{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T09:37:10Z","timestamp":1775209030249,"version":"3.50.1"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"8055","license":[{"start":{"date-parts":[[2025,1,16]],"date-time":"2025-01-16T00:00:00Z","timestamp":1736985600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,1,16]],"date-time":"2025-01-16T00:00:00Z","timestamp":1736985600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Nature"],"published-print":{"date-parts":[[2025,3,20]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>The design of functional materials with desired properties is essential in driving technological advances in areas such as energy storage, catalysis and carbon capture<jats:sup>1\u20133<\/jats:sup>. Generative models accelerate\u00a0materials design by directly generating new materials given desired property constraints, but current methods have a low success rate in proposing stable crystals or can satisfy only\u00a0a limited set of property constraints<jats:sup>4\u201311<\/jats:sup>. Here we present MatterGen, a model that generates stable, diverse inorganic materials across the periodic table and can further be fine-tuned to steer the generation towards a broad range of property constraints. Compared with previous generative models<jats:sup>4,12<\/jats:sup>, structures produced by MatterGen are more than twice as likely to be new and stable, and more than ten times closer to the local energy minimum. After fine-tuning, MatterGen successfully generates stable, new materials with desired chemistry, symmetry and mechanical, electronic and magnetic properties. As a proof of concept, we synthesize one of the generated structures and measure its property value to be within 20% of our target. We believe that the quality of generated materials and the breadth of abilities of MatterGen represent an important advancement towards creating a foundational generative model for materials design.<\/jats:p>","DOI":"10.1038\/s41586-025-08628-5","type":"journal-article","created":{"date-parts":[[2025,1,16]],"date-time":"2025-01-16T10:02:59Z","timestamp":1737021779000},"page":"624-632","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":296,"title":["A generative model for inorganic materials design"],"prefix":"10.1038","volume":"639","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6334-2679","authenticated-orcid":false,"given":"Claudio","family":"Zeni","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1454-188X","authenticated-orcid":false,"given":"Robert","family":"Pinsler","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1626-5065","authenticated-orcid":false,"given":"Daniel","family":"Z\u00fcgner","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7360-3078","authenticated-orcid":false,"given":"Andrew","family":"Fowler","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7777-8871","authenticated-orcid":false,"given":"Matthew","family":"Horton","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7480-6312","authenticated-orcid":false,"given":"Xiang","family":"Fu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0009-9833-7093","authenticated-orcid":false,"given":"Zilong","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Aliaksandra","family":"Shysheya","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0341-7712","authenticated-orcid":false,"given":"Jonathan","family":"Crabb\u00e9","sequence":"additional","affiliation":[]},{"given":"Shoko","family":"Ueda","sequence":"additional","affiliation":[]},{"given":"Roberto","family":"Sordillo","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7971-5222","authenticated-orcid":false,"given":"Lixin","family":"Sun","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0412-1312","authenticated-orcid":false,"given":"Jake","family":"Smith","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5870-4914","authenticated-orcid":false,"given":"Bichlien","family":"Nguyen","sequence":"additional","affiliation":[]},{"given":"Hannes","family":"Schulz","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0009-6484-0352","authenticated-orcid":false,"given":"Sarah","family":"Lewis","sequence":"additional","affiliation":[]},{"given":"Chin-Wei","family":"Huang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2239-8526","authenticated-orcid":false,"given":"Ziheng","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