For a more up-to-date list of publications, please visit Professor Cersonsky’s Google Scholar Page!

In Press and Preprints

• Lin, A. Y.; Ortengren, L.; Hwang, S.; Cho, Y.-C.; Nigam, J.; Cersonsky, R. K. AniSOAP: Machine Learning Representations for Coarse-Grained and Non-Spherical Systems. Journal of Open Source Software 2025.

Publications

• Lin, A.; Huguenin-Dumittan, K. K.; Cho, Y.-C.; Nigam, J.; Cersonsky, R. K. Expanding Density-Correlation Machine Learning Representations for Anisotropic Coarse-Grained Particles. The Journal of Chemical Physics 2024, 161 (7), 074112.
  https://doi.org/10.1063/5.0210910.
• Gazzarrini, E.; Cersonsky, R. K.; Bercx, M.; Adorf, C. S.; Marzari, N. The Rule of Four: Anomalous Distributions in the Stoichiometries of Inorganic Compounds. npj Comput Mater 2024, 10 (1), 73.
  https://doi.org/10.1038/s41524-024-01248-z.
• Cersonsky, R. K.; Cheng, B.; Kofke, D.; Müller, E. A. Machine Learning for Generating and Analyzing Thermophysical Data: Where We Are and Where We’re Going. J. Chem. Eng. Data 2024, 69 (6), 2041–2043.
  https://doi.org/10.1021/acs.jced.4c00207.
• Cersonsky, T. E. K.; Cersonsky, R. K.; Saade, G. R.; Silver, R. M.; Reddy, U. M.; Goldenberg, R. L.; Dudley, D. J.; Pinar, H. Placental Lesions Associated with Stillbirth by Gestational Age, According to Feature Importance: Results from the Stillbirth Collaborative Research Network. Placenta 2023, 137, 59–64. https://doi.org/10.1016/j.placenta.2023.04.005.
• Cersonsky, T. E. K.; Cersonsky, R. K.; Silver, R. M.; Dudley, D. J.; Pinar, H. Placental Lesions Associated With Stillbirth by Gestational Age, as Related to Cause of Death: Follow-Up Results From the Stillbirth Collaborative Research Network. Pediatr Dev Pathol 2023, 10935266231197349.
  https://doi.org/10.1177/10935266231197349.
• Cersonsky, R. K.; Pakhnova, M.; Engel, E. A.; Ceriotti, M. A Data-Driven Interpretation of the Stability of Organic Molecular Crystals. Chem. Sci. 2023, 14 (5), 1272–1285.
  https://doi.org/10.1039/D2SC06198H.
• Cersonsky, R. K.; De, S. Unsupervised Learning. In Quantum Chemistry in the Age of Machine Learning; Elsevier, 2023; pp 153–181. https://doi.org/10.1016/C2020-0-03124-5
• Zhou, Y.; Cersonsky, R. K.; Glotzer, S. C. A Route to Hierarchical Assembly of Colloidal Diamond. Soft Matter 2022, 18 (2), 304–311.
  https://doi.org/10.1039/D1SM01418H.
• Cersonsky, R. K.; Helfrecht, B. A.; Engel, E. A.; Kliavinek, S.; Ceriotti, M. Improving Sample and Feature Selection with Principal Covariates Regression. Machine Learning: Science and Technology 2021, 2 (3), 035038. https://doi.org/10.1088/2632-2153/abfe7c.
• Cersonsky, R. K.; Antonaglia, J.; Dice, B. D.; Glotzer, S. C. The Diversity of Three-Dimensional Photonic Crystals. Nat Commun 2021, 12 (1), 2543.
  https://doi.org/10.1038/s41467-021-22809-6.
• Helfrecht, B. A.; Cersonsky, R. K.; Fraux, G.; Ceriotti, M. Structure-Property Maps with Kernel Principal Covariates Regression. Mach. Learn.: Sci. Technol. 2020, 1 (4), 045021.
  https://doi.org/10.1088/2632-2153/aba9ef.
• Cersonsky, R. K. Designing Nanoparticles for Self-Assembly of Novel Materials; UM, 2019.
  https://hdl.handle.net/2027.42/153520.
• Cersonsky, R. K.; van Anders, G.; Dodd, P. M.; Glotzer, S. C. Relevance of Packing to Colloidal Self-Assembly. Proceedings of the National Academy of Sciences 2018, 115 (7), 1439–1444.
  https://doi.org/10.1073/pnas.1720139115.
• Cersonsky, R. K.; Dshemuchadse, J.; Antonaglia, J.; van Anders, G.; Glotzer, S. C. Pressure-Tunable Photonic Band Gaps in an Entropic Colloidal Crystal. Physical Review Materials 2018, 2 (12), 125201.
  https://doi.org/10.1103/PhysRevMaterials.2.125201.

