Systematically Verified Experimental Thermoelectric Dataset For Data-driven Approaches
Source abstract
Abstract
In the data-driven discovery of high-performance thermoelectric (TE) materials, the lack of high-quality data remains a key bottleneck. Addressing this issue, we introduce the Systematically Verified Thermoelectric (sysTEm) dataset. Leveraging the physical relationships between transport properties, we curated and validated over 8,400 experimental data points, spanning more than 1,400 unique TE materials and 70 elements. Each entry includes the composition, temperature, and up to seven key transport properties: the figure of merit (zT), Seebeck coefficient (S), electrical conductivity (σ), power factor (PF), total (κ), electronic (κe) and lattice thermal conductivities ( κl). The dataset is formatted as a data table, with little preprocessing needed for machine learning. Initial analysis shows that doped materials, defined via a compositional threshold, exhibit a larger number of high-performing zT outliers compared to undoped materials, highlighting that doping can improve TE performance. Overall, the publicly available sysTEm dataset and its accompanying code is intended to accelerate data-driven TE research and benchmarking.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Advanced Thermoelectric Materials and Devices · Machine Learning in Materials Science · Model Reduction and Neural Networks
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (71)
Show source-provided references
1. Wang X, Sheng Y, Ning J, Xi J, Xi L, Qiu D, Yang J, Ke X (2023) A critical review of machine learning techniques on thermoelectric materials. J Phys Chem Lett 14(7):1808–1822. https://doi.org/10.1021/acs.jpclett.2c03073. (Accessed 2024-05-14)10.1021/acs.jpclett.2c03073 ↗
2. Mahan GD (2016) Introduction to thermoelectrics. APL Mater 4(10):104806. https://doi.org/10.1063/1.4954055. (Accessed 2024-06-17)10.1063/1.4954055 ↗
3. Sanin-Villa D (2022) Recent developments in thermoelectric generation: a review. Sustainability 14(24):16821. https://doi.org/10.3390/su142416821. (Accessed 2025-01-14)10.3390/su142416821 ↗
4. Jouhara H, Żabnieńska-Góra A, Khordehgah N, Doraghi Q, Ahmad L, Norman L, Axcell B, Wrobel L, Dai S (2021) Thermoelectric generator (TEG) technologies and applications. Int J Thermofluids 9:100063. https://doi.org/10.1016/j.ijft.2021.100063. (Accessed 2025-06-17)10.1016/j.ijft.2021.100063 ↗
5. Antunes LM, Butler KT, Grau-Crespo R (2023) Predicting thermoelectric transport properties from composition with attention-based deep learning. Mach Learn Sci Technol 4(1):015037. https://doi.org/10.1088/2632-2153/acc4a9. (Accessed 2024-05-27)10.1088/2632-2153/acc4a9 ↗
6. Zheng XF, Liu CX, Yan YY, Wang Q (2014) A review of thermoelectrics research - recent developments and potentials for sustainable and renewable energy applications. Renew Sustain Energy Rev 32:486–503. https://doi.org/10.1016/j.rser.2013.12.053. (Accessed 2025-01-15)10.1016/j.rser.2013.12.053 ↗
7. Vining CB (2009) An inconvenient truth about thermoelectrics. Nat Mater 8(2):83–85. https://doi.org/10.1038/nmat2361. (Accessed 2024-09-19)10.1038/nmat2361 ↗
8. He J, Li K, Jia L, Zhu Y, Zhang H, Linghu J (2024) Advances in the applications of thermoelectric generators. Appl Therm Eng 236:121813. https://doi.org/10.1016/j.applthermaleng.2023.121813. (Accessed 2024-12-08)10.1016/j.applthermaleng.2023.121813 ↗
