{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T01:09:46Z","timestamp":1775264986552,"version":"3.50.1"},"reference-count":47,"publisher":"IOP Publishing","issue":"3","license":[{"start":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T00:00:00Z","timestamp":1755734400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T00:00:00Z","timestamp":1755734400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100006151","name":"Basic Energy Sciences","doi-asserted-by":"crossref","award":["DE-AC0500OR22725"],"award-info":[{"award-number":["DE-AC0500OR22725"]}],"id":[{"id":"10.13039\/100006151","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Mach. Learn.: Sci. Technol."],"published-print":{"date-parts":[[2025,9,30]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The accurate calculation of phonons and vibrational spectra remains a significant challenge, requiring highly precise evaluations of interatomic forces. Traditional methods based on the quantum description of the electronic structure, while widely used, are computationally expensive and demand substantial expertise. Emerging universal machine learning interatomic potentials (uMLIPs) offer a transformative alternative by employing pre-trained neural network surrogates to predict interatomic forces directly from atomic coordinates. This approach dramatically reduces computation time and minimizes the need for technical knowledge. In this paper, we produce a phonon database comprising nearly 5000 inorganic crystals to benchmark the performance of several leading uMLIPs. We further assess these models in real-world applications by using them to analyze experimental inelastic neutron scattering data collected on a variety of materials. Through detailed comparisons, we identify the strengths and limitations of these uMLIPs, providing insights into their accuracy and suitability for fast calculations of phonons and related properties, as well as the potential for real-time interpretation of neutron scattering spectra. Our findings highlight how the rapid advancement of AI in science is revolutionizing experimental research and data analysis.<\/jats:p>","DOI":"10.1088\/2632-2153\/adfa68","type":"journal-article","created":{"date-parts":[[2025,8,11]],"date-time":"2025-08-11T22:51:25Z","timestamp":1754952685000},"page":"030504","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Benchmarking universal machine learning interatomic potentials for rapid analysis of inelastic neutron scattering data<sup>*<\/sup>"],"prefix":"10.1088","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1979-2402","authenticated-orcid":true,"given":"Bowen","family":"Han","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3263-4812","authenticated-orcid":true,"given":"Yongqiang","family":"Cheng","sequence":"additional","affiliation":[]}],"member":"266","published-online":{"date-parts":[[2025,8,21]]},"reference":[{"key":"mlstadfa68bib1","author":"Reissland","year":"1973"},{"key":"mlstadfa68bib2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.scriptamat.2015.07.021","article-title":"First principles phonon calculations in materials science","volume":"108","author":"Togo","year":"2015","journal-title":"Scr. Mater."},{"key":"mlstadfa68bib3","author":"Fultz","year":"2020"},{"key":"mlstadfa68bib4","article-title":"Real-time experiment-theory closed-loop interaction for autonomous materials science","author":"Liang","year":"2024"},{"key":"mlstadfa68bib5","doi-asserted-by":"publisher","DOI":"10.1016\/j.pmatsci.2022.101043","article-title":"Toward autonomous laboratories: convergence of artificial intelligence and experimental automation","volume":"132","author":"Xie","year":"2023","journal-title":"Prog. Mater. Sci."},{"key":"mlstadfa68bib6","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-63931-x","article-title":"A machine learning decision criterion for reducing scan time for hyperspectral neutron computed tomography systems","volume":"14","author":"Tang","year":"2024","journal-title":"Sci. Rep."},{"key":"mlstadfa68bib7","doi-asserted-by":"publisher","first-page":"10142","DOI":"10.1021\/acs.chemrev.0c01111","article-title":"Machine learning force fields","volume":"121","author":"Unke","year":"2021","journal-title":"Chem. Rev."},{"key":"mlstadfa68bib8","article-title":"Matbench discovery\u2014a framework to evaluate machine learning crystal stability predictions","author":"Riebesell","year":"2024"},{"key":"mlstadfa68bib9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41524-024-01500-6","article-title":"Systematic softening in universal machine learning interatomic potentials","volume":"11","author":"Deng","year":"2025","journal-title":"npj Comput. Mater."},{"key":"mlstadfa68bib10","article-title":"Universal machine learning interatomic potentials are ready for phonons","author":"Loew","year":"2024"},{"key":"mlstadfa68bib11","article-title":"Atztogo\/Phonondb","author":"Togo","year":"2025"},{"key":"mlstadfa68bib12","doi-asserted-by":"publisher","DOI":"10.1016\/j.cpc.2024.109288","article-title":"INSPIRED: inelastic neutron scattering prediction for instantaneous results and experimental design","volume":"304","author":"Han","year":"2024","journal-title":"Comput. Phys. Commun."