{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T23:09:13Z","timestamp":1778368153828,"version":"3.51.4"},"publisher-location":"Cham","reference-count":48,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032191076","type":"print"},{"value":"9783032191083","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-19108-3_3","type":"book-chapter","created":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T22:18:48Z","timestamp":1778365128000},"page":"41-56","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Knowledge Distillation Framework for\u00a0Accelerating High-Accuracy Neural Network-Based Molecular Dynamics Simulations"],"prefix":"10.1007","author":[{"given":"Naoki","family":"Matsumura","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuta","family":"Yoshimoto","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuto","family":"Iwasaki","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meguru","family":"Yamazaki","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yasufumi","family":"Sakai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,5,1]]},"reference":[{"issue":"6","key":"3_CR1","doi-asserted-by":"publisher","first-page":"3098","DOI":"10.1103\/PhysRevA.38.3098","volume":"38","author":"AD Becke","year":"1988","unstructured":"Becke, A.D.: Density-functional exchange-energy approximation with correct asymptotic behavior. Phys. Rev. A 38(6), 3098\u20133100 (1988)","journal-title":"Phys. Rev. A"},{"issue":"14","key":"3_CR2","doi-asserted-by":"publisher","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(14), 146401 (2007)","journal-title":"Phys. Rev. Lett."},{"issue":"24","key":"3_CR3","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(24), 17953\u201317979 (1994)","journal-title":"Phys. Rev. B"},{"issue":"28","key":"3_CR4","doi-asserted-by":"publisher","first-page":"36878","DOI":"10.1021\/acsami.4c04491","volume":"16","author":"R Chahal","year":"2024","unstructured":"Chahal, R., Toomey, M.D., Kearney, L.T., et al.: Deep-learning interatomic potential connects molecular structural ordering to the macroscale properties of polyacrylonitrile. ACS Appl. Mater. Interfaces. 16(28), 36878\u201336891 (2024)","journal-title":"ACS Appl. Mater. Interfaces."},{"issue":"11","key":"3_CR5","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(11), 718\u2013728 (2022)","journal-title":"Nat. Comput. Sci."},{"key":"3_CR6","doi-asserted-by":"crossref","unstructured":"Deng, B., Choi, Y., Zhong, P., et\u00a0al.: Systematic softening in universal machine learning interatomic potentials. npj Comput. Mater. 11(1), 9 (2025)","DOI":"10.1038\/s41524-024-01500-6"},{"issue":"9","key":"3_CR7","doi-asserted-by":"publisher","first-page":"1031","DOI":"10.1038\/s42256-023-00716-3","volume":"5","author":"B Deng","year":"2023","unstructured":"Deng, B., Zhong, P., Jun, K., et al.: CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling. Nat. Mach. Intell. 5(9), 1031\u20131041 (2023)","journal-title":"Nat. Mach. Intell."},{"issue":"6","key":"3_CR8","doi-asserted-by":"publisher","first-page":"750","DOI":"10.1038\/s41563-020-0777-6","volume":"20","author":"P Friederich","year":"2021","unstructured":"Friederich, P., H\u00e4se, F., Proppe, J., Aspuru-Guzik, A.: Machine-learned potentials for next-generation matter simulations. Nat. Mater. 20(6), 750\u2013761 (2021)","journal-title":"Nat. Mater."},{"key":"3_CR9","doi-asserted-by":"crossref","unstructured":"Giannozzi, P., Baroni, S., Bonini, N., et\u00a0al.: QUANTUM ESPRESSO: a modular and open-source software project for quantum simulations of materials. J. Physics. Condens. Matter: Inst. Phys. J. 21(39), 395502 (2009)","DOI":"10.1088\/0953-8984\/21\/39\/395502"},{"issue":"15","key":"3_CR10","doi-asserted-by":"publisher","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-Pu. J. Chem. Phys. 132(15), 154104 (2010)","journal-title":"J. Chem. Phys."