Publications

Preprints (*trainee)

1. Absorbing state dynamics of stochastic gradient descent   Read
   G. Zhang*, S. Martiniani, arXiv preprint arxiv:2411.11834 (2024)
2. Gyromorphs: a new class of functional disordered materials   Read
   M. Casiulis*, A. Shih*, S. Martiniani, arXiv preprint arxiv:2410.09023 (2024)
3. Mirages in the Energy Landscape of Soft Sphere Packings   Read
   P. Suryadevara*, M. Casiulis*, S. Martiniani, arXiv preprint arXiv:2409.12113 (2024)
4. On the design space between molecular mechanics and machine learning force fields   Read
   Y. Wang, K. Takaba, M.S. Chen, M. Wieder, Y. Xu, T. Zhu, J.Z.H. Zhang, A. Nagle, K. Yu, X. Wang, D.J. Cole, J.A. Rackers, K. Cho, J.G. Greener, P. Eastman, S. Martiniani, M.E. Tuckerman, arXiv preprint arXiv:2409.01931 (2024)
5. A geometric condition for robot-swarm cohesion and cluster-flock transition   Read
   M. Casiulis*, E. Arbel, Y. Lahini, S. Martiniani, N. Oppenheimer, M. Yah Ben Zion, arXiv preprint arXiv:2409.04618 (2024)
6. Social Physics of Bacteria: Avoidance of an Information Black Hole   Read
   T.V. Phan, S. Li, D. Ferreris, R. Morris, J. Bos, B. Guo, S. Martiniani, P. Chaikin, Y.G. Kevrekidis, R.H. Austin, arXiv preprint arXiv:2401.16691 (2024)

