Roadmap on machine learning glassy dynamics

Machine Learning


  • Ediger, M. D., Angell, C. A. & Nagel, S. R. Supercooled liquids and glasses. J. Phys. Chem. 100, 13200–13212 (1996).

    MATH 

    Google Scholar 

  • Cavagna, A. Supercooled liquids for pedestrians. Phys. Rep. 476, 51–124 (2009).

    ADS 
    MATH 

    Google Scholar 

  • LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).

    ADS 
    MATH 

    Google Scholar 

  • Alzubaidi, L. et al. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J. Big Data 8, 1–74 (2021).

    MATH 

    Google Scholar 

  • Royall, C. P. & Williams, S. R. The role of local structure in dynamical arrest. Phys. Rep. 560, 1–75 (2015).

    ADS 
    MathSciNet 
    MATH 

    Google Scholar 

  • Tanaka, H., Tong, H., Shi, R. & Russo, J. Revealing key structural features hidden in liquids and glasses. Nat. Rev. Phys. 1, 333–348 (2019).

    MATH 

    Google Scholar 

  • Marín-Aguilar, S., Wensink, H. H., Foffi, G. & Smallenburg, F. Tetrahedrality dictates dynamics in hard sphere mixtures. Phys. Rev. Lett. 124, 208005 (2020).

    ADS 

    Google Scholar 

  • Richard, D. et al. Predicting plasticity in disordered solids from structural indicators. Phys. Rev. Mater. 4, 113609 (2020).

    Google Scholar 

  • Boattini, E. et al. Autonomously revealing hidden local structures in supercooled liquids. Nat. Commun. 11, 5479 (2020).

    ADS 
    MATH 

    Google Scholar 

  • Paret, J., Jack, R. L. & Coslovich, D. Assessing the structural heterogeneity of supercooled liquids through community inference. J. Chem. Phys. 152, 144502 (2020).

    ADS 
    MATH 

    Google Scholar 

  • Oyama, N., Koyama, S. & Kawasaki, T. What do deep neural networks find in disordered structures of glasses? Front. Phys. 10, 1320 (2023).

    MATH 

    Google Scholar 

  • Soltani, S., Sinclair, C. W. & Rottler, J. Exploring glassy dynamics with Markov state models from graph dynamical neural networks. Phys. Rev. E 106, 025308 (2022).

    ADS 
    MATH 

    Google Scholar 

  • Widmer-Cooper, A. & Harrowell, P. Predicting the long-time dynamic heterogeneity in a supercooled liquid on the basis of short-time heterogeneities. Phys. Rev. Lett. 96, 185701 (2006).

    ADS 
    MATH 

    Google Scholar 

  • Widmer-Cooper, A., Perry, H., Harrowell, P. & Reichman, D. R. Irreversible reorganization in a supercooled liquid originates from localized soft modes. Nat. Phys. 4, 711–715 (2008).

    Google Scholar 

  • Berthier, L. & Biroli, G. Theoretical perspective on the glass transition and amorphous materials. Rev. Mod. Phys. 83, 587–645 (2011).

    ADS 
    MATH 

    Google Scholar 

  • Schoenholz, S. S., Cubuk, E. D., Sussman, D. M., Kaxiras, E. & Liu, A. J. A structural approach to relaxation in glassy liquids. Nat. Phys. 12, 469–471 (2016).

    MATH 

    Google Scholar 

  • Cubuk, E. D. et al. Identifying structural flow defects in disordered solids using machine-learning methods. Phys Rev. Lett. 114, 108001 (2015).

    ADS 
    MATH 

    Google Scholar 

  • Bapst, V. et al. Unveiling the predictive power of static structure in glassy systems. Nat. Phys. 16, 448–454 (2020).

    MATH 

    Google Scholar 

  • Yang, Z.-Y., Wei, D., Zaccone, A. & Wang, Y.-J. Machine-learning integrated glassy defect from an intricate configurational-thermodynamic-dynamic space. Phys. Rev. B 104, 064108 (2021).

    ADS 

    Google Scholar 

  • Boattini, E., Smallenburg, F. & Filion, L. Averaging local structure to predict the dynamic propensity in supercooled liquids. Phys. Rev. Lett. 127, 088007 (2021).

    ADS 

    Google Scholar 

  • Alkemade, R. M., Boattini, E., Filion, L. & Smallenburg, F. Comparing machine learning techniques for predicting glassy dynamics. J. Chem. Phys. 156, 204503 (2022).

    ADS 

    Google Scholar 

  • Shiba, H., Hanai, M., Suzumura, T. & Shimokawabe, T. BOTAN: BOnd TArgeting Network for prediction of slow glassy dynamics by machine learning relative motion. J. Chem. Phys. 158, 084503 (2023).

    ADS 

    Google Scholar 

  • Alkemade, R. M., Smallenburg, F. & Filion, L. Improving the prediction of glassy dynamics by pinpointing the local cage. J. Chem. Phys. 158, 134512 (2023).

