arxiv: 2604.16433 · v1 · submitted 2026-04-06 · ❄️ cond-mat.supr-con

Recognition: no theorem link

Signature of Unconventional Superconductivity in the High Temperature Normal State Resistivity

Yuchen Wu , Yiwen Liu , Wanyue Lin , Zohar Nussinov , Sheng Ran

Authors on Pith no claims yet

Pith reviewed 2026-05-10 18:52 UTC · model grok-4.3

classification ❄️ cond-mat.supr-con

keywords unconventional superconductivitynormal-state resistivitymachine learningFe-based superconductorspairing mechanismscattering channelshigh-temperature transportiron pnictides

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The pith

Machine learning finds that resistivity data from 150-300 K predicts superconductivity in iron-based materials.

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

The paper applies machine learning to resistivity measurements and shows a strong correlation with the presence of superconductivity in the Fe-based family. The key predictive information comes from a broad temperature window of 150 to 300 K, well above the superconducting transition temperature Tc where most prior studies have searched. This suggests that the normal state at these higher temperatures already encodes essential details about the pairing mechanism. The signatures are not confined to one scattering process but appear across multiple channels in the resistivity. A sympathetic reader would care because it widens the search space for the origin of unconventional superconductivity and implies that simpler, higher-temperature measurements could help identify new superconducting compounds.

Core claim

Using machine learning on normal-state resistivity data, we demonstrate a strong correlation between normal-state resistivity and superconductivity in Fe-based superconductors. Remarkably, the predictive information resides in the wide window of 150-300 K, far above Tc of this family. We further show that the signatures of superconductivity are distributed across multiple scattering channels, which requires further theoretical investigation.

What carries the argument

Machine learning model that extracts predictive features from temperature-dependent resistivity curves specifically in the 150-300 K interval to forecast superconducting behavior.

If this is right

The pairing mechanism in these unconventional superconductors imprints detectable signatures on resistivity at temperatures several times higher than Tc.
Multiple distinct scattering channels in the normal state each carry information relevant to superconductivity rather than a single dominant process.
Theoretical models of the pairing mechanism must incorporate or explain the observed high-temperature transport properties in addition to low-temperature behavior.
Resistivity measurements in the 150-300 K range could serve as a practical screening tool for identifying new candidate superconductors without requiring millikelvin temperatures.

Where Pith is reading between the lines

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

The same machine-learning approach might reveal analogous high-temperature resistivity signatures in other families of unconventional superconductors such as cuprates or heavy-fermion compounds.
If the extracted features can be mapped to specific microscopic quantities like scattering rates or susceptibilities, they could constrain or guide the development of microscopic theories.
Controlled experiments that vary doping, pressure, or disorder while tracking both the resistivity window and Tc could test whether the correlation strength scales with the superconducting dome.

Load-bearing premise

The machine learning correlations reflect genuine physical links to the superconducting mechanism rather than statistical artifacts or overfitting within the available collection of Fe-based samples.

What would settle it

Train the model on one subset of Fe-based resistivity curves and test its predictions on an independent set of previously unexamined Fe-based compounds by measuring their actual superconducting transition temperatures.

Figures

Figures reproduced from arXiv: 2604.16433 by Sheng Ran, Wanyue Lin, Yiwen Liu, Yuchen Wu, Zohar Nussinov.

**Figure 1.** Figure 1: Schematics of the machine learning model. a. The interactions that govern high-temperature resistivity scattering are the same interactions that may drive superconducting pairing at low temperatures. b. Our machine-learning framework takes polynomial coefficients from high-temperature resistivity fits as input and predicts the superconducting critical temperature Tc. By applying an activation function, the… view at source ↗

**Figure 2.** Figure 2: The performance of the two models on the testing dataset. In each sub-figure, the bigger plot on the top is the predicted Tc plotted against the true Tc of testing data; the labels are the mean and median of predicted Tc (over 100 random splits of training and testing datasets) for each unique value of true Tc. The smaller plot on the bottom left is the distribution of the model’s accuracy score over 100 r… view at source ↗