9. Goldsmid HJ (2016) Theory of thermoelectric refrigeration and generation. Introduction to Thermoelectricity. Springer, Berlin, Heidelberg, pp 9–2410.1007/978-3-662-49256-7_2 ↗
10. Feng J, Li J, Liu R (2024) Low-temperature thermoelectric materials and applications. Nano Energy 126:109651. https://doi.org/10.1016/j.nanoen.2024.109651. (Accessed 2025-06-17)10.1016/j.nanoen.2024.109651 ↗
11. He J, Tritt TM (2017) Advances in thermoelectric materials research: looking back and moving forward. Science 357(6358):9997. https://doi.org/10.1126/science.aak9997. (Accessed 2025-06-17)10.1126/science.aak9997 ↗
12. Wang T, Zhang C, Snoussi H, Zhang G (2020) Machine learning approaches for thermoelectric materials research. Adv Func Mater 30(5):1906041. https://doi.org/10.1002/adfm.201906041. (Accessed 2025-01-13)10.1002/adfm.201906041 ↗
13. Xiong Q, Han G, Wang G, Lu X, Zhou X (2024) The doping strategies for modulation of transport properties in thermoelectric materials. Adv Func Mater 34(52):2411304. https://doi.org/10.1002/adfm.202411304. (Accessed 2025-01-16)10.1002/adfm.202411304 ↗
14. Candolfi C, Aydemir U, Baitinger M, Oeschler N, Steglich F, Grin Y (2012) High temperature thermoelectric properties of the type-I clathrate Ba8Au x Si46- x. J Appl Phys 111(4):043706. https://doi.org/10.1063/1.3682585. (Accessed 2024-10-15)10.1063/1.3682585 ↗
15. Chernyavsky D, van den Brink J, Park G-H, Nielsch K, Thomas A (2022) Sustainable thermoelectric materials predicted by machine learning. Adv Theory Simulat 5(11):2200351. https://doi.org/10.1002/adts.202200351. (Accessed 2025-01-03)10.1002/adts.202200351 ↗
16. Choudhary K, Garrity KF, Tavazza F (2020) Data-driven discovery of 3D and 2D thermoelectric materials. J Phys: Condens Matter 32(47):475501. https://doi.org/10.1088/1361-648X/aba06b. (Accessed 2025-01-03)10.1088/1361-648X/aba06b ↗
17. Ricci F, Chen W, Aydemir U, Snyder GJ, Rignanese G-M, Jain A, Hautier G (2017) An ab initio electronic transport database for inorganic materials. Sci Data 4:170085. https://doi.org/10.1038/sdata.2017.8510.1038/sdata.2017.85 ↗
18. Gorai P, Stevanović V, Toberer ES (2017) Computationally guided discovery of thermoelectric materials. Nat Rev Mater 2(9):17053. https://doi.org/10.1038/natrevmats.2017.53. (Accessed 2025-06-18)10.1038/natrevmats.2017.53 ↗
19. Fiedler L, Modine NA, Schmerler S, Vogel DJ, Popoola GA, Thompson AP, Rajamanickam S, Cangi A (2023) Predicting electronic structures at any length scale with machine learning. npj Comput Mater 9(1):115. https://doi.org/10.1038/s41524-023-01070-z. (Accessed 2025-06-18)10.1038/s41524-023-01070-z ↗
20. Na GS, Jang S, Chang H (2021) Predicting thermoelectric properties from chemical formula with explicitly identifying dopant effects. npj Computat Mater 7(1):1–11. https://doi.org/10.1038/s41524-021-00564-y. (Accessed 2024-04-09)10.1038/s41524-021-00564-y ↗
21. Jain A, Ong SP, Hautier G, Chen W, Richards WD, Dacek S, Cholia S, Gunter D, Skinner D, Ceder G, Persson KA (2013) Commentary: the materials project: a materials genome approach to accelerating materials innovation. APL Mater 1(1):011002. https://doi.org/10.1063/1.4812323. (Accessed 2024-11-19)10.1063/1.4812323 ↗
