{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T19:39:27Z","timestamp":1775763567507,"version":"3.50.1"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030787127","type":"print"},{"value":"9783030787134","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-78713-4_13","type":"book-chapter","created":{"date-parts":[[2021,6,16]],"date-time":"2021-06-16T23:06:15Z","timestamp":1623884775000},"page":"237-254","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Enabling AI-Accelerated Multiscale Modeling of Thrombogenesis at Millisecond and Molecular Resolutions on Supercomputers"],"prefix":"10.1007","author":[{"given":"Yicong","family":"Zhu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Changnian","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guojing","family":"Cong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuefan","family":"Deng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,6,17]]},"reference":[{"key":"13_CR1","doi-asserted-by":"publisher","unstructured":"Hodak, H.: The nobel prize in chemistry 2013 for the development of multiscale models of complex chemical systems: a tribute to Martin Karplus, Michael Levitt and Arieh Warshel. J. Mol. Biol. 426(1), 1\u20133 (2014). https:\/\/doi.org\/10.1016\/j.jmb.2013.10.037. ISSN 0022-2836","DOI":"10.1016\/j.jmb.2013.10.037"},{"key":"13_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41746-019-0193-y","volume":"2","author":"M Alber","year":"2019","unstructured":"Alber, M., et al.: Integrating machine learning and multiscale modeling\u2014perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences. NPJ. Digit. Med. 2, 1\u201311 (2019)","journal-title":"NPJ. Digit. Med."},{"key":"13_CR3","unstructured":"Virani, S.S., et al.: Heart disease and stroke statistics\u20142020 update: a report from the American Heart Association. Circulation E139-E596 (2020)"},{"key":"13_CR4","first-page":"501","volume":"13","author":"D Bluestein","year":"2004","unstructured":"Bluestein, D., Yin, W., Affeld, K., Jesty, J.: Flow-induced platelet activation in a mechanical heart valve. J. Heart Valve Dis. 13, 501\u2013508 (2004)","journal-title":"J. Heart Valve Dis."},{"key":"13_CR5","doi-asserted-by":"crossref","unstructured":"Poor, H.D., et al.: COVID\u201019 critical illness pathophysiology driven by diffuse pulmonary thrombi and pulmonary endothelial dysfunction responsive to thrombolysis. Clin. Transl. Med. 10, e44 (2020)","DOI":"10.1002\/ctm2.44"},{"key":"13_CR6","doi-asserted-by":"publisher","first-page":"100434","DOI":"10.1016\/j.eclinm.2020.100434","volume":"24","author":"AV Rapkiewicz","year":"2020","unstructured":"Rapkiewicz, A.V., et al.: Megakaryocytes and platelet-fibrin thrombi characterize multi-organ thrombosis at autopsy in COVID-19: a case series. EClinicalMedicine 24, 100434 (2020)","journal-title":"EClinicalMedicine"},{"key":"13_CR7","doi-asserted-by":"publisher","first-page":"2345","DOI":"10.1007\/s10439-012-0558-8","volume":"40","author":"W Wang","year":"2012","unstructured":"Wang, W., King, M.R.: Multiscale modeling of platelet adhesion and thrombus growth. Ann. Biomed. Eng. 40, 2345\u20132354 (2012)","journal-title":"Ann. Biomed. Eng."},{"key":"13_CR8","doi-asserted-by":"publisher","first-page":"552","DOI":"10.1007\/s12195-014-0356-5","volume":"7","author":"P Zhang","year":"2014","unstructured":"Zhang, P., Gao, C., Zhang, N., Slepian, M.J., Deng, Y., Bluestein, D.: Multiscale particle-based modeling of flowing platelets in blood plasma using dissipative particle dynamics and coarse grained molecular dynamics. Cell. Mol. Bioeng. 7, 552\u2013574 (2014)","journal-title":"Cell. Mol. Bioeng."},{"key":"13_CR9","doi-asserted-by":"publisher","first-page":"110053","DOI":"10.1016\/j.jcp.2020.110053","volume":"427","author":"C Han","year":"2021","unstructured":"Han, C., Zhang, P., Bluestein, D., Cong, G., Deng, Y.: Artificial intelligence for accelerating time integrations in multiscale modeling. J. Comput. Phys. 427, 110053 (2021)","journal-title":"J. Comput. Phys."