    ADS 

    Google Scholar 

  • Ciarella, S., Chiappini, M., Boattini, E., Dijkstra, M. & Janssen, L. M. C. Dynamics of supercooled liquids from static averaged quantities using machine learning. Mach. Learn. Sci. Technol. 4, 025010 (2023).

    ADS 

    Google Scholar 

  • Pezzicoli, F. S., Charpiat, G. & Landes, F. P. Rotation-equivariant graph neural networks for learning glassy liquids representations. SciPost Phys. 16, 136 (2024).

    ADS 
    MathSciNet 

    Google Scholar 

  • Ruiz-Garcia, M. et al. Discovering dynamic laws from observations: the case of self-propelled, interacting colloids. Phys. Rev. E 109, 064611 (2024).

    Google Scholar 

  • Jung, G., Biroli, G. & Berthier, L. Predicting dynamic heterogeneity in glass-forming liquids by physics-inspired machine learning. Phys. Rev. Lett. 130, 238202 (2023).

    ADS 

    Google Scholar 

  • Zhang, G. et al. Structuro-elasto-plasticity model for large deformation of disordered solids. Phys. Rev. Res. 4, 043026 (2022).

    MATH 

    Google Scholar 

  • Jung, G. GlassBench. zenodo https://doi.org/10.5281/zenodo.10118191 (2023).

  • Kob, W. & Andersen, H. C. Testing mode-coupling theory for a supercooled binary Lennard-Jones mixture I: the van Hove correlation function. Phys. Rev. E 51, 4626–4641 (1995).

    ADS 
    MATH 

    Google Scholar 

  • Tarjus, G., Kivelson, D. & Viot, P. The viscous slowing down of supercooled liquids as a temperature-controlled super-Arrhenius activated process: a description in terms of frustration-limited domains. J. Phys. Condens. Matter 12, 6497 (2000).

    ADS 
    MATH 

    Google Scholar 

  • Tanemura, M. et al. Geometrical analysis of crystallization of the soft-core model. Prog. Theor. Phys. 58, 1079–1095 (1977).

    ADS 
    MATH 

    Google Scholar 

  • Malins, A., Williams, S. R., Eggers, J. & Royall, C. P. Identification of structure in condensed matter with the topological cluster classification. J. Chem. Phys. 139, 234506 (2013).

    ADS 
    MATH 

    Google Scholar 

  • Honeycutt, J. D. & Andersen, H. C. Molecular dynamics study of melting and freezing of small Lennard-Jones clusters. J. Phys. Chem. 91, 4950–4963 (1987).

    MATH 

    Google Scholar 

  • Lazar, E. A., Han, J. & Srolovitz, D. J. A topological framework for local structure analysis in condensed matter. Proc. Natl Acad. Sci. USA 112, E5769–E5776 (2015).

    ADS 
    MathSciNet 
    MATH 

    Google Scholar 

  • Steinhardt, P. J., Nelson, D. R. & Ronchetti, M. Bond-orientational order in liquids and glasses. Phys. Rev. B 28, 784–805 (1983).

    ADS 
    MATH 

    Google Scholar 

  • Mehta, P. et al. A high-bias, low-variance introduction to machine learning for physicists. Phys. Rep. 810, 1–124 (2019).

    ADS 
    MathSciNet 
    MATH 

    Google Scholar 

  • Cheng, B. et al. Mapping materials and molecules. Acc. Chem. Res. 53, 1981–1991 (2020).

    MATH 

    Google Scholar 

  • Glielmo, A. et al. Unsupervised learning methods for molecular simulation data. Chem. Rev. 121, 9722–9758 (2021).

    MATH 

    Google Scholar 

  • Jarry, P. & Jakse, N. Medium range ordering in liquid Al-based alloys: towards a machine learning approach of solidification. IOP Conf. Ser. Mater. Sci. Eng. 1274, 012001 (2023).

    MATH 

    Google Scholar 

  • Hu, W., Singh, R. R. P. & Scalettar, R. T. Discovering phases, phase transitions, and crossovers through unsupervised machine learning: a critical examination. Phys. Rev. E 95, 062122 (2017).

    ADS 

    Google Scholar 

  • Rodriguez-Nieva, J. F. & Scheurer, M. S. Identifying topological order through unsupervised machine learning. Nat. Phys. 15, 790–795 (2019).

    MATH 

    Google Scholar 

  • Mendes-Santos, T., Turkeshi, X., Dalmonte, M. & Rodriguez, A. Unsupervised learning universal critical behavior via the intrinsic dimension. Phys. Rev. X 11, 011040 (2021).

    Google Scholar 

  • Bartók, A. P., Kondor, R. & Csányi, G. On representing chemical environments. Phys. Rev. B 87, 184115 (2013).

    ADS 
    MATH 

    Google Scholar 

  • Parsaeifard, B. et al. An assessment of the structural resolution of various fingerprints commonly used in machine learning. Mach. Learn. Sci. Technol. 2, 015018 (2021).

