{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T05:43:35Z","timestamp":1785563015154,"version":"3.56.0"},"reference-count":81,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T00:00:00Z","timestamp":1783468800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T00:00:00Z","timestamp":1783468800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Nat Comput Sci"],"DOI":"10.1038\/s43588-026-01009-6","type":"journal-article","created":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T09:04:28Z","timestamp":1783501468000},"page":"789-801","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["NEP89: universal neuroevolution potential for inorganic and organic materials across 89 elements"],"prefix":"10.1038","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3116-365X","authenticated-orcid":false,"given":"Ting","family":"Liang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ke","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8549-6839","authenticated-orcid":false,"given":"Eric","family":"Lindgren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zherui","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiahui","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5282-7726","authenticated-orcid":false,"given":"Esm\u00e9e","family":"Berger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benrui","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bohan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanzhou","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keke","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5758-2369","authenticated-orcid":false,"given":"Penghua","family":"Ying","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nan","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9870-0467","authenticated-orcid":false,"given":"Haikuan","family":"Dong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5506-7507","authenticated-orcid":false,"given":"Shunda","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2516-6061","authenticated-orcid":false,"given":"Paul","family":"Erhart","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2253-8210","authenticated-orcid":false,"given":"Zheyong","family":"Fan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3210-3181","authenticated-orcid":false,"given":"Tapio","family":"Ala-Nissila","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0509-9508","authenticated-orcid":false,"given":"Jianbin","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,8]]},"reference":[{"key":"1009_CR1","doi-asserted-by":"publisher","first-page":"146401","DOI":"10.1103\/PhysRevLett.98.146401","volume":"98","author":"J Behler","year":"2007","unstructured":"Behler, J. & Parrinello, M. Generalized neural-network representation of high-dimensional potential-energy surfaces. Phys. Rev. Lett. 98, 146401 (2007).","journal-title":"Phys. Rev. Lett."},{"key":"1009_CR2","doi-asserted-by":"publisher","first-page":"136403","DOI":"10.1103\/PhysRevLett.104.136403","volume":"104","author":"AP Bart\u00f3k","year":"2010","unstructured":"Bart\u00f3k, A. P., Payne, M. C., Kondor, R. & Cs\u00e1nyi, G. Gaussian approximation potentials: the accuracy of quantum mechanics, without the electrons. Phys. Rev. Lett. 104, 136403 (2010).","journal-title":"Phys. Rev. Lett."},{"key":"1009_CR3","doi-asserted-by":"publisher","first-page":"170901","DOI":"10.1063\/1.4966192","volume":"145","author":"J Behler","year":"2016","unstructured":"Behler, J. Perspective: machine learning potentials for atomistic simulations. J. Chem. Phys. 145, 170901 (2016).","journal-title":"J. Chem. Phys."},{"key":"1009_CR4","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1146\/annurev-physchem-042018-052331","volume":"71","author":"F No\u00e9","year":"2020","unstructured":"No\u00e9, F., Tkatchenko, A., M\u00fcller, K.