},{"key":"13_CR10","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1146\/annurev-biophys-042910-155245","volume":"41","author":"RO Dror","year":"2012","unstructured":"Dror, R.O., Dirks, R.M., Grossman, J., Xu, H., Shaw, D.E.: Biomolecular simulation: a computational microscope for molecular biology. Annu. Rev. Biophys. 41, 429\u2013452 (2012)","journal-title":"Annu. Rev. Biophys."},{"key":"13_CR11","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1145\/1364782.1364802","volume":"51","author":"DE Shaw","year":"2008","unstructured":"Shaw, D.E., et al.: Anton, a special-purpose machine for molecular dynamics simulation. Commun. ACM 51, 91\u201397 (2008)","journal-title":"Commun. ACM"},{"key":"13_CR12","unstructured":"Shaw, D.E., et al.: Anton 2: raising the bar for performance and programmability in a special-purpose molecular dynamics supercomputer. In: SC 2014: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 41\u201353 (2014)"},{"key":"13_CR13","doi-asserted-by":"crossref","unstructured":"Yang, C., et al.: Fully integrated FPGA molecular dynamics simulations. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 1\u201331 (2019)","DOI":"10.1145\/3295500.3356179"},{"key":"13_CR14","doi-asserted-by":"crossref","unstructured":"Zhang, T.: SW_GROMACS: accelerate GROMACS on sunway TaihuLight. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 1\u201314 (2019)","DOI":"10.1145\/3295500.3356190"},{"key":"13_CR15","doi-asserted-by":"crossref","unstructured":"Jia, W., et al.: Pushing the limit of molecular dynamics with ab initio accuracy to 100 million atoms with machine learning. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 1\u201314 (2020)","DOI":"10.1109\/SC41405.2020.00009"},{"key":"13_CR16","doi-asserted-by":"publisher","first-page":"5087","DOI":"10.1182\/blood-2006-12-027698","volume":"109","author":"SP Jackson","year":"2007","unstructured":"Jackson, S.P.: The growing complexity of platelet aggregation. Blood 109, 5087\u20135095 (2007)","journal-title":"Blood"},{"key":"13_CR17","doi-asserted-by":"publisher","first-page":"2087","DOI":"10.1016\/j.cma.2007.06.030","volume":"197","author":"AL Fogelson","year":"2008","unstructured":"Fogelson, A.L., Guy, R.D.: Immersed-boundary-type models of intravascular platelet aggregation. Comput. Methods Appl. Mech. Eng. 197, 2087\u20132104 (2008)","journal-title":"Comput. Methods Appl. Mech. Eng."},{"key":"13_CR18","doi-asserted-by":"publisher","first-page":"1760","DOI":"10.1098\/rsif.2011.0180","volume":"8","author":"CR Sweet","year":"2011","unstructured":"Sweet, C.R., Chatterjee, S., Xu, Z., Bisordi, K., Rosen, E.D., Alber, M.: Modelling platelet\u2013blood flow interaction using the subcellular element Langevin method. J. R. Soc. Interface 8, 1760\u20131771 (2011)","journal-title":"J. R. Soc. Interface"},{"key":"13_CR19","doi-asserted-by":"crossref","unstructured":"Grinberg, L., et al.: A new computational paradigm in multiscale simulations: application to brain blood flow. In: Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 1\u20135 (2011)","DOI":"10.1145\/2063384.2063390"},{"key":"13_CR20","doi-asserted-by":"publisher","first-page":"20130380","DOI":"10.1098\/rsta.2013.0380","volume":"372","author":"Z Wu","year":"2014","unstructured":"Wu, Z., Xu, Z., Kim, O., Alber, M.: Three-dimensional multi-scale model of deformable platelets adhesion to vessel wall in blood flow. Philos. Trans. Royal Soc. A Math. Phys. Eng. Sci. 372, 20130380 (2014)","journal-title":"Philos. Trans. Royal Soc. A Math. Phys. Eng. Sci."},{"key":"13_CR21","doi-asserted-by":"crossref","unstructured":"Mody, N.A., King, M.R.: Platelet adhesive dynamics. Part I: characterization of platelet hydrodynamic collisions and wall effects. Biophys. J. 95, 2539\u20132555 (2008)","DOI":"10.1529\/biophysj.107.127670"},{"key":"13_CR22","doi-asserted-by":"crossref","unstructured":"Mody, N.A., King, M.R.: Platelet adhesive dynamics. Part II: high shear-induced transient aggregation via GPIb\u03b1-vWF-GPIb\u03b1 bridging. Biophys. J. 95, 2556\u20132574 (2008)","DOI":"10.1529\/biophysj.107.128520"},{"key":"13_CR23","doi-asserted-by":"crossref","unstructured":"Shiozaki, S., Takagi, S., Goto, S.: Prediction of molecular interaction between platelet glycoprotein Ib\u03b1 and von Willebrand factor using molecular dynamics simulations. J. Atheroscl. Thrombosis 32458 (2015)","DOI":"10.5551\/jat.32458"},{"key":"13_CR24","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.jbiomech.2016.11.019","volume":"50","author":"P Zhang","year":"2017","unstructured":"Zhang, P., Zhang, L., Slepian, M.J., Deng, Y., Bluestein, D.: A multiscale biomechanical model of platelets: Correlating with in-vitro results. J. Biomech. 