},{"key":"mlstadfa68bib13","doi-asserted-by":"publisher","DOI":"10.1063\/1.4812323","article-title":"Commentary: The materials project: a materials genome approach to accelerating materials innovation","volume":"1","author":"Jain","year":"2013","journal-title":"APL Mater."},{"key":"mlstadfa68bib14","article-title":"Learning smooth and expressive interatomic potentials for physical property prediction","author":"Fu","year":"2025"},{"key":"mlstadfa68bib15","article-title":"Orb-v3: atomistic simulation at scale","author":"Rhodes","year":"2025"},{"key":"mlstadfa68bib16","doi-asserted-by":"publisher","first-page":"4857","DOI":"10.1021\/acs.jctc.4c00190","article-title":"Scalable parallel algorithm for graph neural network interatomic potentials in molecular dynamics simulations","volume":"20","author":"Park","year":"2024","journal-title":"J. Chem. Theory Comput."},{"key":"mlstadfa68bib17","doi-asserted-by":"publisher","first-page":"1042","DOI":"10.1021\/jacs.4c14455","article-title":"Data-efficient multifidelity training for high-fidelity machine learning interatomic potentials","volume":"147","author":"Kim","year":"2024","journal-title":"J. Am. Chem. Soc."},{"key":"mlstadfa68bib18","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevX.14.021036","article-title":"Graph atomic cluster expansion for semilocal interactions beyond equivariant message passing","volume":"14","author":"Bochkarev","year":"2024","journal-title":"Phys. Rev. X"},{"key":"mlstadfa68bib19","article-title":"MatterSim: a deep learning atomistic model across elements, temperatures and pressures","author":"Yang","year":"2024"},{"key":"mlstadfa68bib20","article-title":"A foundation model for atomistic materials chemistry","author":"Batatia","year":"2024"},{"key":"mlstadfa68bib21","article-title":"MACE: higher order equivariant message passing neural networks for fast and accurate force fields","author":"Batatia","year":"2022"},{"key":"mlstadfa68bib22","article-title":"The design space of E(3)-equivariant atom-centered interatomic potentials","author":"Batatia","year":"2022"},{"key":"mlstadfa68bib23","article-title":"Open Materials 2024 (OMat24) inorganic materials dataset and models","author":"Barroso-Luque","year":"2024"},{"key":"mlstadfa68bib24","article-title":"Orb: a fast, scalable neural network potential","author":"Neumann","year":"2024"},{"key":"mlstadfa68bib25","doi-asserted-by":"publisher","first-page":"1031","DOI":"10.1038\/s42256-023-00716-3","article-title":"CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling","volume":"5","author":"Deng","year":"2023","journal-title":"Nat. Mach. Intell."},{"key":"mlstadfa68bib26","doi-asserted-by":"publisher","first-page":"718","DOI":"10.1038\/s43588-022-00349-3","article-title":"A universal graph deep learning interatomic potential for the periodic table","volume":"2","author":"Chen","year":"2022","journal-title":"Nat. Comput. Sci."},{"key":"mlstadfa68bib27","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1016\/S0003-2670(97)00238-9","article-title":"Computer-assisted IR spectra prediction\u2014linked similarity searches for structures and spectra","volume":"348","author":"Baumann","year":"1997","journal-title":"Anal. Chim. Acta"},{"key":"mlstadfa68bib28","doi-asserted-by":"publisher","first-page":"957","DOI":"10.1038\/s43588-023-00550-y","article-title":"A deep learning model for predicting selected organic molecular spectra","volume":"3","author":"Zou","year":"2023","journal-title":"Nat. Comput. Sci."},{"key":"mlstadfa68bib29","author":"Squires","year":"2012","edition":"3rd edn"},{"key":"mlstadfa68bib30","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1080\/10448639208218770","article-title":"Neutron scattering lengths and cross sections","volume":"3","author":"Sears","year":"1992","journal-title":"Neutron News"},{"key":"mlstadfa68bib31","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/251\/1\/012058","article-title":"SEQUOIA: a newly operating chopper spectrometer at the SNS","volume":"251","author":"Granroth","year":"2010","journal-title":"J. Phys.: Conf. Ser."},{"key":"mlstadfa68bib32","doi-asserted-by":"publisher","first-page":"1974","DOI":"10.1021\/acs.jctc.8b01250","article-title":"Simulation of inelastic neutron scattering spectra using OCLIMAX","volume":"15","author":"Cheng","year":"2019","journal-title":"J. Chem. Theory Comput."},{"key":"mlstadfa68bib33","doi-asserted-by":"publisher","DOI":"10.1088\/2632-2153\/acb315","article-title":"Direct prediction of inelastic neutron scattering spectra from the crystal structure","volume":"4","author":"Cheng","year":"2023","journal-title":"Mach. Learn.: Sci. Technol."},{"key":"mlstadfa68bib34","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevB.105.174308","article-title":"Thermal expansion and phonon anharmonicity of cuprite studied by inelastic neutron scattering and ab initio calculations","volume":"105","author":"Saunders","year":"2022","journal-title":"Phys. Rev. B"},{"key":"mlstadfa68bib35","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevResearch.4.013067","article-title":"Role of the third dimension in searching for Majorana fermions in \u03b1-RuCl3 via phonons","volume":"4","author":"Mu","year":"2022","journal-title":"Phys. Rev. Res."