Yichi","family":"Zhou","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4531-093X","authenticated-orcid":false,"given":"Han","family":"Yang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4382-200X","authenticated-orcid":false,"given":"Hongxia","family":"Hao","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4428-2452","authenticated-orcid":false,"given":"Jielan","family":"Li","sequence":"additional","affiliation":[]},{"given":"Chunlei","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Wenjie","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8092-6553","authenticated-orcid":false,"given":"Ryota","family":"Tomioka","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0987-4666","authenticated-orcid":false,"given":"Tian","family":"Xie","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,16]]},"reference":[{"key":"8628_CR1","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1038\/s41578-019-0165-5","volume":"5","author":"Q Zhao","year":"2020","unstructured":"Zhao, Q., Stalin, S., Zhao, C.-Z. & Archer, L. A. Designing solid-state electrolytes for safe, energy-dense batteries. Nat. Rev. Mater. 5, 229\u2013252 (2020).","journal-title":"Nat. Rev. Mater."},{"key":"8628_CR2","doi-asserted-by":"publisher","first-page":"792","DOI":"10.1038\/s41578-019-0152-x","volume":"4","author":"Z-J Zhao","year":"2019","unstructured":"Zhao, Z.-J. et al. Theory-guided design of catalytic materials using scaling relationships and reactivity descriptors. Nat. Rev. Mater. 4, 792\u2013804 (2019).","journal-title":"Nat. Rev. Mater."},{"key":"8628_CR3","doi-asserted-by":"publisher","first-page":"724","DOI":"10.1021\/cr2003272","volume":"112","author":"K Sumida","year":"2012","unstructured":"Sumida, K. et al. Carbon dioxide capture in metal-organic frameworks. Chem. Rev. 112, 724\u2013781 (2012).","journal-title":"Chem. Rev."},{"key":"8628_CR4","unstructured":"Xie, T., Fu, X., Ganea, O.-E., Barzilay, R. & Jaakkola, T.S. Crystal diffusion variational autoencoder for periodic material generation. In Proc. International Conference on Learning Representations (ICLR, 2022)."},{"key":"8628_CR5","doi-asserted-by":"publisher","DOI":"10.1038\/s41524-023-00987-9","volume":"9","author":"Y Zhao","year":"2023","unstructured":"Zhao, Y. et al. Physics guided deep learning for generative design of crystal materials with symmetry constraints. npj Comput. Mater. 9, 38 (2023).","journal-title":"npj Comput. Mater."},{"key":"8628_CR6","doi-asserted-by":"publisher","first-page":"1412","DOI":"10.1021\/acscentsci.0c00426","volume":"6","author":"S Kim","year":"2020","unstructured":"Kim, S., Noh, J., Gu, G. H., Aspuru-Guzik, A. & Jung, Y. Generative adversarial networks for crystal structure prediction. ACS Cent. Sci. 6, 1412\u20131420 (2020).","journal-title":"ACS Cent. Sci."},{"key":"8628_CR7","doi-asserted-by":"crossref","unstructured":"Zheng, S. et al. Predicting equilibrium distributions for molecular systems with deep learning. Nat. Mach. Intell. 6, 558\u2013567 (2024).","DOI":"10.1038\/s42256-024-00837-3"},{"key":"8628_CR8","unstructured":"Yang, M. et al. Scalable diffusion for materials generation. In Proc. International Conference on Learning Representations (ICLR, 2024)."},{"key":"8628_CR9","doi-asserted-by":"publisher","first-page":"1370","DOI":"10.1016\/j.matt.2019.08.017","volume":"1","author":"J Noh","year":"2019","unstructured":"Noh, J. et al. Inverse design of solid-state materials via a continuous representation. Matter 1, 1370\u20131384 (2019).","journal-title":"Matter"},{"key":"8628_CR10","doi-asserted-by":"crossref","unstructured":"Antunes, L. M., Butler, K. T. & Grau-Crespo, R. Crystal structure generation with autoregressive large language modeling. Nat. Commun. 15, 10570 (2024).","DOI":"10.1038\/s41467-024-54639-7"},{"key":"8628_CR11","unstructured":"Mila AI4Science et al. Crystal-GFN: sampling crystals with desirable properties and constraints. Preprint at https:\/\/arxiv.org\/abs\/2310.04925 (2023)."},{"key":"8628_CR12","unstructured":"Jiao, R. et al. Crystal structure prediction by joint equivariant diffusion. In Proc. Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS, 2023)."},{"key":"8628_CR13","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1038\/nmat3568","volume":"12","author":"S Curtarolo","year":"2013","unstructured":"Curtarolo, S. et al. The high-throughput highway to computational materials design. Nat. Mater. 