Research Interests

Our lab's research focuses on the design of novel theoretical and computational frameworks to address open problems in science and engineering. Our approaches draw primarily from statistical physics, dynamical systems, and machine learning. We are a cognitively diverse group of scientists and engineers whose backgrounds span physics, chemical and biological engineering, materials science, statistics, scientific computing and data science. The problems we work on emerge from our own theoretical interests, as well as our close interactions with experimentalists, with whom we pursue quantitative descriptions of experimentally observed phenomena.

Currently, the lab's active research projects span (i) fundamental work on the energy landscapes of disordered systems, such as amorphous solids, spin glasses, and neural systems; (ii) the development of a unifying framework of neural dynamics, with an emphasis on modelling sensory-evoked activity in the visual cortex, and working memory representation and manipulation in the prefrontal cortex (iii) the integration of machine learning and sampling methodologies for (bio)molecular design and simulation; (iv) the development of experimentally viable approaches to measure entropy production in active matter systems; (v) the exploration of the relationship between information, order and correlations in complex systems.


  • Outstanding Thesis Prize, Department of Chemistry, University of Cambridge (2017)
  • Gates Cambridge Scholarship (2013-2017)
  • St. John's College Benefactors Scholarship (2013-2017)
  • Prize for Best Physical Chemistry Research Project, Department of Chemistry, Imperial College London (2012)

Selected Publications

  • S. Martiniani, P. M. Chaikin, D. Levine, “Correlation lengths in the language of computable information”, Phys. Rev. Lett., 125, 170601 (2020)
  • S. Martiniani, P. M. Chaikin, D. Levine, “Quantifying hidden order out of equilibrium”, Phys. Rev. X, 9, 011031 (2019)
  • D. Frenkel, K. J. Schrenk, S. Martiniani “Monte Carlo sampling for stochastic weight functions”, Proc. Natl. Acad. Sci., 114, 27 (2017)
  • S. Martiniani, K. J. Schrenk, K. Ramola, B. Chakraborty, D. Frenkel, “Numerical test of the Edwards conjecture shows that all packings become equally probable at jamming”, Nature Physics, 13, 848–851 (2017)
  • 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, 12585-12603 (2017)
  • S. Martiniani, K. J. Schrenk, J. D. Stevenson, D. J. Wales, D. Frenkel, “Structural analysis of high dimensional basins of attraction”, Phys. Rev. E 94, 031301 (2016)
  • S. Martiniani, K. J. Schrenk, J. D. Stevenson, D. J. Wales, D. Frenkel, “Turning intractable counting into sampling: computing the configurational entropy of three-dimensional jammed packings”, 
 Phys. Rev. E 93, 012906 (2016)
  • S. Martiniani, J. D. Stevenson, D. J. Wales, D. Frenkel, “Superposition Enhanced Nested Sampling”, Phys. Rev. X 4, 031034 (2014)
Stefano Martiniani in Lab


Office: 231

Support Stefano Martiniani's Research

  • B.Sc. Chemistry, Imperial College London, 2012
  • M.Phil. Scientific Computing, University of Cambridge, 2013
  • Ph.D. Chemistry (Theoretical), University of Cambridge, 2017
  • Postdoctoral Researcher, Center for Soft Matter Research, New York University, 2017-2019