-R. & Clementi, C. Machine learning for molecular simulation. Annu. Rev. Phys. Chem. 71, 361\u2013390 (2020).","journal-title":"Annu. Rev. Phys. Chem."},{"key":"1009_CR5","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 (2021).","journal-title":"Chem. Rev."},{"key":"1009_CR6","doi-asserted-by":"publisher","first-page":"1902765","DOI":"10.1002\/adma.201902765","volume":"31","author":"VL Deringer","year":"2019","unstructured":"Deringer, V. L., Caro, M. A. & Cs\u00e1nyi, G. Machine learning interatomic potentials as emerging tools for materials science. Adv. Mater. 31, 1902765 (2019).","journal-title":"Adv. Mater."},{"key":"1009_CR7","doi-asserted-by":"publisher","first-page":"116980","DOI":"10.1016\/j.actamat.2021.116980","volume":"214","author":"Y Mishin","year":"2021","unstructured":"Mishin, Y. Machine-learning interatomic potentials for materials science. Acta Mater. 214, 116980 (2021).","journal-title":"Acta Mater."},{"key":"1009_CR8","first-page":"041048","volume":"8","author":"AP Bart\u00f3k","year":"2018","unstructured":"Bart\u00f3k, A. P., Kermode, J., Bernstein, N. & Cs\u00e1nyi, G. Machine learning a general-purpose interatomic potential for silicon. Phys. Rev. X 8, 041048 (2018).","journal-title":"Phys. Rev. X"},{"key":"1009_CR9","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-024-54554-x","volume":"15","author":"K Song","year":"2024","unstructured":"Song, K. et al. General-purpose machine-learned potential for 16 elemental metals and their alloys. Nat. Commun. 15, 10208 (2024).","journal-title":"Nat. Commun."},{"key":"1009_CR10","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-30687-9","volume":"13","author":"S Takamoto","year":"2022","unstructured":"Takamoto, S. et al. Towards universal neural network potential for material discovery applicable to arbitrary combination of 45 elements. Nat. Commun. 13, 2991 (2022).","journal-title":"Nat. Commun."},{"key":"1009_CR11","doi-asserted-by":"publisher","DOI":"10.1038\/s41524-024-01493-2","volume":"10","author":"D Zhang","year":"2024","unstructured":"Zhang, D. et al. DPA-2: a large atomic model as a multi-task learner. npj Comput. Mater. 10, 293 (2024).","journal-title":"npj Comput. Mater."},{"key":"1009_CR12","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":"1009_CR13","doi-asserted-by":"publisher","first-page":"1031","DOI":"10.1038\/s42256-023-00716-3","volume":"5","author":"B Deng","year":"2023","unstructured":"Deng, B. et al. CHGNet as a pretrained universal neural network potential for charge-informed atomistic modeling. Nat. Mach. Intell. 5, 1031\u20131041 (2023).","journal-title":"Nat. Mach. Intell."},{"key":"1009_CR14","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":"1009_CR15","doi-asserted-by":"publisher","first-page":"3525","DOI":"10.1016\/j.scib.2024.08.039","volume":"69","author":"F Xie","year":"2024","unstructured":"Xie, F., Lu, T., Meng, S. & Liu, M. GPTFF: a high-accuracy out-of-the-box universal AI force field for arbitrary inorganic materials. Sci. Bull. 69, 3525\u20133532 (2024).","journal-title":"Sci. Bull."},{"key":"1009_CR16","doi-asserted-by":"publisher","first-page":"184110","DOI":"10.1063\/5.0297006","volume":"163","author":"I Batatia","year":"2025","unstructured":"Batatia, I. et al. A foundation model for atomistic materials chemistry. J. Chem. Phys. 163, 184110 (2025).","journal-title":"J. Chem. Phys."},{"key":"1009_CR17","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":"1009_CR18","doi-asserted-by":"publisher","first-page":"104309","DOI":"10.1103\/PhysRevB.104.104309","volume":"104","author":"Z Fan","year":"2021","unstructured":"Fan, Z. et al. Neuroevolution machine learning potentials: combining high accuracy and low cost in atomistic simulations and application to heat transport. Phys. Rev. B 104, 104309 (2021).","journal-title":"Phys. Rev. B"},{"key":"1009_CR19","doi-asserted-by":"publisher","first-page":"eadn4397","DOI":"10.1126\/sciadv.adn4397","volume":"10","author":"OT Unke","year":"2024","unstructured":"Unke, O. T. et al. Biomolecular dynamics with machine-learned quantum-mechanical force fields trained on diverse chemical fragments. Sci. Adv. 10, eadn4397 (2024).","journal-title":"Sci. Adv."