12, 191\u2013201 (2013).","journal-title":"Nat. Mater."},{"key":"8628_CR14","doi-asserted-by":"publisher","first-page":"011002","DOI":"10.1063\/1.4812323","volume":"1","author":"A Jain","year":"2013","unstructured":"Jain, A. et al. Commentary: The Materials Project: a materials genome approach to accelerating materials innovation. APL Mater. 1, 011002 (2013).","journal-title":"APL Mater."},{"key":"8628_CR15","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1016\/j.commatsci.2012.02.005","volume":"58","author":"S Curtarolo","year":"2012","unstructured":"Curtarolo, S. et al. AFLOW: an automatic framework for high-throughput materials discovery. Comput. Mater. Sci. 58, 218\u2013226 (2012).","journal-title":"Comput. Mater. Sci."},{"key":"8628_CR16","doi-asserted-by":"publisher","first-page":"15010","DOI":"10.1038\/npjcompumats.2015.10","volume":"1","author":"S Kirklin","year":"2015","unstructured":"Kirklin, S. et al. The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies. npj Comput. Mater. 1, 15010 (2015).","journal-title":"npj Comput. Mater."},{"key":"8628_CR17","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-020-00637-5","volume":"7","author":"L Talirz","year":"2020","unstructured":"Talirz, L. et al. Materials Cloud, a platform for open computational science. Sci. Data 7, 299 (2020).","journal-title":"Sci. Data"},{"key":"8628_CR18","doi-asserted-by":"publisher","first-page":"145301","DOI":"10.1103\/PhysRevLett.120.145301","volume":"120","author":"T Xie","year":"2018","unstructured":"Xie, T. & Grossman, J. C. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Phys. Rev. Lett. 120, 145301 (2018).","journal-title":"Phys. Rev. Lett."},{"key":"8628_CR19","doi-asserted-by":"publisher","first-page":"3564","DOI":"10.1021\/acs.chemmater.9b01294","volume":"31","author":"C Chen","year":"2019","unstructured":"Chen, C., Ye, W., Zuo, Y., Zheng, C. & Ong, S. P. Graph networks as a universal machine learning framework for molecules and crystals. Chem. Mater. 31, 3564\u20133572 (2019).","journal-title":"Chem. Mater."},{"key":"8628_CR20","doi-asserted-by":"publisher","first-page":"10142","DOI":"10.1021\/acs.chemrev.0c01111","volume":"121","author":"OT Unke","year":"2021","unstructured":"Unke, O. T. et al. Machine learning force fields. Chem. Rev. 121, 10142\u201310186 (2021).","journal-title":"Chem. Rev."},{"key":"8628_CR21","doi-asserted-by":"publisher","first-page":"718","DOI":"10.1038\/s43588-022-00349-3","volume":"2","author":"C Chen","year":"2022","unstructured":"Chen, C. & Ong, S. P. A universal graph deep learning interatomic potential for the periodic table. Nat. Comput. Sci. 2, 718\u2013728 (2022).","journal-title":"Nat. Comput. Sci."},{"key":"8628_CR22","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1038\/s41586-020-2242-8","volume":"581","author":"M Zhong","year":"2020","unstructured":"Zhong, M. et al. Accelerated discovery of CO2 electrocatalysts using active machine learning. Nature 581, 178\u2013183 (2020).","journal-title":"Nature"},{"key":"8628_CR23","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1038\/s41586-023-06735-9","volume":"624","author":"A Merchant","year":"2023","unstructured":"Merchant, A. et al. Scaling deep learning for materials discovery. Nature 624, 80\u201385 (2023).","journal-title":"Nature"},{"key":"8628_CR24","doi-asserted-by":"publisher","first-page":"031001","DOI":"10.1088\/2515-7639\/ac7ba9","volume":"5","author":"J Shen","year":"2022","unstructured":"Shen, J. et al. Reflections on one million compounds in the open quantum materials database (OQMD). J. Phys. Mater. 5, 031001 (2022).","journal-title":"J. Phys. Mater."},{"key":"8628_CR25","doi-asserted-by":"crossref","unstructured":"Schmidt, J. et al. Machine\u2010learning\u2010assisted determination of the global zero\u2010temperature phase diagram of materials. Adv. Mater. 