**Figure 3.** Figure 3: (a) - (c) The changes in AUROC score with varying range of temperature used by the model. (a) fix [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

read the original abstract

Unconventional superconductivity remains one of the central unsolved problems in quantum materials, and revealing its connection to the normal state is widely believed to be key to uncovering the pairing mechanism. Previous efforts have largely focused on the temperature range immediately above the superconducting transition, where specific scattering channels-such as strange-metal transport-have been identified as sharing a possible microscopic origin with superconductivity. Here, using machine learning, we demonstrate a strong correlation between normal-state resistivity and superconductivity in Fe-based superconductors. Remarkably, the predictive information reside in a wide window of 150-300 K, far above $T_c$ of this family. We further show that the signatures of superconductivity are distributed across multiple scattering channels, which requires further theoretical investigation.

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.

Desk Editor's Note private letter to a colleague

ML on resistivity data picks up a correlation with superconductivity in the 150-300 K window for Fe-based compounds, but the small sample family leaves room for non-physical patterns.

read the letter

The main thing to know is that this paper uses machine learning on normal-state resistivity to show predictive power for superconductivity in Fe-based materials, with the useful information sitting in the 150-300 K range rather than right above Tc, and spread across multiple scattering channels. That temperature window is the clearest new angle here. It moves the discussion away from the usual focus on low-temperature anomalies and treats the link as an empirical fact that needs theory to explain. The work does a reasonable job framing the result as a call for more investigation instead of overclaiming a mechanism. It also ties back to prior ideas about normal-state transport without forcing a single scattering picture. That part is straightforward and could be useful for people collecting transport data on these compounds. The soft spots sit in the data handling. Fe-based superconductors are a narrow family with lots of shared chemistry, doping ranges, and measurement habits, so any model trained on them can easily latch onto those common traits instead of something tied to pairing. The description gives no numbers on how many distinct compounds were used, how the data were split for training and testing, what cross-validation was done, or whether simple controls like shuffling labels were tried. Without those, the correlation could be real or it could be an artifact of the limited set. The paper would interest experimental groups working on Fe-based or related unconventional superconductors who want to check if their own resistivity curves carry similar signals. Theorists looking for new constraints on scattering might also scan it. A reader already deep in ML applications to transport data would see it as one more data point rather than a shift in approach. I would bring it to a reading group to talk through the temperature window and what the multiple channels imply, but I would not cite it until the validation steps are clearer. It deserves peer review so the methods can be checked and the dataset details filled in.

Referee Report

3 major / 2 minor

Summary. The paper claims that machine learning applied to normal-state resistivity curves of Fe-based superconductors reveals a strong correlation with superconductivity, with the key predictive information residing in the 150-300 K temperature window far above Tc; it further asserts that this signature is distributed across multiple scattering channels.

Significance. If the ML correlation proves robust, the result would suggest that high-temperature normal-state transport encodes information about the pairing mechanism in iron-based superconductors, potentially broadening the search for microscopic links beyond the immediate vicinity of Tc and motivating new theoretical work on multi-channel scattering.

major comments (3)

[Abstract] Abstract: the assertion of a 'strong correlation' is presented without any quantitative performance metrics (e.g., R², classification accuracy, or cross-validated error), dataset cardinality, or compound list, making it impossible to judge whether the result exceeds what would be expected from a small, chemically related sample set.
[Methods] Methods (or equivalent section describing the ML pipeline): no information is supplied on model architecture, training/validation splits, cross-validation strategy, regularization, or ablation tests that would rule out overfitting to shared non-universal traits such as doping ranges or measurement protocols common to the Fe-based family.
[Results] Results section on temperature-window analysis: the claim that predictive power is localized to 150-300 K requires explicit comparison (e.g., feature-importance or window-ablation curves) against other temperature intervals; without this, the specificity of the window remains untested.

minor comments (2)

[Abstract] Abstract: grammatical error ('the predictive information reside' should read 'resides').
The phrase 'multiple scattering channels' is used without defining how these channels were extracted or distinguished from the resistivity data.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback, which highlights important aspects for improving the clarity and rigor of our work. We will revise the manuscript to incorporate quantitative metrics, expanded methodological details, and additional analyses supporting the temperature-window claims. Our responses to each major comment are provided below.

read point-by-point responses

Referee: [Abstract] Abstract: the assertion of a 'strong correlation' is presented without any quantitative performance metrics (e.g., R², classification accuracy, or cross-validated error), dataset cardinality, or compound list, making it impossible to judge whether the result exceeds what would be expected from a small, chemically related sample set.