22. Barua NK, Lee S, Oliynyk AO, Kleinke H (2024) Thermoelectric material performance (zT) predictions with machine learning. ACS Appl Mater Interf. https://doi.org/10.1021/acsami.4c19149. (Accessed 2025-01-0)10.1021/acsami.4c19149 ↗
23. Katsura Y, Kumagai M, Kodani T, Kaneshige M, Ando Y, Gunji S, Imai Y, Ouchi H, Tobita K, Kimura K, Tsuda K (2019) Data-driven analysis of electron relaxation times in PbTe-type thermoelectric materials. Sci Technol Adv Mater 20(1):511–520. https://doi.org/10.1080/14686996.2019.1603885. (Accessed 2025-03-13)10.1080/14686996.2019.1603885 ↗
24. Gaultois MW, Sparks TD, Borg CKH, Seshadri R, Bonificio WD, Clarke DR (2013) Data-driven review of thermoelectric materials: performance and resource considerations. Chem Mater 25(15):2911–2920. https://doi.org/10.1021/cm400893e. (Accessed 2024-08-16)10.1021/cm400893e ↗
25. Na GS, Chang H (2022) A public database of thermoelectric materials and system-identified material representation for data-driven discovery. npj Comput Mater 8(1):1–11. https://doi.org/10.1038/s41524-022-00897-2. (Accessed 2024-05-14)10.1038/s41524-022-00897-2 ↗
26. Anatychuk LI (2024) On the Discoverers of Thermoelectricity. In: Anatychuk L, Burkov A, Goldsmid J, Grin Y, Koumoto K, Narducci D, Nolas GS (eds.) 200 Years of Thermoelectricity: An Historical Journey Through the Science and Technology of Thermoelectric Materials (1821-2021), pp 3–12. Springer, Cham. https://doi.org/10.1007/978-3-031-22108-8_1 . Accessed 2026-01-1110.1007/978-3-031-22108-8_1 ↗
27. Ørsted HC (1820) Experimenta circa effectum conflictus electrici in acum magneticam10.5479/sil.425701.39088007179419 ↗
28. Seebeck TJ (1895) Magnetische Polarisation der Metalle und Erze Durch Temperatur-differenz. Engelmann
29. Anthropic: The claude 3 model family: Opus, sonnet, haiku. Technical report, Anthropic (2024). https://www-cdn.anthropic.com/de8ba9b01c9ab7cbabf5c33b80b7bbc618857627/Model_Card_Claude_3.pdf
30. OpenAI: Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023)
31. Wang Z, Xia M, He L, Chen H, Liu Y, Zhu R, Liang K, Wu X, Liu H, Malladi S, Chevalier C (2024) Charxiv: charting gaps in realistic chart understanding in multimodal llms. Adv Neural Inf Process Syst 37:113569–697
32. Turan I, Sparks T (2025) Revolutionizing Scientific Figure Decoding: Benchmarking LLM Data Extraction Performance. ChemRxiv. https://doi.org/10.26434/chemrxiv-2025-pcq8d-v2 . https://chemrxiv.org/engage/chemrxiv/article-details/68f8560daec32c65685fd48b Accessed 2026-01-1110.26434/chemrxiv-2025-pcq8d-v2 ↗
33. Sakai A, Kanno T, Takahashi K, Omote A, Adachi H, Yamada Y (2012) Thermoelectric responses in layered strontium-niobates via two ways of charge carrier control techniques. J Am Ceram Soc 95(5):1750–175510.1111/j.1551-2916.2012.05169.x ↗
34. Tsubota T, Ohtaki M, Eguchi K, Arai H (1997) Thermoelectric properties of al-doped zno as a promising oxidematerial for high-temperature thermoelectric conversion. J Mater Chem 7(1):85–9010.1039/a602506d ↗
35. Zhao L-D, Tan G, Hao S, He J, Pei Y, Chi H, Wang H, Gong S, Xu H, Dravid VP, Uher C, Snyder GJ, Wolverton C, Kanatzidis MG (2016) Ultrahigh power factor and thermoelectric performance in hole-doped single-crystal SnSe. Science 351(6269):141–144. https://doi.org/10.1126/science.aad3749. (Accessed 2024-10-15)10.1126/science.aad3749 ↗