35, 2210788 (2023).","DOI":"10.1002\/adma.202210788"},{"key":"8628_CR26","doi-asserted-by":"publisher","first-page":"617","DOI":"10.1016\/j.chempr.2016.09.010","volume":"1","author":"DW Davies","year":"2016","unstructured":"Davies, D. W. et al. Computational screening of all stoichiometric inorganic materials. Chem 1, 617\u2013627 (2016).","journal-title":"Chem"},{"key":"8628_CR27","doi-asserted-by":"publisher","first-page":"360","DOI":"10.1126\/science.aat2663","volume":"361","author":"B Sanchez-Lengeling","year":"2018","unstructured":"Sanchez-Lengeling, B. & Aspuru-Guzik, A. Inverse molecular design using machine learning: generative models for matter engineering. Science 361, 360\u2013365 (2018).","journal-title":"Science"},{"key":"8628_CR28","doi-asserted-by":"publisher","DOI":"10.1038\/s41524-019-0221-0","volume":"5","author":"J Schmidt","year":"2019","unstructured":"Schmidt, J., Marques, M. R., Botti, S. & Marques, M. A. Recent advances and applications of machine learning in solid-state materials science. npj Comput. Mater. 5, 83 (2019).","journal-title":"npj Comput. Mater."},{"key":"8628_CR29","doi-asserted-by":"publisher","DOI":"10.1038\/s41524-020-0322-9","volume":"6","author":"Z Allahyari","year":"2020","unstructured":"Allahyari, Z. & Oganov, A. R. Coevolutionary search for optimal materials in the space of all possible compounds. npj Comput. Mater. 6, 55 (2020).","journal-title":"npj Comput. Mater."},{"key":"8628_CR30","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1021\/jacsau.2c00540","volume":"3","author":"JN Law","year":"2022","unstructured":"Law, J. N., Pandey, S., Gorai, P. & St. John, P. C. Upper-bound energy minimization to search for stable functional materials with graph neural networks. JACS Au 3, 113\u2013123 (2022).","journal-title":"JACS Au"},{"key":"8628_CR31","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1016\/j.matt.2021.11.032","volume":"5","author":"Z Ren","year":"2022","unstructured":"Ren, Z. et al. An invertible crystallographic representation for general inverse design of inorganic crystals with targeted properties. Matter 5, 314\u2013335 (2022).","journal-title":"Matter"},{"key":"8628_CR32","doi-asserted-by":"publisher","first-page":"6986","DOI":"10.1021\/acs.jcim.3c00969","volume":"63","author":"A Sultanov","year":"2023","unstructured":"Sultanov, A., Crivello, J.-C., Rebafka, T. & Sokolovska, N. Data-driven score-based models for generating stable structures with adaptive crystal cells. J. Chem. Inf. Model. 63, 6986\u20136997 (2023).","journal-title":"J. Chem. Inf. Model."},{"key":"8628_CR33","unstructured":"Song, Y. & Ermon, S. Generative modeling by estimating gradients of the data distribution. In Proc. 33rd International Conference on Neural Information Processing Systems Vol. 32, 11918\u201311930 (Curran Associates, 2019)."},{"key":"8628_CR34","unstructured":"Ho, J., Jain, A. & Abbeel, P. Denoising diffusion probabilistic models. In Proc. Advances in Neural Information Processing Systems Vol. 33 (eds Larochelle, H. et al.) 6840\u20136851 (NeurIPS, 2020)."},{"key":"8628_CR35","unstructured":"Song, Y. et al. Score-based generative modeling through stochastic differential equations. In Proc. International Conference on Learning Representations (ICLR, 2021)."},{"key":"8628_CR36","doi-asserted-by":"crossref","unstructured":"Zhang, L., Rao, A. & Agrawala, M. Adding conditional control to text-to-image diffusion models. In Proc. IEEE\/CVF International Conference on Computer Vision 3836\u20133847 (CVF, 2023).","DOI":"10.1109\/ICCV51070.2023.00355"},{"key":"8628_CR37","unstructured":"Ho, J. & Salimans, T. Classifier-free diffusion guidance. Preprint at https:\/\/arxiv.org\/abs\/2207.12598 (2022)."},{"key":"8628_CR38","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-022-01177-w","volume":"9","author":"J Schmidt","year":"2022","unstructured":"Schmidt, J., Wang, H.-C., Cerqueira, T. F. T., Botti, S. & Marques, M. A. A dataset of 175k stable and metastable materials calculated with the PBEsol and SCAN functionals. Sci. Data 9, 64 (2022).","journal-title":"Sci. Data"},{"key":"8628_CR39","doi-asserted-by":"publisher","first-page":"918","DOI":"10.1107\/S160057671900997X","volume":"52","author":"D Zagorac","year":"2019","unstructured":"Zagorac, D., M\u00fcller, H., Ruehl, S., Zagorac, J. & Rehme, S. Recent developments in the inorganic crystal structure database: theoretical crystal structure data and related features. J. Appl. Crystallogr. 52, 918\u2013925 (2019).","journal-title":"J. Appl. Crystallogr."},{"key":"8628_CR40","doi-asserted-by":"publisher","first-page":"011002","DOI":"10.1103\/PRXEnergy.3.011002","volume":"3","author":"J Leeman","year":"2024","unstructured":"Leeman, J. et al. Challenges in high-throughput inorganic materials prediction and autonomous synthesis. PRX Energy 3, 011002 (2024).","journal-title":"PRX Energy"},{"key":"8628_CR41","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1038\/s41578-019-0101-8","volume":"4","author":"AR Oganov","year":"2019","unstructured":"Oganov, A. R., Pickard, C. J., Zhu, Q. & Needs, R. J. Structure prediction drives materials discovery. Nat. Rev. Mater. 