Authors: We agree that the abstract would benefit from explicit quantitative details to allow proper evaluation of the correlation strength. In the revised manuscript, we will add performance metrics including cross-validated classification accuracy and error rates, the total number of resistivity curves in the dataset, and a summary of the compounds included. These additions will clarify that the reported correlation is evaluated against appropriate baselines and is not limited to a trivially small or homogeneous set. revision: yes
Referee: [Methods] Methods (or equivalent section describing the ML pipeline): no information is supplied on model architecture, training/validation splits, cross-validation strategy, regularization, or ablation tests that would rule out overfitting to shared non-universal traits such as doping ranges or measurement protocols common to the Fe-based family.

Authors: We acknowledge the need for a transparent description of the machine-learning pipeline to address potential concerns about overfitting. The revised Methods section will specify the model architecture, the procedure for training/validation splits, the cross-validation strategy employed, regularization methods used, and results from ablation tests that explicitly check robustness against family-specific features such as common doping ranges or experimental protocols. revision: yes
Referee: [Results] Results section on temperature-window analysis: the claim that predictive power is localized to 150-300 K requires explicit comparison (e.g., feature-importance or window-ablation curves) against other temperature intervals; without this, the specificity of the window remains untested.

Authors: We will strengthen the temperature-window analysis by including feature-importance rankings across the full temperature range and ablation experiments that retrain the model on alternative windows (e.g., below 150 K or above 300 K). These additions will provide direct quantitative evidence that the 150-300 K interval carries the dominant predictive signal relative to other intervals, thereby confirming the specificity of the reported finding. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical ML correlation on resistivity data

full rationale

The paper presents a machine-learning analysis that identifies correlations between normal-state resistivity (150-300 K window) and superconductivity across Fe-based compounds. This is framed as an empirical finding rather than a mathematical derivation. No load-bearing step reduces by construction to fitted inputs, self-definition, or a self-citation chain; the result is a statistical correlation extracted from data, with no equations or uniqueness theorems invoked that loop back to the inputs. The approach is self-contained against external benchmarks as a data-driven observation.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No explicit free parameters, axioms, or invented entities are stated in the abstract. The approach relies on machine learning applied to experimental resistivity data, but without full methods it is not possible to enumerate implicit hyperparameters or assumptions.

pith-pipeline@v0.9.0 · 5427 in / 1207 out tokens · 47683 ms · 2026-05-10T18:52:03.927547+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

44 extracted references · 1 canonical work pages · 1 internal anchor

[1]

Keimer, S

B. Keimer, S. A. Kivelson, M. R. Norman, S. Uchida, and J. Zaanen. From quantum matter to high-temperature superconductivity in copper oxides.Nature, 518:179–186, 2015

2015
[2]

Silva, S

E. Silva, S. Sarti, R. Fastampa, and M. Giura. Ex- cess conductivity of overdoped Bi 2Sr2CaCu2O8+x crys- tals well abovet c.Physical Review B, 64:144508, 2001

2001
[3]

Grbi´ c, Miroslav Poˇ zek, Guichuan Yu, Takao Sasagawa, Martin Greven, and Neven Bariˇ si´ c

Damjan Pelc, Marija Vuckovic, Mihael S. Grbi´ c, Miroslav Poˇ zek, Guichuan Yu, Takao Sasagawa, Martin Greven, and Neven Bariˇ si´ c. Emergence of superconductivity in the cuprates via a universal percolation process.Nature Communications, 9, 2018

2018
[4]

Percolative nature of the direct-current para- conductivity in cuprate superconductors.npj Quantum Materials, 3, 2018

Petar Popˇ cevi´ c, Damjan Pelc, Yang Tang, Kristi- jan Velebit, Zachary Anderson, Vikram Nagarajan, Guichuan Yu, Miroslav Poˇ zek, Neven Bariˇ si´ c, and Mar- tin Greven. Percolative nature of the direct-current para- conductivity in cuprate superconductors.npj Quantum Materials, 3, 2018