},{"key":"1009_CR20","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-022-01882-6","volume":"10","author":"P Eastman","year":"2023","unstructured":"Eastman, P. et al. SPICE, a dataset of drug-like molecules and peptides for training machine learning potentials. Sci. Data 10, 11 (2023).","journal-title":"Sci. Data"},{"key":"1009_CR21","doi-asserted-by":"publisher","first-page":"727","DOI":"10.1038\/s41557-023-01427-3","volume":"16","author":"S Zhang","year":"2024","unstructured":"Zhang, S. et al. Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential. Nat. Chem. 16, 727\u2013734 (2024).","journal-title":"Nat. Chem."},{"key":"1009_CR22","doi-asserted-by":"publisher","first-page":"17598","DOI":"10.1021\/jacs.4c07099","volume":"147","author":"DP Kov\u00e1cs","year":"2025","unstructured":"Kov\u00e1cs, D. P. et al. MACE-OFF: short-range transferable machine learning force fields for organic molecules. J. Am. Chem. Soc. 147, 17598 (2025).","journal-title":"J. Am. Chem. Soc."},{"key":"1009_CR23","doi-asserted-by":"publisher","first-page":"642","DOI":"10.1038\/s43588-026-00996-w","volume":"6","author":"L Barroso-Luque","year":"2026","unstructured":"Barroso-Luque, L. et al. Open Materials 2024 (OMat24) inorganic materials dataset and models. Nat. Comput. Sci. 6, 642\u2013652 (2026).","journal-title":"Nat. Comput. Sci."},{"key":"1009_CR24","doi-asserted-by":"publisher","DOI":"10.1038\/s41524-025-01764-6","volume":"11","author":"R Wang","year":"2025","unstructured":"Wang, R. et al. A pre-trained deep potential model for sulfide solid electrolytes with broad coverage and high accuracy. npj Comput. Mater. 11, 266 (2025).","journal-title":"npj Comput. Mater."},{"key":"1009_CR25","doi-asserted-by":"publisher","first-page":"1094","DOI":"10.1021\/acs.chemmater.4c02905","volume":"37","author":"R Ibragimova","year":"2025","unstructured":"Ibragimova, R., Kuklin, M. S., Zarrouk, T. & Caro, M. A. Unifying the description of hydrocarbons and hydrogenated carbon materials with a chemically reactive machine learning interatomic potential. Chem. Mater. 37, 1094\u20131110 (2025).","journal-title":"Chem. Mater."},{"key":"1009_CR26","doi-asserted-by":"publisher","first-page":"084111","DOI":"10.1063\/5.0142843","volume":"158","author":"Y Zhai","year":"2023","unstructured":"Zhai, Y., Caruso, A., Bore, S. L., Luo, Z. & Paesani, F. A \u2018short blanket\u2019 dilemma for a state-of-the-art neural network potential for water: reproducing experimental properties or the physics of the underlying many-body interactions?. J. Chem. Phys. 158, 084111 (2023).","journal-title":"J. Chem. Phys."},{"key":"1009_CR27","doi-asserted-by":"publisher","first-page":"154104","DOI":"10.1063\/1.3382344","volume":"132","author":"S Grimme","year":"2010","unstructured":"Grimme, S., Antony, J., Ehrlich, S. & Krieg, H. A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H\u2013Pu. J. Chem. Phys. 132, 154104 (2010).","journal-title":"J. Chem. Phys."},{"key":"1009_CR28","doi-asserted-by":"publisher","first-page":"1456","DOI":"10.1002\/jcc.21759","volume":"32","author":"S Grimme","year":"2011","unstructured":"Grimme, S., Ehrlich, S. & Goerigk, L. Effect of the damping function in dispersion corrected density functional theory. J. Comput. Chem. 32, 1456\u20131465 (2011).","journal-title":"J. Comput. Chem."