},{"key":"3_CR11","doi-asserted-by":"crossref","unstructured":"Gupta, M., Agrawal, P.: Compression of Deep Learning Models for Text: A Survey. ACM Trans. Knowl. Discov. Data 16(4), 61:1\u201361:55 (2022)","DOI":"10.1145\/3487045"},{"issue":"7","key":"3_CR12","doi-asserted-by":"publisher","first-page":"3641","DOI":"10.1103\/PhysRevB.58.3641","volume":"58","author":"C Hartwigsen","year":"1998","unstructured":"Hartwigsen, C., Goedecker, S., Hutter, J.: Relativistic separable dual-space Gaussian pseudopotentials from H to Rn. Phys. Rev. B 58(7), 3641\u20133662 (1998)","journal-title":"Phys. Rev. B"},{"key":"3_CR13","doi-asserted-by":"crossref","unstructured":"Hayashi, Y., Shiomi, J., Morikawa, J., Yoshida, R.: RadonPy: automated physical property calculation using all-atom classical molecular dynamics simulations for polymer informatics. npj Comput. Mater. 8(1), 1\u201315 (2022)","DOI":"10.1038\/s41524-022-00906-4"},{"key":"3_CR14","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the Knowledge in a Neural Network (2015), arXiv:1503.02531"},{"issue":"6","key":"3_CR15","doi-asserted-by":"publisher","first-page":"2480","DOI":"10.1021\/acs.jced.1c00101","volume":"66","author":"MM Hoffmann","year":"2021","unstructured":"Hoffmann, M.M., Horowitz, R.H., Gutmann, T., Buntkowsky, G.: Densities, viscosities, and self-diffusion coefficients of ethylene glycol oligomers. J. Chem. Eng. Data 66(6), 2480\u20132500 (2021)","journal-title":"J. Chem. Eng. Data"},{"key":"3_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.cossms.2025.101214","volume":"35","author":"R Jacobs","year":"2025","unstructured":"Jacobs, R., Morgan, D., Attarian, S., et al.: A practical guide to machine learning interatomic potentials \u2013 status and future. Curr. Opin. Solid State Mater. Sci. 35, 101214 (2025)","journal-title":"Curr. Opin. Solid State Mater. Sci."},{"issue":"1","key":"3_CR17","doi-asserted-by":"publisher","DOI":"10.1063\/1.4812323","volume":"1","author":"A Jain","year":"2013","unstructured":"Jain, A., Ong, S.P., Hautier, G., et al.: Commentary: the materials project: a materials genome approach to accelerating materials innovation. APL Mater. 1(1), 011002 (2013)","journal-title":"APL Mater."},{"key":"3_CR18","doi-asserted-by":"crossref","unstructured":"Jiao, X., Yin, Y., Shang, L., et\u00a0al.: TinyBERT: Distilling BERT for Natural Language Understanding (2019), arXiv:1909.10351","DOI":"10.18653\/v1\/2020.findings-emnlp.372"},{"key":"3_CR19","unstructured":"Kaplan, A.D., Liu, R., Qi, J., et\u00a0al.: A Foundational Potential Energy Surface Dataset for Materials (2025)"},{"key":"3_CR20","unstructured":"Kelvinius, F.E., Georgiev, D., Toshev, A.P., Gasteiger, J.: Accelerating molecular graph neural networks via knowledge distillation. In: Proceedings of the 37th International Conference on Neural Information Processing Systems. Curran Associates Inc. (2023)"},{"issue":"12","key":"3_CR21","doi-asserted-by":"publisher","first-page":"3548","DOI":"10.1039\/c3ee41728j","volume":"6","author":"A Kuhn","year":"2013","unstructured":"Kuhn, A., Duppel, V., Lotsch, B.V.: Tetragonal Li10GeP2S12 and Li7GePS8 - exploring the Li ion dynamics in LGPS Li electrolytes. Energy Environ. Sci. 6(12), 3548\u20133552 (2013)","journal-title":"Energy Environ. Sci."},{"issue":"6","key":"3_CR22","doi-asserted-by":"publisher","first-page":"2898","DOI":"10.1021\/acs.chemmater.3c03261","volume":"36","author":"D Liu","year":"2024","unstructured":"Liu, D., Wu, Y., Samatov, M.R., et al.: Compression eliminates charge traps by stabilizing perovskite grain boundary structures: an ab initio analysis with machine learning force field. Chem. Mater. 36(6), 2898\u20132906 (2024)","journal-title":"Chem. Mater."},{"issue":"7","key":"3_CR23","doi-asserted-by":"publisher","first-page":"4291","DOI":"10.1021\/acs.jctc.1c00302","volume":"17","author":"C Lu","year":"2021","unstructured":"Lu, C., Wu, C., Ghoreishi, D., et al.: OPLS4: improving force field accuracy on challenging regimes of chemical space. J. Chem. Theory Comput. 17(7), 4291\u20134300 (2021)","journal-title":"J. Chem. Theory Comput."