2018
[5]

Yu, D.-D

G. Yu, D.-D. Xia, D. Pelc, R.-H. He, N.-H. Kaneko, T. Sasagawa, Y. Li, X. Zhao, N. Bariˇ si´ c, and et al. Universal precursor of superconductivity in the cuprates. Physical Review B, 99:214502, 2019

2019
[6]

Scaling of the strange-metal scattering in unconventional superconductors.Nature, 602:431–436, 2022

Jie Yuan, Qihong Chen, Kun Jiang, Zhongpei Feng, Ze- feng Lin, Heshan Yu, Ge He, Jinsong Zhang, Xingyu Jiang, Xu Zhang, Yujun Shi, Yanmin Zhang, Mingyang Qin, Zhi Gang Cheng, Nobumichi Tamura, Yi-feng Yang, Tao Xiang, Jiangping Hu, Ichiro Takeuchi, Kui Jin, and Zhongxian Zhao. Scaling of the strange-metal scattering in unconventional superconductors.Natur...

2022
[7]

Greene, Pampa R

Richard L. Greene, Pampa R. Mandal, Nicholas R. Poni- atowski, and Tarapada Sarkar. The strange metal state of the electron-doped cuprates.Annual Review of Con- densed Matter Physics, 11:213–229, 2020

2020
[8]

Chandra M. Varma. Colloquium: Linear in temperature resistivity and associated mysteries including high tem- perature superconductivity.Reviews of Modern Physics, 92:031001, 2020

2020
[9]

Interplay between superconductivity and the strange-metal state in fese.Nature Physics, 19:365–371, 2023

Xingyu Jiang, Mingyang Qin, Xinjin Wei, Li Xu, Jiezun Ke, Haipeng Zhu, Ruozhou Zhang, Zhanyi Zhao, Qimei Liang, Zhongxu Wei, Zefeng Lin, Zhongpei Feng, Fu- cong Chen, Peiyu Xiong, Jie Yuan, Beiyi Zhu, Yangmu Li, Chuangying Xi, Zhaosheng Wang, Ming Yang, Jun- feng Wang, Tao Xiang, Jiangping Hu, Kun Jiang, and Zhongxian Zhao. Interplay between superconducti...

2023
[10]

Bulk superconductivity up to 96 K in pressurized nickelate single crystals

Feiyu Li, Zhenfang Xing, Di Peng, Jie Dou, Ning Guo, 6 Liang Ma, Yulin Zhang, Jun Luo, Jie Yang, Jian Zhang, et al. Ambient pressure growth of bilayer nickelate sin- gle crystals with superconductivity over 90 k under high pressure.arXiv preprint arXiv:2501.14584, 2025

work page internal anchor Pith review Pith/arXiv arXiv 2025
[11]

N Ni, M. E. Tillman, J.-Q. Yan, A. Kracher, S. T. Han- nahs, S. L. Bud’ko, and P. C. Canfield. Effects of co substitution on thermodynamic and transport properties and anisotropic hc2 in ba(fe 1−xcox)2as2 single crystals. Physical Review B, 78:214515, 2008

2008
[12]

A Thaler, H Hodovanets, M. S. Torikachvili, S Ran, A Kracher, W Straszheim, J. Q. Yan, E Mun, and P. C. Canfield. Physical and magnetic properties of ba(fe1−xmnx)2as2 single crystals.Physical Review B, 84:144528, 2011

2011
[13]

N Ni, A Thaler, J. Q. Yan, A Kracher, E Colombier, S. L. Bud’ko, P. C. Canfield, and S. T. Hannahs. Tempera- ture versus doping phase diagrams for ba(fe 1−xtmx)2as2 (tm=ni, cu, cu/co) single crystals.Physical Review B, 82:024519, 2010

2010
[14]

N Ni, A Thaler, A Kracher, J. Q. Yan, S. L. Bud’ko, and P. C. Canfield. Phase diagrams of ba(fe1−xmx)2as2 single crystals (m=rh, pd).Physical Review B, 80:024511, 2009

2009
[15]