    MATH 

    Google Scholar 

  • Midtvedt, B. et al. Single-shot self-supervised object detection in microscopy. Nat. Commun. 13, 7492 (2022).

    ADS 
    MATH 

    Google Scholar 

  • Caro, M. A., Deringer, V. L., Koskinen, J., Laurila, T. & Csányi, G. Growth mechanism and origin of high sp3 in tetrahedral amorphous carbon. Phys. Rev. Lett. 120, 166101 (2018).

    ADS 

    Google Scholar 

  • Monserrat, B., Brandenburg, J. G., Engel, E. A. & Cheng, B. Liquid water contains the building blocks of diverse ice phases. Nat. Commun. 11, 5757 (2020).

    ADS 
    MATH 

    Google Scholar 

  • Coslovich, D., Jack, R. L. & Paret, J. Dimensionality reduction of local structure in glassy binary mixtures. J. Chem. Phys. 157, 204503 (2022).

    MATH 

    Google Scholar 

  • Banerjee, A., Hsu, H.-P., Kremer, K. & Kukharenko, O. Data-driven identification and analysis of the glass transition in polymer melts. ACS Macro Lett. 12, 679–684 (2023).

    MATH 

    Google Scholar 

  • Banerjee, A., Iscen, A., Kremer, K. & Kukharenko, O. Determining glass transition in all-atom acrylic polymeric melt simulations using machine learning. J. Chem. Phys. 159, 074108 (2023).

    ADS 

    Google Scholar 

  • Offei-Danso, A., Hassanali, A. & Rodriguez, A. High-dimensional fluctuations in liquid water: combining chemical intuition with unsupervised learning. J. Chem. Theory Comput. 18, 3136–3150 (2022).

    MATH 

    Google Scholar 

  • Campadelli, P., Casiraghi, E., Ceruti, C. & Rozza, A. Intrinsic dimension estimation: relevant techniques and a benchmark framework. Math. Probl. Eng. 2015, e759567 (2015).

    MathSciNet 
    MATH 

    Google Scholar 

  • Parsaeifard, B. & Goedecker, S. Manifolds of quasi-constant SOAP and ACSF fingerprints and the resulting failure to machine learn four-body interactions. J. Chem. Phys. 156, 034302 (2022).

    ADS 
    MATH 

    Google Scholar 

  • Darby, J. P., Kermode, J. R. & Csányi, G. Compressing local atomic neighbourhood descriptors. npj Comput. Mater. 8, 1–13 (2022).

    Google Scholar 

  • Darby, J. P. et al. Tensor-reduced atomic density representations. Phys. Rev. Lett. 131, 028001 (2023).

    ADS 

    Google Scholar 

  • Coslovich, D., Ozawa, M. & Berthier, L. Local order and crystallization of dense polydisperse hard spheres. J. Phys. Condens. Matter 30, 144004 (2018).

    ADS 

    Google Scholar 

  • Tong, H. & Tanaka, H. Emerging exotic compositional order on approaching low-temperature equilibrium glasses. Nat. Commun. 14, 4614 (2023).

    ADS 
    MATH 

    Google Scholar 

  • Elliott, S. R. Medium-range structural order in covalent amorphous solids. Nature 354, 445–452 (1991).

    ADS 
    MATH 

    Google Scholar 

  • Sheng, H., Luo, W., Alamgir, F., Bai, J. & Ma, E. Atomic packing and short-to-medium-range order in metallic glasses. Nature 439, 419–425 (2006).

    ADS 
    MATH 

    Google Scholar 

  • Montes de Oca, J. M., Sciortino, F. & Appignanesi, G. A. A structural indicator for water built upon potential energy considerations. J. Chem. Phys. 152, 244503 (2020).

    ADS 
    MATH 

    Google Scholar 

  • Faccio, C., Benzi, M., Zanetti-Polzi, L. & Daidone, I. Low- and high-density forms of liquid water revealed by a new medium-range order descriptor. J. Mol. Liq. 355, 118922 (2022).

    MATH 

    Google Scholar 

  • Mauro, J. C., Tandia, A., Vargheese, K. D., Mauro, Y. Z. & Smedskjaer, M. M. Accelerating the design of functional glasses through modeling. Chem. Mater. 28, 4267–4277 (2016).

    MATH 

    Google Scholar 

  • Cassar, D. R. et al. Predicting and interpreting oxide glass properties by machine learning using large datasets. Ceram. Int. 47, 23958–23972 (2021).

    MATH 

    Google Scholar 

  • Bødker, M. L., Bauchy, M., Du, T., Mauro, J. C. & Smedskjaer, M. M. Predicting glass structure by physics-informed machine learning. npj Comput. Mater. 8, 192 (2022).

    ADS 

    Google Scholar 

  • Bhattoo, R. et al. Artificial intelligence and machine learning in glass science and technology: 21 challenges for the 21st century. Int. J. Appl. Glass Sci. 12, 277–292 (2021).