},{"key":"1009_CR29","doi-asserted-by":"crossref","unstructured":"Schaul, T., Glasmachers, T. & Schmidhuber, J. High dimensions and heavy tails for natural evolution strategies. In Proc. 13th Annual Conference on Genetic and Evolutionary Computation (ed. Krasnogor, N.) 845\u2013852 (Association for Computing Machinery, 2011).","DOI":"10.1145\/2001576.2001692"},{"key":"1009_CR30","doi-asserted-by":"publisher","first-page":"5395","DOI":"10.1021\/ct400863t","volume":"9","author":"V Babin","year":"2013","unstructured":"Babin, V., Leforestier, C. & Paesani, F. Development of a \u2018first principles\u2019 water potential with flexible monomers: dimer potential energy surface, VRT spectrum, and second virial coefficient. J. Chem. Theory Comput. 9, 5395\u20135403 (2013).","journal-title":"J. Chem. Theory Comput."},{"key":"1009_CR31","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":"1009_CR32","doi-asserted-by":"publisher","first-page":"2427","DOI":"10.1021\/ct2002946","volume":"7","author":"J Rezac","year":"2011","unstructured":"Rezac, J., Riley, K. E. & Hobza, P. S66: a well-balanced database of benchmark interaction energies relevant to biomolecular structures. J. Chem. Theory Comput. 7, 2427\u20132438 (2011).","journal-title":"J. Chem. Theory Comput."},{"key":"1009_CR33","doi-asserted-by":"publisher","first-page":"134701","DOI":"10.1063\/5.0102645","volume":"157","author":"F Della Pia","year":"2022","unstructured":"Della Pia, F., Zen, A., Alf\u00e8, D. & Michaelides, A. DMC-ICE13: ambient and high pressure polymorphs of ice from diffusion Monte Carlo and density functional theory. J. Chem. Phys. 157, 134701 (2022).","journal-title":"J. Chem. Phys."},{"key":"1009_CR34","doi-asserted-by":"publisher","first-page":"012001","DOI":"10.7566\/JPSJ.92.012001","volume":"92","author":"A Togo","year":"2023","unstructured":"Togo, A. First-principles phonon calculations with Phonopy and Phono3py. J. Phys. Soc. Jpn. 92, 012001 (2023).","journal-title":"J. Phys. Soc. Jpn."},{"key":"1009_CR35","doi-asserted-by":"publisher","first-page":"094205","DOI":"10.1103\/PhysRevB.111.094205","volume":"111","author":"Y Wang","year":"2025","unstructured":"Wang, Y., Fan, Z., Qian, P., Caro, M. A. & Ala-Nissila, T. Density dependence of thermal conductivity in nanoporous and amorphous carbon with machine-learned molecular dynamics. Phys. Rev. B 111, 094205 (2025).","journal-title":"Phys. Rev. B"},{"key":"1009_CR36","doi-asserted-by":"publisher","DOI":"10.1038\/s41524-025-01777-1","volume":"11","author":"K Xu","year":"2025","unstructured":"Xu, K. et al. NEP-MB-pol: a unified machine-learned framework for fast and accurate prediction of water\u015b thermodynamic and transport properties. npj Comput. Mater. 11, 279 (2025).","journal-title":"npj Comput. Mater."},{"key":"1009_CR37","doi-asserted-by":"publisher","first-page":"21070","DOI":"10.1021\/acsami.0c02156","volume":"12","author":"S Alvi","year":"2020","unstructured":"Alvi, S. et al. Synthesis and mechanical characterization of a CuMoTaWV high-entropy film by magnetron sputtering. ACS Appl. Mater. Interfaces 12, 21070\u201321079 (2020).","journal-title":"ACS Appl. Mater. Interfaces"},{"key":"1009_CR38","unstructured":"Van\u00fdsek, P. in CRC Handbook of Chemistry and Physics (ed. Lide, D. R.) 5-76\u20135-78 (Taylor and Francis, 2006)."},{"key":"1009_CR39","doi-asserted-by":"publisher","first-page":"770","DOI":"10.1021\/j150519a017","volume":"58","author":"LG Longsworth","year":"1954","unstructured":"Longsworth, L. G. Temperature dependence of diffusion in aqueous solutions. J. Phys. Chem. 58, 770\u2013773 (1954).","journal-title":"J. Phys. Chem."},{"key":"1009_CR40","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/S0022-2836(63)80023-6","volume":"7","author":"G Ramachandran","year":"1963","unstructured":"Ramachandran, G., Ramakrishnan, C. & Sasisekharan, V. Stereochemistry of polypeptide chain configurations. J. Mol. Biol. 7, 95\u201399 (1963).","journal-title":"J. Mol. Biol."