A Thaler, N Ni, A Kracher, J. Q. Yan, S. L. Bud’ko, and P. C. Canfield. Physical and magnetic properties of ba(fe 1−xrux)2as2 single crystals.Physical Review B, 82:014534, 2010

2010
[16]

Evolution from non-fermi- to fermi-liquid transport via isovalent doping in bafe2(as1−xpx)2 superconductors.Physical Review B, 81:184519, 2010

S Kasahara, T Shibauchi, K Hashimoto, K Ikada, S Tone- gawa, R Okazaki, H Shishido, H Ikeda, H Takeya, K Hi- rata, T Terashima, and Y Matsuda. Evolution from non-fermi- to fermi-liquid transport via isovalent doping in bafe2(as1−xpx)2 superconductors.Physical Review B, 81:184519, 2010

2010
[17]

H Luo, Z Wang, H Yang, P Cheng, X Zhu, and H.- H. Wen. Growth and characterization of a 1−xkxfe2as2 (a=ba, sr) single crystals with x=0–0.4.Superconductor Science and Technology, 21:125014, 2008

2008
[18]

M. S. Torikachvili, S. L. Bud’ko, N Ni, and P. C. Canfield. Effect of pressure on the structural phase transition and superconductivity in (ba1−xkx)fe2as2 (x=0 and 0.45) and srfe2as2 single crystals.Physical Review B, 78:104527, 2008

2008
[19]

Control of structural transition in fese1−xtex thin films by changing substrate materials.Scientific Reports, 7:46653, 2017

Yoshinori Imai, Yuichi Sawada, Fuyuki Nabeshima, Daisuke Asami, Masataka Kawai, and Atsutaka Maeda. Control of structural transition in fese1−xtex thin films by changing substrate materials.Scientific Reports, 7:46653, 2017

2017
[20]

Su- perconductivity in pressurized trilayer la 4ni3o10−δ single crystals.Nature, 631:531–536, 2024

Yinghao Zhu, Di Peng, Enkang Zhang, Bingying Pan, Xu Chen, Lixing Chen, Huifen Ren, Feiyang Liu, Yiqing Hao, Nana Li, Fujun Lan, Jiyuan Han, Junjie Wang, Donghan Jia, Hongliang Wo, Yiqing Gu, Yimeng Gu, Li Ji, Wenbin Wang, Huiyang Gou, Yao Shen, Tianping Ying, Xiaolong Chen, Wenge Yang, Huibo Cao, Changlin Zheng, Qiaoshi Zeng, Jian-gang Guo, and Jun Zhao....

2024
[21]

Saha, Tyler Drye, and Johnpierre Paglione

Kevin Kirshenbaum, Shanta R. Saha, Tyler Drye, and Johnpierre Paglione. Superconductivity and magnetism in platinum-substituted srfe2as2 single crystals.Physical Review B, 82(14):144518, 2010

2010
[22]

S. Ran, S. L. Bud’ko, W. E. Straszheim, J. Soh, M. G. Kim, A. Kreyssig, A. I. Goldman, and P. C. Canfield. Control of magnetic, nonmagnetic, and superconducting states in annealed ca(fe 1−xcox)2as2.Physical Review B, 85:224528, 2012

2012
[23]

S. Ran, S. L. Bud’ko, W. E. Straszheim, and P. C. Can- field. Combined effects of transition metal (ni and rh) substitution and annealing/quenching on the physical properties of CaFe 2As2.Physical Review B, 90:054501, 2014

2014
[24]

M. Xu, J. Schmidt, M. A. Tanatar, R. Prozorov, S. L. Bud’ko, and P. C. Canfield. Superconductivity and magnetic and transport properties of single-crystalline cak(fe1−xcrx)4as4.Physical Review B, 107:134511, 2023

2023
[25]

Review of single crystal synthesis of 11 iron-based super- conductors.Materials, 16(14):4895, 2023

Qiang Hou, Longfei Sun, Yue Sun, and Zhixiang Shi. Review of single crystal synthesis of 11 iron-based super- conductors.Materials, 16(14):4895, 2023

2023
[26]

S. K. Kim, M. S. Torikachvili, E. Colombier, A. Thaler, S. L. Bud’ko, and P. C. Canfield. Combined effects of pressure and ru substitution on bafe2as2.Physical Review B, 84:134525, 2011