36. Ballikaya S, Chi H, Salvador JR, Uher C (2013) Thermoelectric properties of Ag-doped Cu2Se and Cu2Te. J Mater Chem A 1(40):12478. https://doi.org/10.1039/c3ta12508d. (Accessed 2024-10-15)10.1039/c3ta12508d ↗
37. Lee S, Wilke RHT, Trolier-McKinstry S, Zhang S, Randall CA (2010) Sr x Ba 1–x Nb 2 O 6 - $$\delta $$ ferroelectric-thermoelectrics: crystal anisotropy, conduction mechanism, and power factor. Appl Phys Lett 96(3):031910. https://doi.org/10.1063/1.3291563. (Accessed 2024-10-15)10.1063/1.3291563 ↗
38. Zheng B, Sun H, Yang M, Yang B, Chen X, Zhang Y, Liu X (2024) InP3 facilitating high thermoelectric performance in Te-based composites via high pressure. J Alloy Compd 998:174991. https://doi.org/10.1016/j.jallcom.2024.174991. (Accessed 2024-08-21)10.1016/j.jallcom.2024.174991 ↗
39. Jiang F, Wu X, Zhu Y, Xia C, Han Z, Yu H, Chen C, Feng T, Mao J, Chen Y, Liu W (2024) Boosting room-temperature thermoelectric performance of Mg3Sb1.5Bi0.5 material through breaking the contradiction between carrier concentration and carrier mobility. Acta Mater 265:119636. https://doi.org/10.1016/j.actamat.2023.119636. (Accessed 2024-08-21)10.1016/j.actamat.2023.119636 ↗
40. Biswas K, He J, Blum ID, Wu C-I, Hogan TP, Seidman DN, Dravid VP, Kanatzidis MG (2012) High-performance bulk thermoelectrics with all-scale hierarchical architectures. Nature 489(7416):414–418. https://doi.org/10.1038/nature11439. (Accessed 2024-10-15)10.1038/nature11439 ↗
41. He Y, Day T, Zhang T, Liu H, Shi X, Chen L, Snyder GJ (2014) High thermoelectric performance in non-toxic earth-abundant copper sulfide. Adv Mater 26(23):3974–397810.1002/adma.201400515 ↗
42. Liu F, Piccinno F, Krichene S, Pang C, Lee K, Joshi M, Altun Y, Collier N, Eisenschlos J (2023) MatCha: enhancing visual language pretraining with math reasoning and chart derendering. In: Rogers A, Boyd-Graber J, Okazaki N (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 12756–12770. Association for Computational Linguistics, Toronto, Canada. https://doi.org/10.18653/v1/2023.acl-long.714 . https://aclanthology.org/2023.acl-long.714/ Accessed 2026-01-1110.18653/v1/2023.acl-long.714 ↗
43. Meng F, Shao W, Lu Q, Gao P, Zhang K, Qiao Y, Luo P (2024) Chartassisstant: A universal chart multimodal language model via chart-to-table pre-training and multitask instruction tuning. arXiv preprint arXiv:2401.0238410.18653/v1/2024.findings-acl.463 ↗
44. Polak MP, Morgan D (2025) Leveraging Vision Capabilities of Multimodal LLMs for Automated Data Extraction from Plots. arXiv. https://doi.org/10.48550/arXiv.2503.12326. http://arxiv.org/abs/2503.12326 Accessed 2026-01-1110.48550/arXiv.2503.12326 ↗
45. Griesemer SD, Xia Y, Wolverton C (2023) Accelerating the prediction of stable materials with machine learning. Nat Comput Sci 3(11):934–945. https://doi.org/10.1038/s43588-023-00536-w. (Accessed 2024-12-0)10.1038/s43588-023-00536-w ↗
46. Zhang Y, Ling C (2018) A strategy to apply machine learning to small datasets in materials science. npj Comput Mater 4(1):25. https://doi.org/10.1038/s41524-018-0081-z. (Accessed 2025-06-18)10.1038/s41524-018-0081-z ↗
47. Jain A (2024) Machine learning in materials research: developments over the last decade and challenges for the future. Curr Opin Solid State Mater Sci 33:101189. https://doi.org/10.1016/j.cossms.2024.101189. (Accessed 2025-03-27)10.1016/j.cossms.2024.101189 ↗
