Benchmarking universal machine learning interatomic potentials for rapid analysis of inelastic neutron scattering data
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 subst...
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IOP Publishing
2025-01-01
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| Series: | Machine Learning: Science and Technology |
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| Online Access: | https://doi.org/10.1088/2632-2153/adfa68 |
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| author | Bowen Han Yongqiang Cheng |
| author_facet | Bowen Han Yongqiang Cheng |
| author_sort | Bowen Han |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-709ba751a85e4e23aa45b6625df9d065 |
| institution | Kabale University |
| issn | 2632-2153 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Machine Learning: Science and Technology |
| spelling | doaj-art-709ba751a85e4e23aa45b6625df9d0652025-08-21T12:56:36ZengIOP PublishingMachine Learning: Science and Technology2632-21532025-01-016303050410.1088/2632-2153/adfa68Benchmarking universal machine learning interatomic potentials for rapid analysis of inelastic neutron scattering dataBowen Han0https://orcid.org/0000-0002-1979-2402Yongqiang Cheng1https://orcid.org/0000-0002-3263-4812Neutron Scattering Division, Oak Ridge National Laboratory , Oak Ridge, TN 37830, United States of AmericaNeutron Scattering Division, Oak Ridge National Laboratory , Oak Ridge, TN 37830, United States of AmericaThe 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.https://doi.org/10.1088/2632-2153/adfa68phononinelastic neutron scatteringmachine learninginteratomic potential |
| spellingShingle | Bowen Han Yongqiang Cheng Benchmarking universal machine learning interatomic potentials for rapid analysis of inelastic neutron scattering data Machine Learning: Science and Technology phonon inelastic neutron scattering machine learning interatomic potential |
| title | Benchmarking universal machine learning interatomic potentials for rapid analysis of inelastic neutron scattering data |
| title_full | Benchmarking universal machine learning interatomic potentials for rapid analysis of inelastic neutron scattering data |
| title_fullStr | Benchmarking universal machine learning interatomic potentials for rapid analysis of inelastic neutron scattering data |
| title_full_unstemmed | Benchmarking universal machine learning interatomic potentials for rapid analysis of inelastic neutron scattering data |
| title_short | Benchmarking universal machine learning interatomic potentials for rapid analysis of inelastic neutron scattering data |
| title_sort | benchmarking universal machine learning interatomic potentials for rapid analysis of inelastic neutron scattering data |
| topic | phonon inelastic neutron scattering machine learning interatomic potential |
| url | https://doi.org/10.1088/2632-2153/adfa68 |
| work_keys_str_mv | AT bowenhan benchmarkinguniversalmachinelearninginteratomicpotentialsforrapidanalysisofinelasticneutronscatteringdata AT yongqiangcheng benchmarkinguniversalmachinelearninginteratomicpotentialsforrapidanalysisofinelasticneutronscatteringdata |