{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T22:09:57Z","timestamp":1778364597785,"version":"3.51.4"},"publisher-location":"Cham","reference-count":35,"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_4","type":"book-chapter","created":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T22:04:56Z","timestamp":1778364296000},"page":"57-71","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Improving Neural Network-Based Material Simulations with\u00a0Domain-Specific Data Filtering and\u00a0Atom-Specific Training"],"prefix":"10.1007","author":[{"given":"Meguru","family":"Yamazaki","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuta","family":"Yoshimoto","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Isshin","family":"Gosha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Taku","family":"Fujisawa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tomoya","family":"Matsuda","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Naoki","family":"Matsumura","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuto","family":"Iwasaki","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Atsuki","family":"Inoue","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tomohisa","family":"Yoshioka","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hiroshi","family":"Kawaguchi","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":[{"key":"4_CR1","unstructured":"Barroso-Luque, L., et al.: Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models (2024). arXiv: 2410.12771. https:\/\/arxiv.org\/abs\/2410.12771"},{"key":"4_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, 146401 (2007). https:\/\/doi.org\/10.1103\/PhysRevLett.98.146401","journal-title":"Phys. Rev. Lett."},{"key":"4_CR3","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevMaterials.4.113803","volume":"4","author":"MFC Andrade","year":"2020","unstructured":"Andrade, M.F.C., Selloni, A.: Structure of disordered TiO2 phases from ab initio based deep neural network simulations. Phys. Rev. Mater. 4, 113803 (2020). https:\/\/doi.org\/10.1103\/PhysRevMaterials.4.113803","journal-title":"Phys. Rev. Mater."},{"key":"4_CR4","doi-asserted-by":"publisher","unstructured":"David, R., et al.: ArcaNN: automated enhanced sampling generation of training sets for chemically reactive machine learning interatomic potentials. Digit. Discov. 4 (2025). Open Access Article licensed under a Creative Commons Attribution-Non Commercial 3.0 Unported Licence, pp. 54\u201372. https:\/\/doi.org\/10.1039\/D4DD00209A. https:\/\/pubs.rsc.org\/en\/content\/articlepdf\/2025\/dd\/d4dd00209a","DOI":"10.1039\/D4DD00209A"},{"key":"4_CR5","unstructured":"DeepModeling. DeepMD-kit Documentation. https:\/\/docs.deepmodeling.com\/projects\/deepmd\/en\/stable\/index.html"},{"key":"4_CR6","unstructured":"Deng, B., et al.: Overcoming systematic softening in universal machine learning interatomic potentials by fine-tuning (2024). arXiv: 2405.07105. https:\/\/arxiv.org\/abs\/2405.07105"},{"issue":"1","key":"4_CR7","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1038\/s41524-022-00768-w","volume":"8","author":"LC Erhard","year":"2022","unstructured":"Erhard, L.C., et al.: A machine-learned interatomic potential for silica and its relation to empirical models. NPJ Comput. Mater. 8(1), 90 (2022). https:\/\/doi.org\/10.1038\/s41524-022-00768-w. ISSN: 2057-3960","journal-title":"NPJ Comput. Mater."},{"key":"4_CR8","unstructured":"Fu, X., et al.: Forces are not enough: benchmark and critical evaluation for machine learning force fields with molecular simulations. In: TMLR (2023). Last Modified: 29 Oct 2024, Accepted by TMLR. https:\/\/github.com\/kyonofx\/MDsim"},{"key":"4_CR9","doi-asserted-by":"publisher","unstructured":"Fujiki, T., et al.: Pervaporation dehydration of an isopropanol aqueous solution using microporous TiO2-SiO2-OCL (Organic chelating Ligand) composite membranes prepared under different firing conditions. Sep. Purif. Technol. 337, 126249 (2024). ISSN: 1383-5866. https:\/\/doi.org\/10.1016\/j.seppur.2023.126249. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S138358662303157X","DOI":"10.1016\/j.seppur.2023.126249"},{"key":"4_CR10","doi-asserted-by":"crossref","unstructured":"Guo, C., Zhao, B., Bai, Y.: DeepCore: A Comprehensive Library for Coreset Selection in Deep Learning (2022). arXiv:2204.08499. https:\/\/arxiv.org\/abs\/2204.08499","DOI":"10.1007\/978-3-031-12423-5_14"},{"key":"4_CR11","doi-asserted-by":"publisher","unstructured":"Karki, B.B.: First-principles molecular dynamics simulations of silicate melts: structural and dynamical properties. Rev. Mineral. Geochem. 