    Google Scholar 

  • Doliwa, B. & Heuer, A. What does the potential energy landscape tell us about the dynamics of supercooled liquids and glasses? Phys. Rev. Lett. 91, 235501 (2003).

    ADS 
    MATH 

    Google Scholar 

  • Hocky, G. M., Coslovich, D., Ikeda, A. & Reichman, D. R. Correlation of local order with particle mobility in supercooled liquids is highly system dependent. Phys. Rev. Lett. 113, 157801 (2014).

    ADS 

    Google Scholar 

  • Tong, H. & Tanaka, H. Revealing hidden structural order controlling both fast and slow glassy dynamics in supercooled liquids. Phys. Rev. X 8, 011041 (2018).

    Google Scholar 

  • Schoenholz, S. S., Cubuk, E. D., Kaxiras, E. & Liu, A. J. Relationship between local structure and relaxation in out-of-equilibrium glassy systems. Proc. Natl Acad. Sci. USA 114, 263–267 (2017).

    ADS 
    MATH 

    Google Scholar 

  • Sussman, D. M., Schoenholz, S. S., Cubuk, E. D. & Liu, A. J. Disconnecting structure and dynamics in glassy thin films. Proc. Natl Acad. Sci. USA 114, 10601–10605 (2017).

    ADS 
    MathSciNet 
    MATH 

    Google Scholar 

  • Cubuk, E. D. et al. Structure-property relationships from universal signatures of plasticity in disordered solids. Science 358, 1033–1037 (2017).

    ADS 
    MATH 

    Google Scholar 

  • Harrington, M., Liu, A. J. & Durian, D. J. Machine learning characterization of structural defects in amorphous packings of dimers and ellipses. Phys. Rev. E 99, 022903 (2019).

    ADS 
    MATH 

    Google Scholar 

  • Ma, X. et al. Heterogeneous activation, local structure, and softness in supercooled colloidal liquids. Phys Rev. Lett.122, 028001 (2019).

    ADS 
    MATH 

    Google Scholar 

  • Cubuk, E. D., Liu, A. J., Kaxiras, E. & Schoenholz, S. S. Unifying framework for strong and fragile liquids via machine learning: a study of liquid silica. Preprint at https://doi.org/10.48550/arXiv.2008.09681 (2020).

  • Ridout, S. A., Rocks, J. W. & Liu, A. J. Correlation of plastic events with local structure in jammed packings across spatial dimensions. Proc. Natl Acad. Sci. USA 119, e2119006119 (2022).

    MATH 

    Google Scholar 

  • Tah, I., Ridout, S. A., & Liu, A. J. Fragility in glassy liquids: a structural approach based on machine learning. J. Chem. Phys.157, 124501 (2022).

    ADS 
    MATH 

    Google Scholar 

  • Liu, H., Smedskjaer, M. M. & Bauchy, M. Deciphering a structural signature of glass dynamics by machine learning. Phys. Rev. B 106, 214206 (2022).

    ADS 

    Google Scholar 

  • Zhang, G., Ridout, S. A. & Liu, A. J. Interplay of rearrangements, strain, and local structure during avalanche propagation. Phys. Rev. X 11, 041019 (2021).

    Google Scholar 

  • Xiao, H. et al. Machine learning-informed structuro-elastoplasticity predicts ductility of disordered solids. Preprint at https://doi.org/10.48550/arXiv.2303.12486 (2023).

  • Widmer-Cooper, A., Harrowell, P. & Fynewever, H. How reproducible are dynamic heterogeneities in a supercooled liquid? Phys. Rev. Lett. 93, 135701 (2004).

    ADS 

    Google Scholar 

  • Berthier, L. & Jack, R. L. Structure and dynamics of glass formers: predictability at large length scales. Phys. Rev. E 76, 041509 (2007).

    ADS 
    MATH 

    Google Scholar 

  • Jung, G., Biroli, G. & Berthier, L. Dynamic heterogeneity at the experimental glass transition predicted by transferable machine learning. Phys. Rev. B 109, 064205 (2024).

    ADS 

    Google Scholar 

  • Berthier, L., Biroli, G., Bouchaud, J.-P., Cipelletti, L. & Saarloos, W. Dynamical Heterogeneities in Glasses, Colloids, and Granular Media (Oxford Univ. Press, 2011).

  • Gidaris, S., Singh, P. & Komodakis, N. Unsupervised representation learning by predicting image rotations. Preprint at https://doi.org/10.48550/arXiv.1803.07728 (2018).

  • Toninelli, C., Wyart, M., Berthier, L., Biroli, G. & Bouchaud, J.-P. Dynamical susceptibility of glass formers: contrasting the predictions of theoretical scenarios. Phys. Rev. E 71, 041505 (2005).

    ADS 
    MATH 

    Google Scholar 

  • Kingma, D. P. et al. An introduction to variational autoencoders. Found. Trends Mach. Learn. 12, 307–392 (2019).

    MATH 

    Google Scholar 

  • Wang, Q. & Zhang, L. Inverse design of glass structure with deep graph neural networks. Nat. Commun. 12, 5359 (2021).