},{"issue":"8","key":"3_CR24","doi-asserted-by":"publisher","first-page":"3832","DOI":"10.1021\/acs.jctc.4c01613","volume":"21","author":"N Matsumura","year":"2025","unstructured":"Matsumura, N., Yoshimoto, Y., Yamazaki, T., et al.: Generator of neural network potential for molecular dynamics: constructing robust and accurate potentials with active learning for nanosecond-scale simulations. J. Chem. Theory Comput. 21(8), 3832\u20133846 (2025)","journal-title":"J. Chem. Theory Comput."},{"key":"3_CR25","doi-asserted-by":"crossref","unstructured":"Mo, Y., Ong, S.P., Ceder, G.: First Principles Study of the Li $$_{\\rm 10}$$ GeP $$_{\\rm 2}$$ S $$_{\\rm 12}$$ Lithium Super Ionic Conductor Material. Chem. Mater. 24(1), 15\u201317 (2012)","DOI":"10.1021\/cm203303y"},{"issue":"1","key":"3_CR26","doi-asserted-by":"publisher","first-page":"17251","DOI":"10.1038\/s41598-023-43804-5","volume":"13","author":"S Mohanty","year":"2023","unstructured":"Mohanty, S., Stevenson, J., Browning, A.R., et al.: Development of scalable and generalizable machine learned force field for polymers. Sci. Rep. 13(1), 17251 (2023)","journal-title":"Sci. Rep."},{"issue":"6","key":"3_CR27","doi-asserted-by":"publisher","first-page":"765","DOI":"10.1038\/s41587-020-00801-7","volume":"39","author":"A Narayan","year":"2021","unstructured":"Narayan, A., Berger, B., Cho, H.: Assessing single-cell transcriptomic variability through density-preserving data visualization. Nat. Biotechnol. 39(6), 765\u2013774 (2021)","journal-title":"Nat. Biotechnol."},{"issue":"18","key":"3_CR28","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(18), 3865\u20133868 (1996)","journal-title":"Phys. Rev. Lett."},{"key":"3_CR29","doi-asserted-by":"crossref","unstructured":"Qi, J., Ko, T.W., Wood, B.C., et\u00a0al.: Robust training of machine learning interatomic potentials with dimensionality reduction and stratified sampling. npj Comput. Mater. 10(1), 43 (2024)","DOI":"10.1038\/s41524-024-01227-4"},{"key":"3_CR30","unstructured":"Romero, A., Ballas, N., Kahou, S.E., et\u00a0al.: FitNets: Hints for Thin Deep Nets (2014), arXiv:1412.6550"},{"key":"3_CR31","unstructured":"Sanh, V., Debut, L., Chaumond, J., Wolf, T.: DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter (2019), arXiv:1910.01108"},{"key":"3_CR32","doi-asserted-by":"crossref","unstructured":"Saputra, M.R.U., Gusmao, P., Almalioglu, Y., et\u00a0al.: Distilling Knowledge From a Deep Pose Regressor Network. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 263\u2013272. IEEE (2019)","DOI":"10.1109\/ICCV.2019.00035"},{"issue":"22","key":"3_CR33","doi-asserted-by":"publisher","first-page":"2210788","DOI":"10.1002\/adma.202210788","volume":"35","author":"J Schmidt","year":"2023","unstructured":"Schmidt, J., Hoffmann, N., Wang, H.C., et al.: Machine-learning-assisted determination of the global zero-temperature phase diagram of materials. Adv. Mater. 35(22), 2210788 (2023)","journal-title":"Adv. Mater."},{"key":"3_CR34","unstructured":"Sch\u00fctt, K., Unke, O., Gastegger, M.: Equivariant message passing for the prediction of tensorial properties and molecular spectra. In: Proceedings of the 38th International Conference on Machine Learning, pp. 9377\u20139388. PMLR (2021)"},{"issue":"16","key":"3_CR35","doi-asserted-by":"publisher","first-page":"10142","DOI":"10.1021\/acs.chemrev.0c01111","volume":"121","author":"OT Unke","year":"2021","unstructured":"Unke, O.T., Chmiela, S., Sauceda, H.E., et al.: Machine learning force fields. Chem. Rev. 121(16), 10142\u201310186 (2021)","journal-title":"Chem. Rev."},{"key":"3_CR36","doi-asserted-by":"crossref","unstructured":"Wang, H., Zhang, L., Han, J., E, W.: DeePMD-kit: a deep learning package for many-body potential energy representation and molecular dynamics. Comput. Phys. Commun. 228, 178\u2013184 (2018)","DOI":"10.1016\/j.cpc.2018.03.016"},{"issue":"6","key":"3_CR37","doi-asserted-by":"publisher","first-page":"3048","DOI":"10.1109\/TPAMI.2021.3055564","volume":"44","author":"L Wang","year":"2022","unstructured":"Wang, L., Yoon, K.J.: Knowledge distillation and student-teacher learning for visual intelligence: a review and new outlooks. IEEE Trans. Pattern Anal. Mach. Intell. 