4, 331\u2013348 (2019).","journal-title":"Nat. Rev. Mater."},{"key":"8628_CR42","doi-asserted-by":"publisher","first-page":"053201","DOI":"10.1088\/0953-8984\/23\/5\/053201","volume":"23","author":"CJ Pickard","year":"2011","unstructured":"Pickard, C. J. & Needs, R. J. Ab initio random structure searching. J. Phys. Cond. Matter 23, 053201 (2011).","journal-title":"J. Phys. Cond. Matter"},{"key":"8628_CR43","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-023-41005-2","volume":"14","author":"PP Ferreira","year":"2023","unstructured":"Ferreira, P. P. et al. Search for ambient superconductivity in the Lu-N-H system. Nat. Commun. 14, 5367 (2023).","journal-title":"Nat. Commun."},{"key":"8628_CR44","unstructured":"Yang, H. et al. MatterSim: a deep learning atomistic model across elements, temperatures and pressures. Preprint at https:\/\/arxiv.org\/abs\/2405.04967 (2024)."},{"key":"8628_CR45","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1016\/j.actamat.2018.07.049","volume":"158","author":"J Cui","year":"2018","unstructured":"Cui, J. et al. Current progress and future challenges in rare-earth-free permanent magnets. Acta Mater. 158, 118\u2013137 (2018).","journal-title":"Acta Mater."},{"key":"8628_CR46","doi-asserted-by":"publisher","first-page":"2911","DOI":"10.1021\/cm400893e","volume":"25","author":"MW Gaultois","year":"2013","unstructured":"Gaultois, M. W. et al. Data-driven review of thermoelectric materials: performance and resource considerations. Chem. Mater. 25, 2911\u20132920 (2013).","journal-title":"Chem. Mater."},{"key":"8628_CR47","unstructured":"Ramesh, A., Dhariwal, P., Nichol, A., Chu, C. & Chen, M. Hierarchical text-conditional image generation with CLIP latents. Preprint at https:\/\/arxiv.org\/abs\/2204.06125 (2022)."},{"key":"8628_CR48","doi-asserted-by":"publisher","first-page":"1089","DOI":"10.1038\/s41586-023-06415-8","volume":"620","author":"JL Watson","year":"2023","unstructured":"Watson, J. L. et al. De novo design of protein structure and function with RFdiffusion. Nature 620, 1089\u20131100 (2023).","journal-title":"Nature"},{"key":"8628_CR49","doi-asserted-by":"publisher","first-page":"5658","DOI":"10.1039\/C9CS00159J","volume":"48","author":"W Guo","year":"2019","unstructured":"Guo, W., Zhang, K., Liang, Z., Zou, R. & Xu, Q. Electrochemical nitrogen fixation and utilization: theories, advanced catalyst materials and system design. Chem. Soc. Rev. 48, 5658\u20135716 (2019).","journal-title":"Chem. Soc. Rev."},{"key":"8628_CR50","unstructured":"Gebauer, N., Gastegger, M. & Sch\u00fctt, K. Symmetry-adapted generation of 3D point sets for the targeted discovery of molecules. In Proc. Advances in Neural Information Processing Systems Vol. 32 (NeurIPS, 2019)."}],"container-title":["Nature"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41586-025-08628-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41586-025-08628-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41586-025-08628-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T12:20:30Z","timestamp":1742386830000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41586-025-08628-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,16]]},"references-count":50,"journal-issue":{"issue":"8055","published-print":{"date-parts":[[2025,3,20]]}},"alternative-id":["8628"],"URL":"https:\/\/doi.org\/10.1038\/s41586-025-08628-5","relation":{},"ISSN":["0028-0836","1476-4687"],"issn-type":[{"value":"0028-0836","type":"print"},{"value":"1476-4687","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,16]]},"assertion":[{"value":"17 January 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 January 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 January 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"A.F., M.H., R.P., R.T., T.X., C.Z. and D.Z. are inventors of the pending, non-provisional patent application 18\/759,208 in the name of Microsoft Technology Licensing, relating to generative models for the computational design of materials. The other authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}