    ADS 
    MATH 

    Google Scholar 

  • Kivelson, S. & Kivelson, S. Understanding complexity. Nat. Phys. 14, 426–427 (2018).

    MATH 

    Google Scholar 

  • Wang, Q. et al. Predicting the propensity for thermally activated β events in metallic glasses via interpretable machine learning. npj Comput. Mater. 6, 194 (2020).

    ADS 
    MATH 

    Google Scholar 

  • Miao, S., Liu, M. & Li, P. Interpretable and generalizable graph learning via stochastic attention mechanism. In Proc. 39th International Conference on Machine Learning, Volume 162 of Proceedings of Machine Learning Research (eds Chaudhuri, K. et al.) 15524–15543 (PMLR, 2022).

  • Duede, E. Deep learning opacity in scientific discovery. Philos. Sci. 90, 1089–1099 (2023).

    MathSciNet 
    MATH 

    Google Scholar 

  • Glielmo, A., Zeni, C., Cheng, B., Csányi, G. & Laio, A. Ranking the information content of distance measures. PNAS Nexus 1, pgac039 (2022).

    Google Scholar 

  • Sandberg, J., Voigtmann, T., Devijver, E. & Jakse, N. Feature selection for high-dimensional neural network potentials with the adaptive group lasso. Mach. Learn. Sci. Technol. 5, 025043 (2024).

    ADS 

    Google Scholar 

  • Sharma, A., Liu, C. & Ozawa, M. Selecting relevant structural features for glassy dynamics by information imbalance. J. Chem. Phys. 161, 184506 (2024).

    Google Scholar 

  • Berthier, L., Flenner, E. & Szamel, G. Glassy dynamics in dense systems of active particles. J. Chem. Phys. 150, 200901 (2019).

    ADS 
    MATH 

    Google Scholar 

  • Janzen, G. & Janssen, L. M. C. Rejuvenation and memory effects in active glasses induced by thermal and active cycling. Phys. Rev. Res. 6, 023257 (2024).

    MATH 

    Google Scholar 

  • Janzen, G. et al. Dead or alive: distinguishing active from passive particles using supervised learning. Europhys. Lett. 143, 17004 (2023).

    ADS 

    Google Scholar 

  • Janzen, G. et al. Classifying the age of a glass based on structural properties: a machine learning approach. Phys. Rev. Mater. 8, 025602 (2024).

    MATH 

    Google Scholar 

  • Scalliet, C., Guiselin, B. & Berthier, L. Excess wings and asymmetric relaxation spectra in a facilitated trap model. J. Chem. Phys. 155, 064505 (2021).

    ADS 

    Google Scholar 

  • Guiselin, B., Scalliet, C. & Berthier, L. Microscopic origin of excess wings in relaxation spectra of supercooled liquids. Nat. Phys. 18, 468–472 (2022).

    MATH 

    Google Scholar 

  • Nicolas, A., Ferrero, E. E., Martens, K. & Barrat, J.-L. Deformation and flow of amorphous solids: Insights from elastoplastic models. Rev. Mod. Phys. 90, 045006 (2018).

    ADS 
    MATH 

    Google Scholar 

  • Ozawa, M. & Biroli, G. Elasticity, facilitation and dynamic heterogeneity in glass-forming liquids. Phys. Rev. Lett. 130, 138201 (2023).

    ADS 
    MATH 

    Google Scholar 

  • Tahaei, A., Biroli, G., Ozawa, M., Popović, M. & Wyart, M. Scaling description of dynamical heterogeneity and avalanches of relaxation in glass-forming liquids. Phys. Rev. X 13, 031034 (2023).

    Google Scholar 

  • Lerbinger, M., Barbot, A., Vandembroucq, D. & Patinet, S. Relevance of shear transformations in the relaxation of supercooled liquids. Phys. Rev. Lett. 129, 195501 (2022).

    ADS 
    MATH 

    Google Scholar 

  • Chacko, R. N. et al. Elastoplasticity mediates dynamical heterogeneity below the mode coupling temperature. Phys Rev. Lett. 127, 048002 (2021).

    ADS 
    MATH 

    Google Scholar 

  • Monthus, C. & Bouchaud, J. P. Models of traps and glass phenomenology. J. Phys. A Math. Gen. 29 3847 (1996).

    ADS 
    MATH 

    Google Scholar 

  • Ridout, S. A., Tah, I. & Liu, A. J. Building a “trap model” of glassy dynamics from a local structural predictor of rearrangements. Europhys. Lett. 144, 47001 (2023).

    ADS 

    Google Scholar 

  • Ridout, S. A. & Liu, A. J. The dynamics of machine-learned “softness” in supercooled liquids describe dynamical heterogeneity. Preprint at https://doi.org/10.48550/arXiv.2406.05868 (2024).

  • Ciarella, S. et al. Finding defects in glasses through machine learning. Nat. Commun. 14, 4229 (2023).