},{"key":"1009_CR41","doi-asserted-by":"publisher","first-page":"670","DOI":"10.1126\/science.abb7023","volume":"369","author":"Y-L Hong","year":"2020","unstructured":"Hong, Y.-L. et al. Chemical vapor deposition of layered two-dimensional MoSi2N4 materials. Science 369, 670\u2013674 (2020).","journal-title":"Science"},{"key":"1009_CR42","doi-asserted-by":"publisher","first-page":"121868","DOI":"10.1016\/j.jnoncrysol.2022.121868","volume":"596","author":"S Hosokawa","year":"2022","unstructured":"Hosokawa, S. et al. Relationship between atomic structure and excellent glass forming ability in Pd42.5Ni7.5Cu30P20 metallic glass. J. Non Cryst. Solids 596, 121868 (2022).","journal-title":"J. Non Cryst. Solids"},{"key":"1009_CR43","doi-asserted-by":"publisher","first-page":"112012","DOI":"10.1016\/j.matdes.2023.112012","volume":"231","author":"R Zhao","year":"2023","unstructured":"Zhao, R. et al. Development of a neuroevolution machine learning potential of Pd-Cu-Ni-P alloys. Mater. Design 231, 112012 (2023).","journal-title":"Mater. Design"},{"key":"1009_CR44","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1016\/j.intermet.2006.10.040","volume":"15","author":"O Haruyama","year":"2007","unstructured":"Haruyama, O. Thermodynamic approach to free volume kinetics during isothermal relaxation in bulk Pd-Cu-Ni-P20 glasses. Intermetallics 15, 659\u2013662 (2007).","journal-title":"Intermetallics"},{"key":"1009_CR45","doi-asserted-by":"publisher","first-page":"064308","DOI":"10.1103\/PhysRevB.99.064308","volume":"99","author":"Z Fan","year":"2019","unstructured":"Fan, Z., Dong, H., Harju, A. & Ala-Nissila, T. Homogeneous nonequilibrium molecular dynamics method for heat transport and spectral decomposition with many-body potentials. Phys. Rev. B 99, 064308 (2019).","journal-title":"Phys. Rev. B"},{"key":"1009_CR46","doi-asserted-by":"publisher","first-page":"4699","DOI":"10.1021\/acs.jctc.5c01950","volume":"22","author":"W Jiang","year":"2026","unstructured":"Jiang, W. et al. Accurate modeling of interfacial thermal transport in van der waals heterostructures via hybrid machine learning and registry-dependent potentials. J. Chem. Theory Comput. 22, 4699\u20134715 (2026).","journal-title":"J. Chem. Theory Comput."},{"key":"1009_CR47","doi-asserted-by":"publisher","first-page":"107669","DOI":"10.1016\/j.ijthermalsci.2022.107669","volume":"179","author":"W Bao","year":"2022","unstructured":"Bao, W., Chen, G., Wang, Z. & Tang, D. Bilateral phonon transport modulation of Bi-layer TMDCs (MX2, M=Mo, W; X=S). Int. J. Therm. Sci. 179, 107669 (2022).","journal-title":"Int. J. Therm. Sci."},{"key":"1009_CR48","doi-asserted-by":"publisher","first-page":"085106","DOI":"10.1063\/1.4942827","volume":"119","author":"X Gu","year":"2016","unstructured":"Gu, X., Li, B. & Yang, R. Layer thickness-dependent phonon properties and thermal conductivity of MoS2. J. Appl. Phys. 119, 085106 (2016).","journal-title":"J. Appl. Phys."},{"key":"1009_CR49","doi-asserted-by":"publisher","first-page":"6400","DOI":"10.1038\/ncomms7400","volume":"6","author":"A Cepellotti","year":"2015","unstructured":"Cepellotti, A. et al. Phonon hydrodynamics in two-dimensional materials. Nat. Commun. 6, 6400 (2015).","journal-title":"Nat. Commun."},{"key":"1009_CR50","doi-asserted-by":"publisher","first-page":"25509","DOI":"10.1039\/D5TA03325J","volume":"13","author":"E Lindgren","year":"2025","unstructured":"Lindgren, E. et al. Predicting neutron experiments from first principles: a workflow powered by machine learning. J. Mater. Chem. A 13, 25509 (2025).","journal-title":"J. Mater. Chem. A"},{"key":"1009_CR51","doi-asserted-by":"publisher","first-page":"4787","DOI":"10.1021\/acs.jctc.6c00146","volume":"22","author":"Z Fan","year":"2026","unstructured":"Fan, Z. et al. qNEP: a highly efficient neuroevolution potential with dynamic charges for large-scale atomistic simulations. J. Chem. Theory Comput. 