# Machine learning applications ## expand\_more The entire space of materials - including organic and inorganic crystal structures - is huge. Insight into the behavior of materials can be gained by using data-driven tools to reveal hidden physical relationships which are embedded in a high-dimensional feature space, not readily perceived without statistical tools. The goal of this research is multifaceted and includes both knowledge discovery - gaining new physical insight through data analytics methods - and materials discovery. The Kaxiras group is currently exploiting machine learning tools to explore two-dimensional magnetic materials, catalysis on metal surfaces and branched electronic flow in graphene. ## Recent Publications in Machine Learning Applications Download 3 citations download- [BibTeX](/bibcite/export?pager_style=no_pager&number_of_items=6&sort_field=bibcite_year--desc&taxonomy_filters%5Bfield_hwp_c_topic%5D%5B0%5D%5Btarget_id%5D=21872&&&format=bibtex) - [EndNote X3 XML](/bibcite/export?pager_style=no_pager&number_of_items=6&sort_field=bibcite_year--desc&taxonomy_filters%5Bfield_hwp_c_topic%5D%5B0%5D%5Btarget_id%5D=21872&&&format=endnote8) - [EndNote 7 XML](/bibcite/export?pager_style=no_pager&number_of_items=6&sort_field=bibcite_year--desc&taxonomy_filters%5Bfield_hwp_c_topic%5D%5B0%5D%5Btarget_id%5D=21872&&&format=endnote7) - [Endnote tagged](/bibcite/export?pager_style=no_pager&number_of_items=6&sort_field=bibcite_year--desc&taxonomy_filters%5Bfield_hwp_c_topic%5D%5B0%5D%5Btarget_id%5D=21872&&&format=tagged) - [Marc](/bibcite/export?pager_style=no_pager&number_of_items=6&sort_field=bibcite_year--desc&taxonomy_filters%5Bfield_hwp_c_topic%5D%5B0%5D%5Btarget_id%5D=21872&&&format=marc) - [PubMedId](/bibcite/export?pager_style=no_pager&number_of_items=6&sort_field=bibcite_year--desc&taxonomy_filters%5Bfield_hwp_c_topic%5D%5B0%5D%5Btarget_id%5D=21872&&&format=pubmed_id) - [RIS](/bibcite/export?pager_style=no_pager&number_of_items=6&sort_field=bibcite_year--desc&taxonomy_filters%5Bfield_hwp_c_topic%5D%5B0%5D%5Btarget_id%5D=21872&&&format=ris) ### 2022 Angeli M, Neofotistos G, Mattheakis M, Kaxiras E. [Modeling the effect of the vaccination campaign on the COVID-19 pandemic](/publications/modeling-effect-vaccination-campaign-covid-19-pandemic). Chaos, Solitons & Fractals. 2022;154:111621. doi:10.1016/j.chaos.2021.111621 Angeli M, Neofotistos G, Mattheakis M, Kaxiras E. [Modeling the effect of the vaccination campaign on the COVID-19 pandemic](/publications/modeling-effect-vaccination-campaign-covid-19-pandemic). Chaos, Solitons & Fractals. 2022;154:111621. doi:10.1016/j.chaos.2021.111621 ### 2020 Rhone TD, Chen W, Desai S, Torrisi S, Larson D, Yacoby A, Kaxiras E. [Data-driven studies of magnetic two-dimensional materials](/publications/data-driven-studies-magnetic-two-dimensional-materials). Scientific reports. 2020;10(1):1–11. doi:10.1038/s41598-020-72811-z Rhone TD, Chen W, Desai S, Torrisi S, Larson D, Yacoby A, Kaxiras E. [Data-driven studies of magnetic two-dimensional materials](/publications/data-driven-studies-magnetic-two-dimensional-materials). Scientific reports. 2020;10(1):1–11. doi:10.1038/s41598-020-72811-z ### 2019 Neofotistos GN, M.Mattheakis, Barbaris G, Hitzanidi J, Tsironis GP, Kaxiras E. [Machine learning with observers predicts complex spatiotemporal behavior.](/publications/machine-learning-observers-predicts-complex-spatiotemporal-behavior) Front. Phys. - Quantum Computing. 2019;7(24):1–9. Neofotistos GN, M.Mattheakis, Barbaris G, Hitzanidi J, Tsironis GP, Kaxiras E. [Machine learning with observers predicts complex spatiotemporal behavior.](/publications/machine-learning-observers-predicts-complex-spatiotemporal-behavior) Front. Phys. - Quantum Computing. 2019;7(24):1–9. - add\_circle do\_not\_disturb\_on Abstract - [ descriptionPublisher's Version](https://www.frontiersin.org/articles/10.3389/fphy.2019.00024/full) Chimeras and branching are two archetypical complex phenomena that appear in many physical systems; because of their different intrinsic dynamics, they delineate opposite non-trivial limits in the complexity of wave motion and present severe challenges in... - [ descriptionPublisher's Version](https://www.frontiersin.org/articles/10.3389/fphy.2019.00024/full) ## Members working on this project [### Chiara Cignarella ](/people/chiara-cignarella) Chiara investigates low-dimensional materials using density-functional-theory and machine-learned force fields, with a particular focus on vibrational properties. Her work at Harvard focuses on the corrugation of twisted bilayer graphene using machine... ![Chiara Cignarella](/sites/g/files/omnuum11321/files/styles/hwp_4_5__690x865/public/2026-06/Chiara.jpeg?h=35f6776a&itok=rh9BeT3h) [ More arrow\_circle\_right ](/people)