},{"key":"mlstadfa68bib36","doi-asserted-by":"publisher","first-page":"719","DOI":"10.1016\/j.nima.2009.03.204","article-title":"Resolution of VISION, a crystal-analyzer spectrometer","volume":"604","author":"Seeger","year":"2009","journal-title":"Nucl. Instrum. Methods Phys. Res."},{"key":"mlstadfa68bib37","doi-asserted-by":"crossref","DOI":"10.1021\/jacs.4c07099","article-title":"MACE-OFF: transferable short range machine learning force fields for organic molecules","author":"Kov\u00e1cs","year":"2025"},{"key":"mlstadfa68bib38","doi-asserted-by":"publisher","first-page":"29","DOI":"10.3390\/inorganics9050029","article-title":"Study of anharmonicity in zirconium hydrides using inelastic neutron scattering and ab-initio computer modeling","volume":"9","author":"Zhang","year":"2021","journal-title":"Inorganics"},{"key":"mlstadfa68bib39","doi-asserted-by":"publisher","first-page":"2750","DOI":"10.1002\/slct.201700250","article-title":"Understanding ZIF-8 performance upon gas adsorption by means of inelastic neutron scattering","volume":"2","author":"Casco","year":"2017","journal-title":"Chem. Select"},{"key":"mlstadfa68bib40","doi-asserted-by":"publisher","DOI":"10.1016\/j.cplett.2021.138727","article-title":"Low rotational barriers for the most dynamically active methyl groups in the proposed antiviral drugs for treatment of SARS-CoV-2, apilimod and tetrandrine","volume":"777","author":"Mamontov","year":"2021","journal-title":"Chem. Phys. Lett."},{"key":"mlstadfa68bib41","doi-asserted-by":"publisher","first-page":"11169","DOI":"10.1103\/PhysRevB.54.11169","article-title":"Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set","volume":"54","author":"Kresse","year":"1996","journal-title":"Phys. Rev. B"},{"key":"mlstadfa68bib42","doi-asserted-by":"publisher","first-page":"17953","DOI":"10.1103\/PhysRevB.50.17953","article-title":"Projector augmented-wave method","volume":"50","author":"Bl\u00f6chl","year":"1994","journal-title":"Phys. Rev. B"},{"key":"mlstadfa68bib43","doi-asserted-by":"publisher","first-page":"3865","DOI":"10.1103\/PhysRevLett.77.3865","article-title":"Generalized gradient approximation made simple","volume":"77","author":"Perdew","year":"1996","journal-title":"Phys. Rev. Lett."},{"key":"mlstadfa68bib44","doi-asserted-by":"publisher","DOI":"10.1088\/1361-648X\/acd831","article-title":"Implementation strategies in phonopy and phono3py","volume":"35","author":"Togo","year":"2023","journal-title":"J. Phys.: Condens. Matter"},{"key":"mlstadfa68bib45","doi-asserted-by":"publisher","DOI":"10.7566\/JPSJ.92.012001","article-title":"First-principles phonon calculations with phonopy and phono3py","volume":"92","author":"Togo","year":"2023","journal-title":"J. Phys. Soc. Japan"},{"key":"mlstadfa68bib46","doi-asserted-by":"publisher","DOI":"10.1088\/1361-648X\/aa680e","article-title":"The atomic simulation environment-a python library for working with atoms","volume":"29","author":"Larsen","year":"2017","journal-title":"J. Phys.: Condens. Matter"},{"key":"mlstadfa68bib47","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1016\/j.commatsci.2012.10.028","article-title":"Python materials genomics (pymatgen): a robust, open-source python library for materials analysis","volume":"68","author":"Ong","year":"2013","journal-title":"Comput. Mater. Sci."}],"container-title":["Machine Learning: Science and Technology"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/adfa68","content-type":"text\/html","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/adfa68\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/adfa68","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/adfa68\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/adfa68\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/adfa68\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/adfa68\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/adfa68\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T12:56:44Z","timestamp":1755781004000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/adfa68"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,21]]},"references-count":47,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,8,21]]},"published-print":{"date-parts":[[2025,9,30]]}},"URL":"https:\/\/doi.org\/10.1088\/2632-2153\/adfa68","relation":{},"ISSN":["2632-2153"],"issn-type":[{"value":"2632-2153","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,21]]},"assertion":[{"value":"Benchmarking universal machine learning interatomic potentials for rapid analysis of inelastic neutron scattering data*","name":"article_title","label":"Article Title"},{"value":"Machine Learning: Science and Technology","name":"journal_title","label":"Journal Title"},{"value":"paper","name":"article_type","label":"Article Type"},{"value":"\u00a9 2025 The Author(s). Published by IOP Publishing Ltd","name":"copyright_information","label":"Copyright Information"},{"value":"2025-05-01","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2025-08-11","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2025-08-21","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}