22, 4787\u20134801 (2026).","journal-title":"J. Chem. Theory Comput."},{"key":"1009_CR52","doi-asserted-by":"publisher","unstructured":"Liu, J. GPUMD-Wizard: a Python package for generating and evaluating machine learning potentials. Zenodo https:\/\/doi.org\/10.5281\/zenodo.13948627 (2024).","DOI":"10.5281\/zenodo.13948627"},{"key":"1009_CR53","doi-asserted-by":"publisher","first-page":"273002","DOI":"10.1088\/1361-648X\/aa680e","volume":"29","author":"A Hjorth Larsen","year":"2017","unstructured":"Hjorth Larsen, A. et al. The atomic simulation environment\u2014a Python library for working with atoms. J. Phys. Condens. Matter 29, 273002 (2017).","journal-title":"J. Phys. Condens. Matter"},{"key":"1009_CR54","doi-asserted-by":"publisher","first-page":"6264","DOI":"10.21105\/joss.06264","volume":"9","author":"E Lindgren","year":"2024","unstructured":"Lindgren, E. et al. calorine: a python package for constructing and sampling neuroevolution potential models. J. Open Source Softw. 9, 6264 (2024).","journal-title":"J. Open Source Softw."},{"key":"1009_CR55","unstructured":"Liu, R. et al. MatCalc: a Python library for calculating materials properties from the potential energy surface (PES). GitHub https:\/\/github.com\/materialsvirtuallab\/matcalc (2024)"},{"key":"1009_CR56","doi-asserted-by":"publisher","first-page":"015012","DOI":"10.1088\/0965-0393\/18\/1\/015012","volume":"18","author":"A Stukowski","year":"2009","unstructured":"Stukowski, A. Visualization and analysis of atomistic simulation data with OVITO\u2014the Open Visualization Tool. Model. Simul. Mater. Sci. Eng. 18, 015012 (2009).","journal-title":"Model. Simul. Mater. Sci. Eng."},{"key":"1009_CR57","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1016\/j.msea.2003.10.257","volume":"375-377","author":"B Cantor","year":"2004","unstructured":"Cantor, B., Chang, I., Knight, P. & Vincent, A. Microstructural development in equiatomic multicomponent alloys. Mater. Sci. Eng A 375-377, 213\u2013218 (2004).","journal-title":"Mater. Sci. Eng A"},{"key":"1009_CR58","doi-asserted-by":"publisher","first-page":"2635","DOI":"10.1063\/1.463940","volume":"97","author":"GJ Martyna","year":"1992","unstructured":"Martyna, G. J., Klein, M. L. & Tuckerman, M. Nos\u00e9\u2013Hoover chains: the canonical ensemble via continuous dynamics. J. Chem. Phys. 97, 2635\u20132643 (1992).","journal-title":"J. Chem. Phys."},{"key":"1009_CR59","doi-asserted-by":"publisher","first-page":"e70028","DOI":"10.1002\/mgea.70028","volume":"3","author":"K Xu","year":"2025","unstructured":"Xu, K. et al. GPUMD 4.0: a high-performance molecular dynamics package for versatile materials simulations with machine-learned potentials. Mater. Genome Eng. Adv. 3, e70028 (2025).","journal-title":"Mater. Genome Eng. Adv."},{"key":"1009_CR60","doi-asserted-by":"publisher","first-page":"108171","DOI":"10.1016\/j.cpc.2021.108171","volume":"271","author":"AP Thompson","year":"2022","unstructured":"Thompson, A. P. et al. LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales. Comput. Phys. Commun. 271, 108171 (2022).","journal-title":"Comput. Phys. Commun."},{"key":"1009_CR61","doi-asserted-by":"publisher","first-page":"014101","DOI":"10.1063\/1.2408420","volume":"126","author":"G Bussi","year":"2007","unstructured":"Bussi, G., Donadio, D. & Parrinello, M. Canonical sampling through velocity rescaling. J. Chem. Phys. 126, 014101 (2007).","journal-title":"J. Chem. Phys."},{"key":"1009_CR62","doi-asserted-by":"publisher","first-page":"114107","DOI":"10.1063\/5.0020514","volume":"153","author":"M Bernetti","year":"2020","unstructured":"Bernetti, M. & Bussi, G. Pressure control using stochastic cell rescaling. J. Chem. Phys. 153, 114107 (2020).","journal-title":"J. Chem. Phys."