71(1), 355\u2013389 (2010). ISSN: 1529-6466. https:\/\/doi.org\/10.2138\/rmg.2010.71.17. https:\/\/pubs.geoscienceworld.org\/msa\/rimg\/article-pdf\/71\/1\/355\/2950619\/355_Karki.pdf","DOI":"10.2138\/rmg.2010.71.17"},{"key":"4_CR12","doi-asserted-by":"publisher","unstructured":"Kov\u00e1cs, D.P., et al.: MACE-OFF: short-range transferable machine learning force fields for organic molecules. J. Am. Chem. Soc. PMID: 40387214. https:\/\/doi.org\/10.1021\/jacs.4c07099","DOI":"10.1021\/jacs.4c07099"},{"key":"4_CR13","unstructured":"Liao, Y.-L., et al.: EquiformerV2: improved equivariant transformer for scaling to higher-degree representations. In: International Conference on Learning Representations (ICLR) (2024). https:\/\/iclr.cc\/Conferences\/2024\/AuthorGuide"},{"key":"4_CR14","doi-asserted-by":"publisher","unstructured":"Makiewicz, A., Ratajczak, W.: Principal components analysis (PCA). Comput. Geosci. 19(3), 303\u2013342 (1993). ISSN: 0098-3004. https:\/\/doi.org\/10.1016\/0098-3004(93)90090-R. https:\/\/www.sciencedirect.com\/science\/article\/pii\/009830049390090R","DOI":"10.1016\/0098-3004(93)90090-R"},{"key":"4_CR15","doi-asserted-by":"publisher","unstructured":"Matsumura, N., 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). PMID: 40195003. https:\/\/doi.org\/10.1021\/acs.jctc.4c01613","DOI":"10.1021\/acs.jctc.4c01613"},{"key":"4_CR16","doi-asserted-by":"publisher","unstructured":"Narayan, A., Berger, B., Cho, H.: Assessing singlecell transcriptomic variability through density-preserving data visualization. Nat. Biotechnol. 39(6), 765\u2013774 (2021). Epub 2021 Jan 18, ISSN: 1546-1696. https:\/\/doi.org\/10.1038\/s41587-020-00801-7","DOI":"10.1038\/s41587-020-00801-7"},{"key":"4_CR17","unstructured":"Neumann, M., et al.: Orb: A Fast, Scalable Neural Network Potential (2024). arXiv:2410.22570. https:\/\/arxiv.org\/abs\/2410.22570"},{"key":"4_CR18","doi-asserted-by":"publisher","unstructured":"Nos\u00e9, S.: A unified formulation of the constant temperature molecular dynamics methods. J. Chem. Phys. 81(1), 511\u2013519 (1984). ISSN: 0021-9606. https:\/\/doi.org\/10.1063\/1.447334. https:\/\/pubs.aip.org\/aip\/jcp\/article-pdf\/81\/1\/511\/18949062\/511_1_online.pdf","DOI":"10.1063\/1.447334"},{"key":"4_CR19","unstructured":"Oyama, Y., et al.: LaMM: Semi-Supervised Pre-Training of Large-Scale Materials Models (2025). arXiv:2505.22208. https:\/\/arxiv.org\/abs\/2505.22208"},{"issue":"25","key":"4_CR20","doi-asserted-by":"publisher","first-page":"10024","DOI":"10.1021\/ja00051a040","volume":"114","author":"AK Rappe","year":"1992","unstructured":"Rappe, A.K., et al.: UFF, a full periodic table force field for molecular mechanics and molecular dynamics simulations. J. Am. Chem. Soc. 114(25), 10024\u201310035 (1992). https:\/\/doi.org\/10.1021\/ja00051a040","journal-title":"J. Am. Chem. Soc."},{"key":"4_CR21","doi-asserted-by":"publisher","unstructured":"Savin, A.V., Mazo, M.A.: The COMPASS force field: validation for carbon nanoribbons. Physica E: Low-Dimensional Syst. Nanostruct. 118, 113937 (2020). ISSN: 1386-9477. https:\/\/doi.org\/10.1016\/j.physe.2019.113937. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S138694771930342X","DOI":"10.1016\/j.physe.2019.113937"},{"key":"4_CR22","unstructured":"Sch\u00fctt, K.T., Unke, O.T., Gastegger, M.: Equivariant message passing for the prediction of tensorial properties and molecular spectra (2021). arXiv:2102.03150. https:\/\/arxiv.org\/abs\/2102.03150"},{"key":"4_CR23","doi-asserted-by":"publisher","unstructured":"Seung, H.S., Opper, M., Sompolinsky, H.: Query by committee. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory. COLT 1992. Pittsburgh, Pennsylvania, USA, pp. 287\u2013294. Association for Computing Machinery (1992). ISBN: 089791497X. https:\/\/doi.org\/10.1145\/130385.130417","DOI":"10.1145\/130385.130417"},{"key":"4_CR24","doi-asserted-by":"publisher","unstructured":"Shang, Z., Li, H.: Unraveling pyrolysis mechanisms of lignin dimer model compounds: neural network-based molecular dynamics simulation investigations. Fuel 357, 129909 (2024). ISSN: 0016-2361. https:\/\/doi.org\/10.1016\/j.fuel.2023.129909. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0016236123025231","DOI":"10.1016\/j.fuel.2023.129909"},{"key":"4_CR25","doi-asserted-by":"publisher","unstructured":"Sun, H., Ren, P., Fried, J.R.: The COMPASS force field: parameterization and validation for phosphazenes. Comput. Theor. Polym. Sci. 8(1), 229\u2013246 (1998). ISSN: 1089-3156. https:\/\/doi.org\/10.1016\/S1089-3156(98)00042-7. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1089315698000427","DOI":"10.1016\/S1089-3156(98)00042-7"},{"key":"4_CR26","doi-asserted-by":"publisher","unstructured":"Tachi, E., et al.: The effects of firing temperature and Ti\/Si ratio on the H2 permeation characteristics of microporous TiO2-SiO2-organic chelating-ligand composite membranes. Sep. Purif. Technol. 322, 124091 (2023). ISSN: 1383-5866. https:\/\/doi.org\/10.1016\/j.seppur.2023.124091. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1383586623009991","DOI":"10.1016\/j.seppur.2023.124091"},{"issue":"40","key":"4_CR27","doi-asserted-by":"publisher","first-page":"21840","DOI":"10.1021\/acs.jpcc.1c06247","volume":"125","author":"T Tsuchimochi","year":"2021","unstructured":"Tsuchimochi, T., et al.: First-principles investigation on the heterostructure photocatalyst comprising BiVO4 and few-layer black phosphorus. J. Phys. Chem. C 125(40), 21840\u201321850 (2021). https:\/\/doi.org\/10.1021\/acs.jpcc.1c06247","journal-title":"J. Phys. Chem. C"},{"key":"4_CR28","doi-asserted-by":"publisher","unstructured":"Wang, H, et al.: DeePMD-kit: a deep learning package for many-body potential energy representation and molecular dynamics. Comput. Phys. Commun. 228, 178\u2013184 (2018). ISSN: 0010-4655. https:\/\/doi.org\/10.1016\/j.cpc.2018.03.016. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0010465518300882","DOI":"10.1016\/j.cpc.2018.03.016"},{"issue":"9","key":"4_CR29","doi-asserted-by":"publisher","first-page":"1157","DOI":"10.1002\/jcc.20035","volume":"25","author":"J Wang","year":"2004","unstructured":"Wang, J., et al.: Development and testing of a general amber force field. J. Comput. Chem. 25(9), 1157\u20131174 (2004). https:\/\/doi.org\/10.1002\/jcc.20035","journal-title":"J. Comput. Chem."},{"key":"4_CR30","doi-asserted-by":"publisher","unstructured":"Xu, L., et al.: Data efficient and stability indicated sampling for developing reactive machine learning potential to achieve ultra-long simulation in lithium metal batteries. ChemRxiv (2023). This content is a preprint and has not been peer-reviewed. https:\/\/doi.org\/10.26434\/chemrxiv-2023-4x3gr","DOI":"10.26434\/chemrxiv-2023-4x3gr"},{"key":"4_CR31","unstructured":"Yang, H., et al.: MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures (2024). arXiv:2405.04967. https:\/\/arxiv.org\/abs\/2405.04967"},{"key":"4_CR32","unstructured":"Yoshimoto, Y., et al.: Large-Scale, Long-Time Atomistic Simulations of Proton Transport in Polymer Electrolyte Membranes Using a Neural Network Interatomic Potential (2025). arXiv:2503.20412. https:\/\/arxiv.org\/abs\/2503.20412"},{"key":"4_CR33","doi-asserted-by":"publisher","unstructured":"Yoshioka, T., et al.: Molecular dynamics simulation study of polyamide membrane structures and RO\/FOWater permeation properties. Membranes 8(4) (2018). ISSN: 2077-0375. https:\/\/doi.org\/10.3390\/membranes8040127. https:\/\/www.mdpi.com\/2077-0375\/8\/4\/127","DOI":"10.3390\/membranes8040127"},{"key":"4_CR34","doi-asserted-by":"publisher","unstructured":"Yoshioka, T., et al.: Molecular dynamics simulation study of solid vibration permeation in microporous amorphous silica network voids. Membranes 9(10) (2019). ISSN: 2077-0375. https:\/\/doi.org\/10.3390\/membranes9100132. https:\/\/www.mdpi.com\/2077-0375\/9\/10\/132","DOI":"10.3390\/membranes9100132"},{"key":"4_CR35","doi-asserted-by":"publisher","unstructured":"Zhang, Y., 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). ISSN: 0010-4655. https:\/\/doi.org\/10.1016\/j.cpc.2020.107206. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S001046552030045X","DOI":"10.1016\/j.cpc.2020.107206"}],"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_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T22:05:01Z","timestamp":1778364301000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-19108-3_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032191076","9783032191083"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-19108-3_4","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"}}]}}