44(6), 3048\u20133068 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3_CR38","doi-asserted-by":"crossref","unstructured":"Wang, R., Gao, Y., Wu, H., Zhong, Z.: PFD: Automatically Generating Machine Learning Force Fields from Universal Models (2025), arXiv:2502.20809","DOI":"10.1103\/sbz6-btz8"},{"issue":"6","key":"3_CR39","doi-asserted-by":"publisher","first-page":"2105","DOI":"10.1021\/acsmaterialslett.5c00093","volume":"7","author":"D Wines","year":"2025","unstructured":"Wines, D., Choudhary, K.: CHIPS-FF: evaluating universal machine learning force fields for material properties. ACS Mater. Lett. 7(6), 2105\u20132114 (2025)","journal-title":"ACS Mater. Lett."},{"issue":"2","key":"3_CR40","doi-asserted-by":"crossref","DOI":"10.1088\/0305-4616\/11\/2\/014","volume":"5","author":"G Winter","year":"2023","unstructured":"Winter, G., G\u00f3mez-Bombarelli, R.: Simulations with machine learning potentials identify the ion conduction mechanism mediating non-Arrhenius behavior in LGPS. J. Phys.: Energy 5(2), 024004 (2023)","journal-title":"J. Phys.: Energy"},{"key":"3_CR41","doi-asserted-by":"crossref","unstructured":"Yang, C., Zhu, Y., Lu, W., et al.: Survey on knowledge distillation for large language models: methods, evaluation, and application. ACM Trans. Intell. Syst. Technol. (2024)","DOI":"10.1145\/3699518"},{"key":"3_CR42","unstructured":"Yang, H., Hu, C., Zhou, Y., et\u00a0al.: MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures (2024), arXiv:2405.04967"},{"key":"3_CR43","doi-asserted-by":"crossref","unstructured":"Yim, J., Joo, D., Bae, J., Kim, J.: A gift from knowledge distillation: fast optimization, network minimization and transfer learning. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7130\u20137138. IEEE (2017)","DOI":"10.1109\/CVPR.2017.754"},{"key":"3_CR44","unstructured":"Yoshimoto, Y., Matsumura, N., Iwasaki, Y., et\u00a0al.: Large-Scale, Long-Time Atomistic Simulations of Proton Transport in Polymer Electrolyte Membranes Using a Neural Network Interatomic Potential (2025), arXiv:2503.20412"},{"issue":"8055","key":"3_CR45","doi-asserted-by":"publisher","first-page":"624","DOI":"10.1038\/s41586-025-08628-5","volume":"639","author":"C Zeni","year":"2025","unstructured":"Zeni, C., Pinsler, R., Z\u00fcgner, D., et al.: A generative model for inorganic materials design. Nature 639(8055), 624\u2013632 (2025)","journal-title":"Nature"},{"key":"3_CR46","doi-asserted-by":"crossref","unstructured":"Zhang, D., Liu, X., Zhang, X., et\u00a0al.: DPA-2: a large atomic model as a multi-task learner. npj Comput. Mater. 10(1), 293 (2024)","DOI":"10.1038\/s41524-024-01493-2"},{"key":"3_CR47","unstructured":"Zhang, L., Han, J., Wang, H., et\u00a0al.: End-to-end symmetry preserving inter-atomic potential energy model for finite and extended systems. In: Advances in Neural Information Processing Systems, vol.\u00a031. Curran Associates, Inc. (2018)"},{"key":"3_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.cpc.2020.107206","volume":"253","author":"Y Zhang","year":"2020","unstructured":"Zhang, Y., Wang, H., Chen, W., et al.: DP-GEN: a concurrent learning platform for the generation of reliable deep learning based potential energy models. Comput. Phys. Commun. 253, 107206 (2020)","journal-title":"Comput. Phys. Commun."}],"container-title":["Communications in Computer and Information Science","Machine Learning and Principles and Practice of Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-19108-3_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T22:18:51Z","timestamp":1778365131000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-19108-3_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032191076","9783032191083"],"references-count":48,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-19108-3_3","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"1 May 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Porto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecmlpkdd.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}