article published yes Carl Peter Goodrich author EB352CD2-F68A-11E9-89C5-A432E66974250000-0002-1307-5074 Ella M. King author Samuel S. Schoenholz author Ekin D. Cubuk author Michael P. Brenner author CaGo department The inverse problem of designing component interactions to target emergent structure is fundamental to numerous applications in biotechnology, materials science, and statistical physics. Equally important is the inverse problem of designing emergent kinetics, but this has received considerably less attention. Using recent advances in automatic differentiation, we show how kinetic pathways can be precisely designed by directly differentiating through statistical physics models, namely free energy calculations and molecular dynamics simulations. We consider two systems that are crucial to our understanding of structural self-assembly: bulk crystallization and small nanoclusters. In each case, we are able to assemble precise dynamical features. Using gradient information, we manipulate interactions among constituent particles to tune the rate at which these systems yield specific structures of interest. Moreover, we use this approach to learn nontrivial features about the high-dimensional design space, allowing us to accurately predict when multiple kinetic features can be simultaneously and independently controlled. These results provide a concrete and generalizable foundation for studying nonstructural self-assembly, including kinetic properties as well as other complex emergent properties, in a vast array of systems. https://research-explorer.ista.ac.at/download/9257/9278/2021_PNAS_Goodrich.pdf application/pdfno https://creativecommons.org/licenses/by-nc-nd/4.0/ National Academy of Sciences2021 eng 0027-8424 1091-6490 33653960 00062742910009710.1073/pnas.2024083118 11810 Goodrich CP, King EM, Schoenholz SS, Cubuk ED, Brenner MP. Designing self-assembling kinetics with differentiable statistical physics models. <i>Proceedings of the National Academy of Sciences of the United States of America</i>. 2021;118(10). doi:<a href="https://doi.org/10.1073/pnas.2024083118">10.1073/pnas.2024083118</a> Goodrich, Carl Peter, Ella M. King, Samuel S. Schoenholz, Ekin D. Cubuk, and Michael P. Brenner. “Designing Self-Assembling Kinetics with Differentiable Statistical Physics Models.” <i>Proceedings of the National Academy of Sciences of the United States of America</i>. National Academy of Sciences, 2021. <a href="https://doi.org/10.1073/pnas.2024083118">https://doi.org/10.1073/pnas.2024083118</a>. C. P. Goodrich, E. M. King, S. S. Schoenholz, E. D. Cubuk, and M. P. Brenner, “Designing self-assembling kinetics with differentiable statistical physics models,” <i>Proceedings of the National Academy of Sciences of the United States of America</i>, vol. 118, no. 10. National Academy of Sciences, 2021. Goodrich, C. P., King, E. M., Schoenholz, S. S., Cubuk, E. D., &#38; Brenner, M. P. (2021). Designing self-assembling kinetics with differentiable statistical physics models. <i>Proceedings of the National Academy of Sciences of the United States of America</i>. National Academy of Sciences. <a href="https://doi.org/10.1073/pnas.2024083118">https://doi.org/10.1073/pnas.2024083118</a> Goodrich CP, King EM, Schoenholz SS, Cubuk ED, Brenner MP. 2021. Designing self-assembling kinetics with differentiable statistical physics models. Proceedings of the National Academy of Sciences of the United States of America. 118(10), e2024083118. Goodrich, Carl Peter, et al. “Designing Self-Assembling Kinetics with Differentiable Statistical Physics Models.” <i>Proceedings of the National Academy of Sciences of the United States of America</i>, vol. 118, no. 10, e2024083118, National Academy of Sciences, 2021, doi:<a href="https://doi.org/10.1073/pnas.2024083118">10.1073/pnas.2024083118</a>. C.P. Goodrich, E.M. King, S.S. Schoenholz, E.D. Cubuk, M.P. Brenner, Proceedings of the National Academy of Sciences of the United States of America 118 (2021). 92572021-03-21T23:01:20Z2025-05-14T10:58:42Z