},{"key":"1009_CR63","doi-asserted-by":"publisher","first-page":"056707","DOI":"10.1103\/PhysRevE.75.056707","volume":"75","author":"G Bussi","year":"2007","unstructured":"Bussi, G. & Parrinello, M. Accurate sampling using langevin dynamics. Phys. Rev. E 75, 056707 (2007).","journal-title":"Phys. Rev. E"},{"key":"1009_CR64","doi-asserted-by":"crossref","unstructured":"Berendsen, H., van der Spoel, D. & van Drunen, R. GROMACS: a message-passing parallel molecular dynamics implementation. Comput. Phys. Commun. 91, 43\u201356 (1995).","DOI":"10.1016\/0010-4655(95)00042-E"},{"key":"1009_CR65","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1038\/nmeth.4067","volume":"14","author":"J Huang","year":"2017","unstructured":"Huang, J. et al. CHARMM36m: an improved force field for folded and intrinsically disordered proteins. Nat. Methods 14, 71\u201373 (2017).","journal-title":"Nat. Methods"},{"key":"1009_CR66","doi-asserted-by":"publisher","first-page":"4812","DOI":"10.1016\/j.bmc.2016.06.034","volume":"24","author":"I Soteras Guti\u00e9rrez","year":"2016","unstructured":"Soteras Guti\u00e9rrez, I. et al. Parametrization of halogen bonds in the CHARMM general force field: improved treatment of ligand-protein interactions. Bioorg. Med. Chem. 24, 4812\u20134825 (2016).","journal-title":"Bioorg. Med. Chem."},{"key":"1009_CR67","doi-asserted-by":"publisher","first-page":"17953","DOI":"10.1103\/PhysRevB.50.17953","volume":"50","author":"PE Bl\u00f6chl","year":"1994","unstructured":"Bl\u00f6chl, P. E. Projector augmented-wave method. Phys. Rev. B 50, 17953 (1994).","journal-title":"Phys. Rev. B"},{"key":"1009_CR68","doi-asserted-by":"publisher","first-page":"11169","DOI":"10.1103\/PhysRevB.54.11169","volume":"54","author":"G Kresse","year":"1996","unstructured":"Kresse, G. & Furthm\u00fcller, J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys. Rev. B 54, 11169 (1996).","journal-title":"Phys. Rev. B"},{"key":"1009_CR69","doi-asserted-by":"publisher","first-page":"3865","DOI":"10.1103\/PhysRevLett.77.3865","volume":"77","author":"JP Perdew","year":"1996","unstructured":"Perdew, J. P., Burke, K. & Ernzerhof, M. Generalized gradient approximation made simple. Phys. Rev. Lett. 77, 3865 (1996).","journal-title":"Phys. Rev. Lett."},{"key":"1009_CR70","doi-asserted-by":"publisher","first-page":"055007","DOI":"10.1088\/0965-0393\/24\/5\/055007","volume":"24","author":"PM Larsen","year":"2016","unstructured":"Larsen, P. M., Schmidt, S. & Schi\u00f8tz, J. Robust structural identification via polyhedral template matching. Model. Simul. Mater. Sci. Eng. 24, 055007 (2016).","journal-title":"Model. Simul. Mater. Sci. Eng."},{"key":"1009_CR71","doi-asserted-by":"publisher","first-page":"246401","DOI":"10.1103\/PhysRevLett.92.246401","volume":"92","author":"M Dion","year":"2004","unstructured":"Dion, M., Rydberg, H., Schr\u00f6der, E., Langreth, D. C. & Lundqvist, B. I. Van der Waals density functional for general geometries. Phys. Rev. Lett. 92, 246401 (2004).","journal-title":"Phys. Rev. Lett."},{"key":"1009_CR72","doi-asserted-by":"publisher","first-page":"3684","DOI":"10.1063\/1.448118","volume":"81","author":"HJC Berendsen","year":"1984","unstructured":"Berendsen, H. J. C., Postma, J. P. M., van Gunsteren, W. F., DiNola, A. & Haak, J. R. Molecular dynamics with coupling to an external bath. J. Chem. Phys. 81, 3684\u20133690 (1984).","journal-title":"J. Chem. Phys."},{"key":"1009_CR73","doi-asserted-by":"publisher","first-page":"124104","DOI":"10.1063\/1.3489925","volume":"133","author":"M Ceriotti","year":"2010","unstructured":"Ceriotti, M., Parrinello, M., Markland, T. E. & Manolopoulos, D. E. Efficient stochastic thermostatting of path integral molecular dynamics. J. Chem. Phys. 133, 124104 (2010).","journal-title":"J. Chem. Phys."