Published

7. Transport and Energetics of Bacterial Rectification   Read
   S Anand*, X Ma, S Guo, S. Martiniani, X Cheng, Proc. Natl. Acad. Sci., 121(52), e2411608121 (2024)
8. Unconditional stability of a recurrent neural circuit implementing divisive normalization  Read
   S. Rawat*, S. Martiniani, Adv. Neural. Inf. Process. Syst. 38 (NeurIPS 2024)
9. Advancing the ColabFit Exchange towards a Web-scale Data Source for Machine Learning Interatomic Potentials   Read
   E. Fuemmeler*, G. Wolfe*, A. Gupta*, J.A. Vita, E.B. Tadmor, S. Martiniani, AI for Accelerated Materials Design - NeurIPS 2024 (2024)
10. Benchmarking of Universal Machine Learning Interatomic Potentials for Structural Relaxation   Read
    A.C. Gonzales, E. Fuemmeler*, E.B. Tadmor, S. Martiniani, S. Miret, AI for Accelerated Materials Design - NeurIPS 2024 (2024)
11. Element-wise and Recursive Solutions for the Power Spectral Density of Biological Stochastic Dynamical Systems at Fixed Points  Read
    S. Rawat*, S. Martiniani, Phys. Rev. Research 6, 043179 (2024)
12. Fast Generation of Spectrally-Shaped Disorder  Read
    A. Shih*, M. Casiulis*, S. Martiniani, Phys. Rev. E 110, 034122 (2024) (2024)
    • Highlighted as Editor's Suggestion in Phys. Rev. E.
    • Highlighted in “Old Movie Demos New Tech”, Physics 17, 134 (2024)
13. KUSP: Python server for deploying ML interatomic potentials”, AI for Accelerated Materials Design - Vienna 2024   Read
    A. Gupta*, E.B. Tadmor, S. Martiniani, AI for Accelerated Materials Design - Vienna 2024 (2024)
14. ColabFit Exchange: open-access datasets for data-driven interatomic potentials  Read
    A. Vita*, E. G. Fuemmeler*, A. Gupta*, G.P. Wolfe*, A. Q. Tao*, R. S. Elliott, S. Martiniani, E. B. Tadmor, J. Chem. Phys. 159, 154802 (2023)
15. Predicting and Interpreting Protein Developability via Transfer of Convolutional Sequence Representation  Read
    A. W. Golinski*, Z. D. Schmitz*, G. H. Nielsen, B. Johnson*, D. Saha*, S. Appiah*, B. J. Hackel, S. Martiniani, ACS Synthetic Biology 12 (9), 2600-2615
16. Coherence influences the dimensionality of communication subspaces  Read
    S. Rawat*, D. Heeger, S. Martiniani. Cosyne Abstracts (2023)
17. Bit-propelled active matter  Read
    S. Martiniani, Journal Club for Condensed Matter Physics, March 2023.
18. When you can’t count sample! Computable entropies beyond equilibrium from basin volumes  Read
    M. Casiulis*, S. Martiniani, Papers in Physics, 15, 150001 (2023).
• Invited perspective in “Focus Series on Challenges in Granular Matter”, Editors: K. Daniels, L. Pugnaloni, J. Zhao.
19. Estimating random close packing in polydisperse and bidisperse hard spheres via an equilibrium model of crowding  Read
    C. Anzivino, M. Casiulis*, T. Zhang,* A. S. Moussa, S. Martiniani, A. Zaccone, J. Chem. Phys., 158, 044901 (2023)
20. Model-Free Measurement of Local Entropy Production and Extractable Work in Active Matter  Read
    S. Ro, B. Guo, A. Shih*, T.V. Phan, R.H. Austin, D. Levine, P.M. Chaikin, S. Martiniani, Phys. Rev. Lett. 129, 220601 (2022).
• Cover article for Phys. Rev. Lett.
• Highlighted as Editor's Suggestion in Phys. Rev. Lett.
• Highlighted in “ Viewpoint: Measuring Entropy in Active-Matter Systems”, Physics 15, 179 (2022)
21. High-Throughput Developability Assays Enable Library-Scale Identification of Producible Protein Scaffold Variants  Read
    A. W. Golinski*, K. M. Mischler, S. Laxminarayan, N. Neurock, M. Fossing, H. Pichman, S. Martiniani, B. J. Hackel, Proc. Natl. Acad. Sci., 118, 23 (2021)
22. Vicsek Model by Time-Interlaced Compression: a Dynamical Computable Information Density  Read
    A. Cavagna, P. M. Chaikin, D. Levine, S. Martiniani, A. Puglisi, M. Viale, Phys. Rev. E 103, 062141 (2021)
23. Correlation lengths in the language of computable information  Read
    S. Martiniani, Y. Lemberg, P. M. Chaikin, D. Levine, Phys. Rev. Lett., 125, 170601 (2020)
24. Quantifying hidden order out of equilibrium  Read
    S. Martiniani, P. M. Chaikin, D. Levine, Phys. Rev. X, 9, 011031 (2019)
• Highlighted in “File compression uncovers hidden order”, Physics Today (2019).
25. Numerical test of the Edwards conjecture shows that all packings become equally probable at jamming  Read
    S. Martiniani, K. J. Schrenk, K. Ramola, B. Chakraborty, D. Frenkel, Nature Physics, 13, 848-851 (2017)
• Highlighted in “Intuition harnessed in the name of particle packing”, Nature, 546, 575 (2017).
• Highlighted in “A thermodynamic theory of granular materials endures”, Physics Today 70, 9 (2017).
• Highlighted in “Material Witness: A jammy guess?”, Nature Materials, 15, 1227 (2016).
26. Monte Carlo sampling for stochastic weight functions  Read
    D. Frenkel, K. J. Schrenk, S. Martiniani, Proc. Natl. Acad. Sci., 114, 27 (2017)
27. A. J. Ballard, R. Das, S. Martiniani, D. Mehta, L. Sagun, J. D. Stevenson, D. J. Wales, “Energy Landscapes for Machine Learning”, Phys. Chem. Chem. Phys., 19, 12585 (2017)   Read
• Cover article for Phys. Chem. Chem. Phys.
28. Structural analysis of high dimensional basins of attraction  Read
    Martiniani, K. J. Schrenk, J. D. Stevenson, D. J. Wales, D. Frenkel, Phys. Rev. E 94, 031301 (2016)
29. Turning intractable counting into sampling: computing the configurational entropy of three-dimensional jammed packings  Read
    S. Martiniani, K. J. Schrenk, J. D. Stevenson, D. J. Wales, D. Frenkel, Phys. Rev. E 93, 012906 (2016)
• Highlighted in NewScientist and other news sources, see Press.
30. Exploiting the potential energy landscape to sample free energy   Read
    A. J. Ballard, S. Martiniani, J. D. Stevenson, S. Somani, D. J. Wales, WIREs Comput. Mol. Sci. 5, 273 (2015)
31. Superposition Enhanced Nested Sampling  Read
    S. Martiniani, J. D. Stevenson, D. J. Wales, D. Frenkel, Phys. Rev. X 4, 031034 (2014)
32. Near-infrared absorbing squaraine dye with extended π conjugation for dye-sensitized solar cells  Read
    C. Magistris†, S. Martiniani†, N. Barbero, J. Park, C. Benzi, A. Anderson, C. H. Law, C. Barolo, B. C. O'Regan, Renewable Energy 60, 672 (2013)
33. The Mechanism of Iodine Reduction by TiO2 Electrons and the Kinetics of Recombination in Dye-Sensitized Solar Cells  Read
    C. E. Richards, A. Y. Anderson, S. Martiniani, C. Law, B. C. O'Regan, J. Phys. Chem. Lett. 3, 1980 (2012)
34. New insight into the regeneration kinetics of dye sensitised solar cells  Read
    S. Martiniani, A. Y. Anderson, C. Law, B. C. O'Regan and C. Barolo, Chem. Commun. 48, 2406 (2012)

Ph.D. Theses

35. Data-driven approach to engineering protein evolvability and developability  Read
    A. Golinsky*, University of Minnesota (2021)
36. On the complexity of energy landscapes: algorithms and a direct test of the Edwards conjecture  Read
    S. Martiniani, University of Cambridge (2017)