    ADS 
    MATH 

    Google Scholar 

  • Richard, D., Kapteijns, G. & Lerner, E. Detecting low-energy quasilocalized excitations in computer glasses. Phys. Rev. E 108, 044124 (2023).

    ADS 

    Google Scholar 

  • Scalliet, C., Guiselin, B. & Berthier, L. Thirty milliseconds in the life of a supercooled liquid. Phys. Rev. X 12, 041028 (2022).

    MATH 

    Google Scholar 

  • Ediger, M. D. Spatially heterogeneous dynamics in supercooled liquids. Annu. Rev. Phys. Chem. 51, 99–128 (2000).

    ADS 
    MATH 

    Google Scholar 

  • Tong, H. & Tanaka, H. Structural order as a genuine control parameter of dynamics in simple glass formers. Nat. Commun. 10, 5596 (2019).

    ADS 
    MATH 

    Google Scholar 

  • Lačević, N., Starr, F. W., Schrøder, T. B. & Glotzer, S. C. Spatially heterogeneous dynamics investigated via a time-dependent four-point density correlation function. J. Chem. Phys. 119, 7372–7387 (2003).

    ADS 
    MATH 

    Google Scholar 

  • Flenner, E., Zhang, M. & Szamel, G. Analysis of a growing dynamic length scale in a glass-forming binary hard-sphere mixture. Phys. Rev. E 83, 051501 (2011).

    ADS 
    MATH 

    Google Scholar 

  • Jiang, X., Tian, Z., Li, K. & Hu, W. A geometry-enhanced graph neural network for learning the smoothness of glassy dynamics from static structure. J. Chem. Phys. 159, 144504 (2023).

    ADS 
    MATH 

    Google Scholar 

  • Swendsen, R. H. & Wang, J.-S. Replica Monte Carlo simulation of spin-glasses. Phys. Rev. Lett. 57, 2607 (1986).

    ADS 
    MathSciNet 
    MATH 

    Google Scholar 

  • Berthier, L., Coslovich, D., Ninarello, A. & Ozawa, M. Equilibrium sampling of hard spheres up to the jamming density and beyond. Phys. Rev. Lett. 116, 238002 (2016).

    ADS 
    MathSciNet 

    Google Scholar 

  • Fan, Z. & Ma, E. Predicting orientation-dependent plastic susceptibility from static structure in amorphous solids via deep learning. Nat. Commun. 12, 1506 (2021).

    ADS 
    MATH 

    Google Scholar 

  • Du, T. et al. Predicting fracture propensity in amorphous alumina from its static structure using machine learning. ACS Nano 15, 17705–17716 (2021).

    Google Scholar 

  • Font-Clos, F. et al. Predicting the failure of two-dimensional silica glasses. Nat. Commun. 13, 2820 (2022).

    ADS 
    MATH 

    Google Scholar 

  • Liu, H., Fu, Z., Yang, K., Xu, X. & Bauchy, M. Machine learning for glass science and engineering: a review. J. Non Cryst. Solids 557, 119419 (2021).

    Google Scholar 

  • Cassar, D. R. GlassNet: a multitask deep neural network for predicting many glass properties. Ceram. Int. 49, 36013–36024 (2023).

    MATH 

    Google Scholar 

  • Tandia, A., Onbasli, M. C. & Mauro, J. C. in Springer Handbook of Glass 1157–1192 (2019).

  • Merchant, A. et al. Scaling deep learning for materials discovery. Nature 624, 80–85 (2023).

    ADS 
    MATH 

    Google Scholar 

  • Unke, O. T. et al. Machine learning force fields. Chem. Rev. 121, 10142–10186 (2021).

    Google Scholar 

  • Volpe, G. et al. Roadmap on deep learning for microscopy. Preprint at https://doi.org/10.48550/arXiv.2303.03793 (2023).

  • Midtvedt, B., Pineda, J., Klein Morberg, H., Manzo, C. & Volpe, G. DeepTrack2. https://github.com/softmatterlab/DeepTrack2 (2024).

  • Gabrié, M. Mean-field inference methods for neural networks. J. Phys. A Math. Theor. 53, 223002 (2020).

    ADS 
    MathSciNet 
    MATH 

    Google Scholar 

  • Merchant, A., Metz, L., Schoenholz, S. S. & Cubuk, E. D. Learn2hop: learned optimization on rough landscapes. In International Conference on Machine Learning 7643–7653 (PMLR, 2021).

  • Gabrié, M., Ganguli, S., Lucibello, C. & Zecchina, R. Neural networks: from the perceptron to deep nets. Preprint at https://doi.org/10.48550/arXiv.2304.06636 (2023).

  • Bonnaire, T. et al. High-dimensional non-convex landscapes and gradient descent dynamics. J. Stat. Mech. 104004 (2024).

  • Mézard, M. Spin glass theory and its new challenge: structured disorder. Indian J. Phys. 98, 3757 (2023).

