author J. Bi , author Y. Nakamoto , author K. Shimizu , author H. Liu , author B. Zou , author G. Liu , author H. Wang , \ and\ author Y. Ma ,\ 10.1103/PhysRevLett.130.266001 journal journal Phys. Rev. Lett. \ volume 130 ,\ pages 266001 ( year 2023 ) NoStop

    work page doi:10.1103/physrevlett.130.266001 2023

  64. [64]

    Zhang , author T

    author author Z. Zhang , author T. Cui , author M. J. \ Hutcheon , author A. M. \ Shipley , author H. Song , author M. Du , author V. Z. \ Kresin , author D. Duan , author C. J. \ Pickard , \ and\ author Y. Yao ,\ 10.1103/PhysRevLett.128.047001 journal journal Phys. Rev. Lett. \ volume 128 ,\ pages 047001 ( year 2022 ) NoStop

    work page doi:10.1103/physrevlett.128.047001 2022

  65. [65]

    Gu , author P

    author author Q. Gu , author P. Lu , author K. Xia , author J. Sun , \ and\ author D. Xing ,\ 10.1103/physrevb.96.064517 journal journal Phys. Rev. B. \ volume 96 ( year 2017 ),\ 10.1103/physrevb.96.064517 NoStop

    work page doi:10.1103/physrevb.96.064517 2017

  66. [66]

    Stanev , author C

    author author V. Stanev , author C. Oses , author A. G. \ Kusne , author E. Rodriguez , author J. Paglione , author S. Curtarolo , \ and\ author I. Takeuchi ,\ 10.1038/s41524-018-0085-8 journal journal Npj Comput. Mater. \ volume 4 ,\ pages 1 ( year 2018 ) NoStop

    work page doi:10.1038/s41524-018-0085-8 2018

  67. [67]

    Konno , author H

    author author T. Konno , author H. Kurokawa , author F. Nabeshima , author Y. Sakishita , author R. Ogawa , author I. Hosako , \ and\ author A. Maeda ,\ 10.1103/physrevb.103.014509 journal journal Phys. Rev. B. \ volume 103 ( year 2021 ),\ 10.1103/physrevb.103.014509 NoStop

    work page doi:10.1103/physrevb.103.014509 2021

  68. [68]

    author author S. R. \ Xie , author G. R. \ Stewart , author J. J. \ Hamlin , author P. J. \ Hirschfeld , \ and\ author R. G. \ Hennig ,\ 10.1103/physrevb.100.174513 journal journal Phys. Rev. B. \ volume 100 ( year 2019 b ),\ 10.1103/physrevb.100.174513 NoStop

    work page doi:10.1103/physrevb.100.174513 2019

  69. [69]

    author author S. R. \ Xie , author Y. Quan , author A. C. \ Hire , author B. Deng , author J. M. \ DeStefano , author I. Salinas , author U. S. \ Shah , author L. Fanfarillo , author J. Lim , author J. Kim , author G. R. \ Stewart , author J. J. \ Hamlin , author P. J. \ Hirschfeld , \ and\ author R. G. \ Hennig ,\ 10.1038/s41524-021-00666-7 journal journ...

    work page doi:10.1038/s41524-021-00666-7 2022

  70. [70]

    author author C. J. \ Court \ and\ author J. M. \ Cole ,\ 10.1038/s41524-020-0287-8 journal journal Npj Comput. Mater. \ volume 6 ( year 2020 ),\ 10.1038/s41524-020-0287-8 NoStop

    work page doi:10.1038/s41524-020-0287-8 2020

  71. [71]

    author author M. J. \ Hutcheon , author A. M. \ Shipley , \ and\ author R. J. \ Needs ,\ 10.1103/physrevb.101.144505 journal journal Phys. Rev. B. \ volume 101 ( year 2020 ),\ 10.1103/physrevb.101.144505 NoStop

    work page doi:10.1103/physrevb.101.144505 2020

  72. [72]

    Jiang , author X

    author author B. Jiang , author X. Luo , author Y. Sun , author X. Zhong , author J. Lv , author Y. Xie , author Y. Ma , \ and\ author H. Liu ,\ 10.1103/physrevb.111.054505 journal journal Phys. Rev. B. \ volume 111 ( year 2025 ),\ 10.1103/physrevb.111.054505 NoStop

    work page doi:10.1103/physrevb.111.054505 2025

  73. [73]

    author author M. D. \ Group ,\ @noop title Mdr supercon datasheet readme , \ NoStop

    work page

  74. [74]

    author author P. B. \ Allen \ and\ author R. C. \ Dynes ,\ 10.1103/PhysRevB.12.905 journal journal Phys. Rev. B \ volume 12 ,\ pages 905 ( year 1975 ) NoStop

    work page doi:10.1103/physrevb.12.905 1975

  75. [75]

    Eliashberg ,\ @noop journal journal Sov

    author author G. Eliashberg ,\ @noop journal journal Sov. Phys. JETP \ volume 11 ,\ pages 696 ( year 1960 ) NoStop

    work page 1960

  76. [76]

    author author S. P. \ Ong , author W. D. \ Richards , author A. Jain , author G. Hautier , author M. Kocher , author S. Cholia , author D. Gunter , author V. L. \ Chevrier , author K. A. \ Persson , \ and\ author G. Ceder ,\ 10.1016/j.commatsci.2012.10.028 journal journal Comput. Mater. Sci. \ volume 68 ,\ pages 314 ( year 2013 ) NoStop

    work page doi:10.1016/j.commatsci.2012.10.028 2012

  77. [77]

    Mentel ,\ @noop title mendeleev -- a python resource for properties of chemical elements, ions and isotopes, ver

    author author . Mentel ,\ @noop title mendeleev -- a python resource for properties of chemical elements, ions and isotopes, ver. 1.0.0 , \ howpublished https://github.com/lmmentel/mendeleev ( year 2014 ) NoStop

    work page 2014

  78. [78]

    Chen and C

    author author T. Chen \ and\ author C. Guestrin ,\ in\ 10.1145/2939672.2939785 booktitle Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining ,\ series and number KDD '16 \ ( publisher Association for Computing Machinery ,\ address New York, NY, USA ,\ year 2016 )\ p.\ pages 785–794 NoStop

    work page doi:10.1145/2939672.2939785 2016

  79. [79]

    Wang , author C

    author author X. Wang , author C. Zhang , author Z. Wang , author H. Liu , author J. Lv , author H. Wang , author W. E , \ and\ author Y. Ma ,\ https://arxiv.org/abs/2502.16558 journal journal arXiv \ ( year 2025 ) ,\ note arXiv:2502.16558 [cond-mat.supr-con] NoStop

    work page arXiv 2025

  80. [80]

    Belli, E

    author author F. Belli , author E. Zurek , \ and\ author I. Errea ,\ https://arxiv.org/abs/2501.14420 journal journal arXiv \ ( year 2025 ) ,\ note arXiv:2501.14420 [cond-mat.supr-con] NoStop

    work page arXiv 2025

Showing first 80 references.