2011
[27]

S. K. Kim, E. Colombier, N. Ni, S. L. Bud’ko, and P. C. Canfield. Evolution of the electronic transport properties of v 6o11 and v 7o13 under pressure.Physical Review B, 87:115140, 2013

2013
[28]

S. K. Kim, M. S. Torikachvili, S. L. Bud’ko, and P. C. Canfield. Search for pressure-induced quantum criticality in ybfe2zn20.Physical Review B, 88:045116, 2013

2013
[29]

Influence of interstitial fe to the phase diagram of fe 1+yte1−xsex single crystals.Scientific Re- ports, 6:32290, 2016

Yue Sun, Tatsuhiro Yamada, Sunseng Pyon, and Tsuyoshi Tamegai. Influence of interstitial fe to the phase diagram of fe 1+yte1−xsex single crystals.Scientific Re- ports, 6:32290, 2016

2016
[30]

Comprehensive phase diagram of two- dimensional space charge doped bi2sr2cacu2o8+x.Nature Communications, 8(1):2060, 2017

Edoardo Sterpetti, Johan Biscaras, Andreas Erb, and Abhay Shukla. Comprehensive phase diagram of two- dimensional space charge doped bi2sr2cacu2o8+x.Nature Communications, 8(1):2060, 2017

2060
[31]

Cava, and Liling Sun

Shu Cai, Jinyu Zhao, Ni Ni, Jing Guo, Run Yang, Pengyu Wang, Jinyu Han, Sijin Long, Yazhou Zhou, Qi Wu, Xi- anggang Qiu, Tao Xiang, Robert J. Cava, and Liling Sun. The breakdown of both strange metal and superconduct- ing states at a pressure-induced quantum critical point in iron-pnictide superconductors.Nature Communications, 14(1), May 2023

2023
[32]

R. E. A. Goodall and Alpha A. Lee. Predicting super- conductor critical temperatures with machine learning. npj Computational Materials, 6:18, 2020

2020
[33]

C. Oses, E. Gossett, and et al. Data-mining approaches for superconductivity.Journal of Chemical Physics, 150(4):041101, 2018

2018
[34]

Zhang and et al

H. Zhang and et al. Machine learning study of supercon- ductivity.npj Computational Materials, 5:25, 2019

2019
[35]

Wang and et al

W. Wang and et al. Supervised learning approaches to predicting superconductivity.Computational Materials Science, 153:241–248, 2018

2018
[36]

Jiang and et al

W. Jiang and et al. Neural-network approaches to super- conductivity prediction.Journal of Physical Chemistry, 125(15):7860–7868, 2021

2021
[37]

Zhang and et al

P. Zhang and et al. Machine learning prediction of su- perconducting critical temperatures from chemical com- position.npj Computational Materials, 4:25, 2018

2018
[38]

Chen and et al

W. Chen and et al. Machine learning prediction of new superconductors.npj Computational Materials, 7:52, 2021

2021
[39]

Sanfilippo and et al

P. Sanfilippo and et al. Machine-learning-assisted dis- covery of superconductors.npj Computational Materials, 8:15, 2022

2022
[40]

Sanna and et al

A. Sanna and et al. Machine learning prediction of hydride superconductivity.Physical Review B, 102(5):054507, 2020. 7

2020
[41]

M. J. Hutcheon, A. M. Shipley, and R. J. Needs. Predic- tions of novel superconducting hydrides using machine learning and structure searching.Physical Review B, 101(14):144506, 2020

2020
[42]

Zhang and et al

Y. Zhang and et al. Unsupervised learning in supercon- ductivity.Journal of Chemical Physics, 152(5):054101, 2020

2020
[43]

Zhang and et al

Z. Zhang and et al. Prediction of superconducting ma- terials using machine learning.Computational Materials Science, 193:110360, 2021

2021
[44]

Structure-driven prediction of magnetic order in uranium compounds.Physical Review Materials, 8(11):114405, 2024

Christopher Broyles, William Charles, and Sheng Ran. Structure-driven prediction of magnetic order in uranium compounds.Physical Review Materials, 8(11):114405, 2024

2024