48. Damewood J, Karaguesian J, Lunger JR, Tan AR, Xie M, Peng J, Gómez-Bombarelli R (2023) Representations of materials for machine learning. Annu Rev Mater Res 53:399–426. https://doi.org/10.1146/annurev-matsci-080921-085947. (Accessed 2024-10-27)10.1146/annurev-matsci-080921-085947 ↗
49. Jiang Y, Dong J, Zhuang H-L, Yu J, Su B, Li H, Pei J, Sun F-H, Zhou M, Hu H, Li J-W, Han Z, Zhang B-P, Mori T, Li J-F (2022) Evolution of defect structures leading to high ZT in GeTe-based thermoelectric materials. Nat Commun 13(1):6087. https://doi.org/10.1038/s41467-022-33774-z. (Accessed 2024-06-17)10.1038/s41467-022-33774-z ↗
50. Qian X, Li X, Chen R, Jin H, Hou Z, Wang J-L, Wang S-F (2024) Suppression of bipolar diffusion effect on layered anisotropic PbBi4Te7 thermoelectric material via Se alloying. Ceram Int 50(19, Part A):34720–34725. https://doi.org/10.1016/j.ceramint.2024.06.279. (Accessed 2024-08-21)10.1016/j.ceramint.2024.06.279 ↗
51. Dong X, Wang H, Hua Z, Peng S, Dong L, Wang Y (2012) Thermoelectric properties of WO3-based ceramics doped with Co2O3. J Mater Sci: Mater Electron 23(6):1210–1214. https://doi.org/10.1007/s10854-011-0574-8. (Accessed 2024-10-15)10.1007/s10854-011-0574-8 ↗
52. Wang T, Xiong Y, Wang Y, Qiu P, Song Q, Zhao K, Yang J, Xiao J, Shi X, Chen L (2020) Cu3ErTe3: a new promising thermoelectric material predicated by high-throughput screening. Mater Today Phys 12:100180. https://doi.org/10.1016/j.mtphys.2020.100180. (Accessed 2024-10-15)10.1016/j.mtphys.2020.100180 ↗
53. Fu C, Bai S, Liu Y, Tang Y, Chen L, Zhao X, Zhu T (2015) Realizing high figure of merit in heavy-band p-type half-Heusler thermoelectric materials. Nat Commun 6(1):8144. https://doi.org/10.1038/ncomms9144. (Accessed 2024-10-15)10.1038/ncomms9144 ↗
54. Liu K, Dong X, Jiuxing Z (2006) The effects of La on thermoelectric properties of LaxCo4Sb12 prepared by MA-SPS. Mater Chem Phys 96(2–3):371–375. https://doi.org/10.1016/j.matchemphys.2005.07.068. (Accessed 2024-10-15)10.1016/j.matchemphys.2005.07.068 ↗
55. Pan L, Bérardan D, Dragoe N (2013) High thermoelectric properties of n-Type AgBiSe $$2$$. J Am Chem Soc 135(13):4914–4917. https://doi.org/10.1021/ja312474n. (Accessed 2024-10-15)10.1021/ja312474n ↗
56. Li S, Feng Z, Tang Z, Zhang F, Cao F, Liu X, Singh DJ, Mao J, Ren Z, Zhang Q (2020) Defect engineering for realizing p-Type AgBiSe $$2$$ with a promising thermoelectric performance. Chem Mater 32(8):3528–3536. https://doi.org/10.1021/acs.chemmater.0c00481. (Accessed 2024-10-15)10.1021/acs.chemmater.0c00481 ↗
57. Zhang D, Yang J, Jiang Q, Fu L, Xiao Y, Luo Y, Zhou Z (2016) Improvement of thermoelectric properties of Cu 3 SbSe 4 compound by In doping. Mater Design 98:150–154. https://doi.org/10.1016/j.matdes.2016.03.001. (Accessed 2024-10-15)10.1016/j.matdes.2016.03.001 ↗
58. Zhou C, Lee YK, Yu Y, Byun S, Luo Z-Z, Lee H, Ge B, Lee Y-L, Chen X, Lee JY, Cojocaru-Mirédin O, Chang H, Im J, Cho S-P, Wuttig M, Dravid VP, Kanatzidis MG, Chung I (2021) Polycrystalline SnSe with a thermoelectric figure of merit greater than the single crystal. Nat Mater 20(10):1378–1384. https://doi.org/10.1038/s41563-021-01064-6. (Accessed 2025-01-02)10.1038/s41563-021-01064-6 ↗