50, 26\u201333 (2017)","journal-title":"J. Biomech."},{"key":"13_CR25","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1007\/s12195-019-00583-2","volume":"12","author":"P Gupta","year":"2019","unstructured":"Gupta, P., Zhang, P., Sheriff, J., Bluestein, D., Deng, Y.: A multiscale model for recruitment aggregation of platelets by correlating with in vitro results. Cell. Mol. Bioeng. 12, 327\u2013343 (2019)","journal-title":"Cell. Mol. Bioeng."},{"key":"13_CR26","doi-asserted-by":"publisher","first-page":"668","DOI":"10.1016\/j.jcp.2015.01.004","volume":"284","author":"P Zhang","year":"2015","unstructured":"Zhang, P., Zhang, N., Deng, Y., Bluestein, D.: A multiple time stepping algorithm for efficient multiscale modeling of platelets flowing in blood plasma. J. Comput. Phys. 284, 668\u2013686 (2015)","journal-title":"J. Comput. Phys."},{"key":"13_CR27","unstructured":"Han, C., Zhang, P., Deng, Y.: AI-guided adaptive multiscale modeling of platelet dynamics. In: ACM Student Research Competition Poster of the International Conference for High Performance Computing, Networking, Storage and Analysis (2020)"},{"key":"13_CR28","doi-asserted-by":"crossref","unstructured":"Hanson, W.A.: The CORAL supercomputer systems. IBM J. Res. Dev. 64, 1:1\u20131:10 (2019)","DOI":"10.1147\/JRD.2019.2960220"},{"key":"13_CR29","doi-asserted-by":"crossref","unstructured":"Sheriff, J., Bluestein, D.: Platelet dynamics in blood flow. In: Dynamics of Blood Cell Suspensions in Microflows, pp. 215\u2013256. CRC Press (2019)","DOI":"10.1201\/b21806-7"},{"key":"13_CR30","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1016\/j.jbiomech.2016.11.016","volume":"50","author":"MJ Slepian","year":"2017","unstructured":"Slepian, M.J., et al.: Shear-mediated platelet activation in the free flow: perspectives on the emerging spectrum of cell mechanobiological mechanisms mediating cardiovascular implant thrombosis. J. Biomech. 50, 20\u201325 (2017)","journal-title":"J. Biomech."}],"container-title":["Lecture Notes in Computer Science","High Performance Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-78713-4_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,29]],"date-time":"2023-03-29T07:06:39Z","timestamp":1680073599000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-78713-4_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030787127","9783030787134"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-78713-4_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"17 June 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISC High Performance","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on High Performance Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 June 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 July 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"36","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"supercomputing2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.isc-hpc.com\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Linklings","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"74","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"24","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"32% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4.28","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4.13","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"In the ISC High Performance Workshop, there were 49 submissions, out of which 35  were accepted.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}