    MATH 

    Google Scholar 

  • Vaswani, A. Attention is all you need. In 31st Conference on Neural Information Processing Systems (NIPS, 2017).

  • Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).

    ADS 
    MATH 

    Google Scholar 

  • Baek, M. et al. Accurate prediction of protein structures and interactions using a three-track neural network. Science 373, 871–876 (2021).

    ADS 
    MATH 

    Google Scholar 

  • Bratholm, L. A. et al. A community-powered search of machine learning strategy space to find NMR property prediction models. PLoS ONE 16, e0253612 (2021).

    Google Scholar 

  • Qin, Y. et al. A dual-stage attention-based recurrent neural network for time series prediction. Preprint at https://doi.org/10.48550/arXiv.1704.02971 (2017).

  • Chapelle, O., Schölkopf, B. & Zien, A. (eds.) Semi-Supervised Learning (MIT Press, 2006).

  • Rong, Y. et al. Self-supervised graph transformer on large-scale molecular data. Adv. Neural Inf. Process. Syst. 33, 12559–12571 (2020).

    MATH 

    Google Scholar 

  • Magar, R., Wang, Y. & Barati Farimani, A. Crystal twins: self-supervised learning for crystalline material property prediction. npj Comput. Mater. 8, 231 (2022).

    ADS 
    MATH 

    Google Scholar 

  • Zhang, Z. et al. Graph self-supervised learning for optoelectronic properties of organic semiconductors. In ICML 2022 2nd AI for Science Workshop (2022).

  • Kaelbling, L. P., Littman, M. L. & Moore, A. W. Reinforcement learning: a survey. J. Artif. Intell. Res. 4, 237–285 (1996).

    MATH 

    Google Scholar 

  • Shin, K. et al. Enhancing biomolecular sampling with reinforcement learning: a tree search molecular dynamics simulation method. ACS Omega 4, 138530–13862 (2019).

    Google Scholar 

  • Fan, C. et al. Searching for spin glass ground states through deep reinforcement learning. Nat. Commun. 14, 725 (2023).

    ADS 
    MATH 

    Google Scholar 

  • Ahuja, K., Green, W. H. & Li, Y.-P. Learning to optimize molecular geometries using reinforcement learning. J. Chem. Theory Comput. 17, 818–825 (2021).

    MATH 

    Google Scholar 

  • Bihani, V., Manchanda, S., Sastry, S., Ranu, S. & Krishnan, N. A. Stridernet: a graph reinforcement learning approach to optimize atomic structures on rough energy landscapes. In International Conference on Machine Learning 2431–2451 (PMLR, 2023).

  • Bojesen, T. A. Policy-guided Monte Carlo: reinforcement-learning Markov chain dynamics. Phys. Rev. E 98, 063303 (2018).

    ADS 
    MATH 

    Google Scholar 

  • Galliano, L., Rende, R. & Coslovich, D. Policy-guided Monte Carlo on general state spaces: application to glass-forming mixtures. J. Chem. Phys. 161, 064503 (2024).

    Google Scholar 

  • Christiansen, H., Errica, F. & Alesiani, F. Self-tuning Hamiltonian Monte Carlo for accelerated sampling. J. Chem. Phys. 159, 234109 (2023).

    ADS 

    Google Scholar 

  • Gabrié, M., Rotskoff, G. M. & Vanden-Eijnden, E. Adaptive Monte Carlo augmented with normalizing flows. Proc. Natl Acad. Sci. USA 119, e2109420119 (2022).

    MathSciNet 
    MATH 

    Google Scholar 

  • Ninarello, A., Berthier, L. & Coslovich, D. Models and algorithms for the next generation of glass transition studies. Phys. Rev. X 7, 021039 (2017).

    Google Scholar 

  • Berthier, L. & Reichman, D. R. Modern computational studies of the glass transition. Nat. Rev. Phys. 5, 102–116 (2023).

    MATH 

    Google Scholar 

  • Noé, F., Olsson, S., Köhler, J. & Wu, H. Boltzmann generators: sampling equilibrium states of many-body systems with deep learning. Science 365, eaaw1147 (2019).

    ADS 
    MATH 

    Google Scholar 

  • Wu, D., Wang, L. & Zhang, P. Solving statistical mechanics using variational autoregressive networks. Phys. Rev. Lett. 122, 080602 (2019).

    ADS 
    MATH 

    Google Scholar 

  • Köhler, J., Klein, L. & Noé, F. Equivariant flows: exact likelihood generative learning for symmetric densities. In International Conference on Machine Learning 5361–5370 (PMLR, 2020).

  • Dibak, M., Klein, L., Krämer, A. & Noé, F. Temperature steerable flows and Boltzmann generators. Phys. Rev. Res. 4, L042005 (2022).

    Google Scholar 

  • Invernizzi, M., Krämer, A., Clementi, C. & Noé, F. Skipping the replica exchange ladder with normalizing flows. J. Phys. Chem. Lett. 13, 11643–11649 (2022).