Outreach and Scientific Education

• Travitz, A.; Muniz, A.; Beckwith, J.; Cersonsky, R. K. Paper: Bringing Science Education and Research Together to REACT. 2020, 35030.
  https://doi.org/10.18260/1-2–35030.
• Cersonsky, R. K.; Foster, L. L.; Ahn, T.; Hall, R. J.; van der Laan, H. L.; Scott, T. F. Augmenting Primary and Secondary Education with Polymer Science and Engineering. Journal of Chemical Education 2017, 94 (11), 1639–1646.
  https://doi.org/10.1021/acs.jchemed.6b00805.

Software

• Goscinski, A.; Principe, V. P.; Fraux, G.; Kliavinek, S.; Helfrecht, B. A.; Loche, P.; Ceriotti, M.; Cersonsky, R. K. Scikit-Matter : A Suite of Generalisable Machine Learning Methods Born out of Chemistry and Materials Science. Open Res Europe 2023, 3, 81.
  https://doi.org/10.12688/openreseurope.15789.1.
• Fraux, G.; Cersonsky, R.; Ceriotti, M. Chemiscope: Interactive Structure-Property Explorer for Materials and Molecules. Journal of Open Source Software 2020, 5 (51), 2117–2117.
  https://doi.org/10.21105/joss.02117.

Open Source Datasets

• Cersonsky, R. K.; Pakhnova, M.; Engel, E.; Ceriotti, M. Lattice Energies and Relaxed Geometries for 2’707 Organic Molecular Crystals and Their 3’242 Molecular Components., 2023.
  https://doi.org/10.24435/MATERIALSCLOUD:71-21.
• Helfrecht, B. A.; Cersonsky, R. K.; Fraux, G.; Ceriotti, M. Structure-Property Maps with Kernel Principal Covariates Regression, 2020.
  https://doi.org/10.24435/MATERIALSCLOUD:AY-EQ.
• Cersonsky, R. K.; Antonaglia, J. A.; Dice, B. D.; Glotzer, S. C. The Diversity of Three-Dimensional Photonic Crystals, 2021. https://glotzerlab.engin.umich.edu/photonics/index.html

Perspectives

• Pártay, L. B.; Teich, E. G.; Cersonsky, R. K. Not yet Defect-Free: The Current Landscape for Women in Computational Materials Research. npj Comput Mater 2023, 9 (1), 98.
  https://doi.org/10.1038/s41524-023-01054-z.
• Allen, M.; Bediako, K.; Bowman, W.; Calabrese, M.; Caretta, L.; Cersonsky, R. K.; Chen, W.; Correa, S.; Davidson, R.; Dresselhaus-Marais, L.; Eisler, C. N.; Furst, A.; Ge, T.; Hook, A.; Hsu, Y.-T.; Jia, C.; Lu, J.; Lunghi, A.; Messina, M.; Moreno-Hernandez, I. A.; Nichols, E.; Rao, R.; Seifrid, M.; Shulenberger, K. E.; Simonov, A.; Su, X.; Swearer, D.; Tang, E.; Taylor, M.; Tran, H.; Trindade, G.; Truby, R.; Utzat, H.; Yang, Y.; Yee, D. W.; Zhao, S.; Cranford, S. 35+1 Challenges In Materials Science Being Tackled by PIs Under 35(Ish) in 2023. Matter 2023, 6 (8), 2480–2487.
  https://doi.org/10.1016/j.matt.2023.06.046.