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Stefano Martiniani

Energy landscapes for machine learning
2017 · Andrew J. Ballard, Ritankar Das, et al. · Physical Chemistry Chemical Physics · 104
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Quantifying Hidden Order out of Equilibrium
2019 · Stefano Martiniani, Paul M. Chaikin, et al. · Physical Review X · 81
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Model-Free Measurement of Local Entropy Production and Extractable Work in Active Matter
2022 · Sunghan Ro, Buming Guo, et al. · Physical Review Letters · 74
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The Mechanism of Iodine Reduction by TiO 2 Electrons and the Kinetics of Recombination in Dye-Sensitized Solar Cells
2012 · Caryl E. Richards, Assaf Y. Anderson, et al. · The Journal of Physical Chemistry Letters · 71
Turning intractable counting into sampling: Computing the configurational entropy of three-dimensional jammed packings
2016 · Stefano Martiniani, K. Julian Schrenk, et al. · Physical review. E · 62
Numerical test of the Edwards conjecture shows that all packings are equally probable at jamming
2017 · Stefano Martiniani, K. Julian Schrenk, et al. · Nature Physics · 48
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Estimating random close packing in polydisperse and bidisperse hard spheres via an equilibrium model of crowding
2022 · Carmine Anzivino, Mathias Casiulis, et al. · The Journal of Chemical Physics · 42
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Superposition Enhanced Nested Sampling
2014 · Martiniani, Stefano, Stevenson, Jacob D, et al. · 38
Near-infrared absorbing squaraine dye with extended π conjugation for dye-sensitized solar cells
2013 · Claudio Magistris, Stefano Martiniani, et al. · Renewable Energy · 38
On the design space between molecular mechanics and machine learning force fields
2025 · Yuanqing Wang, Kenichiro Takaba, et al. · Applied Physics Reviews · 35
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New insight into the regeneration kinetics of organic dye sensitised solar cells
2012 · Stefano Martiniani, Assaf Y. Anderson, et al. · Chemical Communications · 33
Exploiting the potential energy landscape to sample free energy
2015 · Andrew J. Ballard, Stefano Martiniani, et al. · Wiley Interdisciplinary Reviews Computational Molecular Science · 23
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High-throughput developability assays enable library-scale identification of producible protein scaffold variants
2021 · Alexander W. Golinski, Katelynn M. Mischler, et al. · Proceedings of the National Academy of Sciences · 22
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Structural analysis of high-dimensional basins of attraction
2016 · Stefano Martiniani, K. Julian Schrenk, et al. · Physical review. E · 22
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ColabFit exchange: Open-access datasets for data-driven interatomic potentials
2023 · Joshua A. Vita, Eric G. Fuemmeler, et al. · The Journal of Chemical Physics · 20
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Monte Carlo sampling for stochastic weight functions
2017 · Daan Frenkel, K. Julian Schrenk, et al. · Proceedings of the National Academy of Sciences · 18
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Fast generation of spectrally shaped disorder
2024 · Aaron Shih, Mathias Casiulis, et al. · Physical review. E · 17
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Transport and energetics of bacterial rectification
2024 · Satyam Anand, Xiaolei Ma, et al. · Proceedings of the National Academy of Sciences · 16
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Vicsek model by time-interlaced compression: A dynamical computable information density
2021 · A. Cavagna, P. M. Chaikin, et al. · Physical review. E · 13
Correlation Lengths in the Language of Computable Information
2020 · Stefano Martiniani, Yuval Lemberg, et al. · Physical Review Letters · 12
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A geometric condition for robot-swarm cohesion and cluster–flock transition
2025 · Mathias Casiulis, Eden Arbel, et al. · Proceedings of the National Academy of Sciences · 10
Gyromorphs: A New Class of Functional Disordered Materials
2025 · Mathias Casiulis, Aaron Shih, et al. · Physical Review Letters · 7
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Predicting and Interpreting Protein Developability Via Transfer of Convolutional Sequence Representation
2023 · Alexander W. Golinski, Zachary D. Schmitz, et al. · ACS Synthetic Biology · 7
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When you can’t count, sample!
2023 · Stefano Martiniani, Mathias Casiulis · Papers in Physics · 6
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PropMolFlow: property-guided molecule generation with geometry-complete flow matching
2026 · Cheng Zeng, Jirui Jin, et al. · Nature Computational Science · 5
Works count: 70
Citation count: 849
H-index: 16
I10-index: 21
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