59. Zheng Z-H, Shi X-L, Ao D-W, Liu W-D, Li M, Kou L-Z, Chen Y-X, Li F, Wei M, Liang G-X, Fan P, Lu GQM, Chen Z-G (2023) Harvesting waste heat with flexible Bi2Te3 thermoelectric thin film. Nat Sust 6(2):180–191. https://doi.org/10.1038/s41893-022-01003-6. (Accessed 2024-07-02)10.1038/s41893-022-01003-6 ↗
60. Rohatgi A WebPlotDigitizer. https://automeris.io
61. Luo X, Huang B, Guo X, Lu W, Zheng G, Huang B, Li J, Li P, Yang Y (2021) High ZT value of pure SnSe polycrystalline materials prepared by high-energy ball milling plus hot pressing sintering. ACS Appl Mater Interfaces 13(36):43011–43021. https://doi.org/10.1021/acsami.1c09208. (Accessed 2024-10-15)10.1021/acsami.1c09208 ↗
62. Parashchuk T, Wiendlocha B, Cherniushok O, Knura R, Wojciechowski KT (2021) High thermoelectric performance of p -Type PbTe enabled by the synergy of resonance scattering and lattice softening. ACS Appl Mater Interfaces 13(41):49027–49042. https://doi.org/10.1021/acsami.1c14236. (Accessed 2024-10-15)10.1021/acsami.1c14236 ↗
63. Zhu H, Mao J, Li Y, Sun J, Wang Y, Zhu Q, Li G, Song Q, Zhou J, Fu Y, He R, Tong T, Liu Z, Ren W, You L, Wang Z, Luo J, Sotnikov A, Bao J, Nielsch K, Chen G, Singh DJ, Ren Z (2019) Discovery of TaFeSb-based half-Heuslers with high thermoelectric performance. Nat Commun 10(1):270. https://doi.org/10.1038/s41467-018-08223-5. (Accessed 2024-10-15)10.1038/s41467-018-08223-5 ↗
64. Sparks T, Tang (2025) Tankylz/Dopant_dataset: V1.0. Zenodo. https://doi.org/10.5281/zenodo.1536534510.5281/zenodo.15365345 ↗
65. Lammey R (2016) Using the Crossref Metadata API to explore publisher content. Sci Edit 3(2):109–111. https://doi.org/10.6087/kcse.75. (Accessed 2025-01-02)10.6087/kcse.75 ↗
66. Zhou Z, Huang Y, Wei B, Yang Y, Yu D, Zheng Y, He D, Zhang W, Zou M, Lan J-L, He J, Nan C-W, Lin Y-H (2023) Compositing effects for high thermoelectric performance of Cu2Se-based materials. Nat Commun 14(1):2410. https://doi.org/10.1038/s41467-023-38054-y. (Accessed 2024-06-28)10.1038/s41467-023-38054-y ↗
67. Ong SP, Richards WD, Jain A, Hautier G, Kocher M, Cholia S, Gunter D, Chevrier VL, Persson KA, Ceder G (2013) Python materials genomics (pymatgen): a robust, open-source python library for materials analysis. Comput Mater Sci 68:314–319. https://doi.org/10.1016/j.commatsci.2012.10.028. (Accessed 2024-06-11)10.1016/j.commatsci.2012.10.028 ↗
68. Dunn A, Wang Q, Ganose A, Dopp D, Jain A (2020) Benchmarking materials property prediction methods: the matbench test set and automatminer reference algorithm. npj Comput Mater 6(1):1–10. https://doi.org/10.1038/s41524-020-00406-3. (Accessed 2024-04-10)10.1038/s41524-020-00406-3 ↗
69. Rosen AS (2025) Andrew-S-Rosen/Periodic_trends. https://github.com/Andrew-S-Rosen/periodic_trends Accessed 2025-01-16
70. Hu H, Ju Y, Yu J, Wang Z, Pei J, Thong H-C, Li J-W, Cai B, Liu F, Han Z, Su B, Zhuang H-L, Jiang Y, Li H, Li Q, Zhao H, Zhang B-P, Zhu J, Li J-F (2024) Highly stabilized and efficient thermoelectric copper selenide. Nat Mater 23(4):527–534. https://doi.org/10.1038/s41563-024-01815-1. (Accessed 2025-06-19)10.1038/s41563-024-01815-1 ↗
71. Roychowdhury S, Ghosh T, Arora R, Samanta M, Xie L, Singh NK, Soni A, He J, Waghmare UV, Biswas K (2021) Enhanced atomic ordering leads to high thermoelectric performance in AgSbTe2. Science 371(6530):722–727. https://doi.org/10.1126/science.abb3517. (Accessed 2024-06-18)10.1126/science.abb3517 ↗