    Google Scholar 

  • Xu, M. et al. Geodiff: a geometric diffusion model for molecular conformation generation. Preprint at https://doi.org/10.48550/arXiv.2203.02923 (2022).

  • Coretti, A., Falkner, S., Geissler, P. & Dellago, C. Learning mappings between equilibrium states of liquid systems using normalizing flows. Preprint at https://doi.org/10.48550/arXiv.2208.10420 (2022).

  • van Leeuwen, S., de Alba Ortíz, A. P. & Dijkstra, M. A Boltzmann generator for the isobaric-isothermal ensemble. Preprint at https://doi.org/10.48550/arXiv.2305.08483 (2023).

  • Jung, G., Biroli, G. & Berthier, L. Normalizing flows as an enhanced sampling method for atomistic supercooled liquids. Mach. Learn. Sci. Technol. 5, 035053 (2024).

    Google Scholar 

  • McNaughton, B., Milošević, M., Perali, A. & Pilati, S. Boosting Monte Carlo simulations of spin glasses using autoregressive neural networks. Phys. Rev. E 101, 053312 (2020).

    ADS 

    Google Scholar 

  • Hibat-Allah, M., Inack, E. M., Wiersema, R., Melko, R. G. & Carrasquilla, J. Variational neural annealing. Nat. Mach. Intell. 3, 952–961 (2021).

    MATH 

    Google Scholar 

  • Wu, D., Rossi, R. & Carleo, G. Unbiased Monte Carlo cluster updates with autoregressive neural networks. Phys. Rev. Res. 3, L042024 (2021).

    MATH 

    Google Scholar 

  • Inack, E. M., Morawetz, S. & Melko, R. G. Neural annealing and visualization of autoregressive neural networks in the Newman–Moore model. Condens. Matter 7, 38 (2022).

    Google Scholar 

  • Ciarella, S., Trinquier, J., Weigt, M. & Zamponi, F. Machine-learning-assisted Monte Carlo fails at sampling computationally hard problems. Mach. Learn. Sci. Technol. 4, 010501 (2023).

    ADS 

    Google Scholar 

  • Schuetz, M. J., Brubaker, J. K., Zhu, Z. & Katzgraber, H. G. Graph coloring with physics-inspired graph neural networks. Phys. Rev. Res. 4, 043131 (2022).

    Google Scholar 

  • Schuetz, M. J., Brubaker, J. K. & Katzgraber, H. G. Combinatorial optimization with physics-inspired graph neural networks. Nat. Mach. Intell. 4, 367–377 (2022).

    Google Scholar 

  • Albergo, M. S., Kanwar, G. & Shanahan, P. E. Flow-based generative models for Markov chain Monte Carlo in lattice field theory. Phys. Rev. D 100, 034515 (2019).

    ADS 
    MathSciNet 

    Google Scholar 

  • Kanwar, G. et al. Equivariant flow-based sampling for lattice gauge theory. Phys. Rev. Lett. 125, 121601 (2020).

    ADS 
    MathSciNet 
    MATH 

    Google Scholar 

  • de Haan, P., Rainone, C., Cheng, M. C. & Bondesan, R. Scaling up machine learning for quantum field theory with equivariant continuous flows. Preprint at https://doi.org/10.48550/arXiv.2110.02673 (2021).

  • Gerdes, M., de Haan, P., Rainone, C., Bondesan, R. & Cheng, M. C. Learning lattice quantum field theories with equivariant continuous flows. SciPost Phys. 15, 238 (2023).

    ADS 
    MathSciNet 
    MATH 

    Google Scholar 

  • Luo, D., Carleo, G., Clark, B. K. & Stokes, J. Gauge equivariant neural networks for quantum lattice gauge theories. Phys. Rev. Lett. 127, 276402 (2021).

    ADS 
    MathSciNet 
    MATH 

    Google Scholar 

  • Marchand, T., Ozawa, M., Biroli, G. & Mallat, S. Wavelet conditional renormalization group. Preprint at https://doi.org/10.48550/arXiv.2207.04941 (2022).

  • Angelini, M. C. & Ricci-Tersenghi, F. Modern graph neural networks do worse than classical greedy algorithms in solving combinatorial optimization problems like maximum independent set. Nat. Mach. Intell. 5, 29–31 (2023).

    MATH 

    Google Scholar 

  • Boettcher, S. Inability of a graph neural network heuristic to outperform greedy algorithms in solving combinatorial optimization problems. Nat. Mach. Intell. 5, 24–25 (2023).

    MATH 

    Google Scholar 

  • Boettcher, S. Deep reinforced learning heuristic tested on spin-glass ground states: the larger picture. Nat. Commun. 14, 5658 (2023).

    ADS 
    MATH 

    Google Scholar 

  • Ghio, D., Dandi, Y., Krzakala, F. & Zdeborová, L. Sampling with flows, diffusion and autoregressive neural networks: a spin-glass perspective. Proc. Natl Acad. Sci. USA 121, e2311810121 (2024).

    MATH 

    Google Scholar 



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