},{"key":"1009_CR74","doi-asserted-by":"publisher","first-page":"064109","DOI":"10.1063\/5.0241006","volume":"162","author":"P Ying","year":"2025","unstructured":"Ying, P. et al. Highly efficient path-integral molecular dynamics simulations with GPUMD using neuroevolution potentials: case studies on thermal properties of materials. J. Chem. Phys. 162, 064109 (2025).","journal-title":"J. Chem. Phys."},{"key":"1009_CR75","doi-asserted-by":"publisher","first-page":"2000240","DOI":"10.1002\/adts.202000240","volume":"4","author":"E Fransson","year":"2021","unstructured":"Fransson, E., Slabanja, M., Erhart, P. & Wahnstr\u00f6m, G. Dynasor\u2014a tool for extracting dynamical structure factors and current correlation functions from molecular dynamics simulations. Adv. Theory Simul. 4, 2000240 (2021).","journal-title":"Adv. Theory Simul."},{"key":"1009_CR76","doi-asserted-by":"publisher","first-page":"109759","DOI":"10.1016\/j.cpc.2025.109759","volume":"316","author":"E Berger","year":"2025","unstructured":"Berger, E. et al. Dynasor 2: from simulation to experiment through correlation functions. Comput. Phys. Commun. 316, 109759 (2025).","journal-title":"Comput. Phys. Commun."},{"key":"1009_CR77","doi-asserted-by":"publisher","first-page":"1689","DOI":"10.1107\/S1600576722009256","volume":"55","author":"R Fair","year":"2022","unstructured":"Fair, R. et al. Euphonic: inelastic neutron scattering simulations from force constants and visualization tools for phonon properties. J. Appl. Crystallogr. 55, 1689\u20131703 (2022).","journal-title":"J. Appl. Crystallogr."},{"key":"1009_CR78","unstructured":"Turanyi, R., Jackson, A. & Wilkins, J. Pace-neutrons\/resins: Python library for resolution functions of inelastic neutron scattering instruments. GitHub https:\/\/github.com\/pace-neutrons\/resins (2025)."},{"key":"1009_CR79","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1016\/j.nima.2014.07.029","volume":"764","author":"O Arnold","year":"2014","unstructured":"Arnold, O. et al. Mantid\u2014data analysis and visualization package for neutron scattering and \u03bc SR experiments. Nucl. Instrum. Methods Phys. Res. A 764, 156\u2013166 (2014).","journal-title":"Nucl. Instrum. Methods Phys. Res. A"},{"key":"1009_CR80","doi-asserted-by":"publisher","unstructured":"Liang, T. et al. Dataset and models supporting \u2018NEP89: universal neuroevolution potential for inorganic and organic materials across 89 elements\u2019. Zenodo https:\/\/doi.org\/10.5281\/zenodo.19440423 (2026).","DOI":"10.5281\/zenodo.19440423"},{"key":"1009_CR81","doi-asserted-by":"publisher","unstructured":"Fan, Z. brucefan1983\/GPUMD: GPUMD-v5.0. Zenodo https:\/\/doi.org\/10.5281\/zenodo.18977569 (2026).","DOI":"10.5281\/zenodo.18977569"}],"container-title":["Nature Computational Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s43588-026-01009-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s43588-026-01009-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s43588-026-01009-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T22:01:36Z","timestamp":1784757696000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s43588-026-01009-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,8]]},"references-count":81,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2026,7]]}},"alternative-id":["1009"],"URL":"https:\/\/doi.org\/10.1038\/s43588-026-01009-6","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-7058411\/v1","asserted-by":"object"}]},"ISSN":["2662-8457"],"issn-type":[{"value":"2662-8457","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,8]]},"assertion":[{"value":"6 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 May 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 July 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Ethics"}}]}}