@article{14710,
  abstract     = {The self-assembly of complex structures from a set of non-identical building blocks is a hallmark of soft matter and biological systems, including protein complexes, colloidal clusters, and DNA-based assemblies. Predicting the dependence of the equilibrium assembly yield on the concentrations and interaction energies of building blocks is highly challenging, owing to the difficulty of computing the entropic contributions to the free energy of the many structures that compete with the ground state configuration. While these calculations yield well known results for spherically symmetric building blocks, they do not hold when the building blocks have internal rotational degrees of freedom. Here we present an approach for solving this problem that works with arbitrary building blocks, including proteins with known structure and complex colloidal building blocks. Our algorithm combines classical statistical mechanics with recently developed computational tools for automatic differentiation. Automatic differentiation allows efficient evaluation of equilibrium averages over configurations that would otherwise be intractable. We demonstrate the validity of our framework by comparison to molecular dynamics simulations of simple examples, and apply it to calculate the yield curves for known protein complexes and for the assembly of colloidal shells.},
  author       = {Curatolo, Agnese I. and Kimchi, Ofer and Goodrich, Carl Peter and Krueger, Ryan K. and Brenner, Michael P.},
  issn         = {20411723},
  journal      = {Nature Communications},
  publisher    = {Springer Nature},
  title        = {{A computational toolbox for the assembly yield of complex and heterogeneous structures}},
  doi          = {10.1038/s41467-023-43168-4},
  volume       = {14},
  year         = {2023},
}

@article{9257,
  abstract     = {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.},
  author       = {Goodrich, Carl Peter and King, Ella M. and Schoenholz, Samuel S. and Cubuk, Ekin D. and Brenner, Michael P.},
  issn         = {1091-6490},
  journal      = {Proceedings of the National Academy of Sciences},
  number       = {10},
  publisher    = {National Academy of Sciences},
  title        = {{Designing self-assembling kinetics with differentiable statistical physics models}},
  doi          = {10.1073/pnas.2024083118},
  volume       = {118},
  year         = {2021},
}

@phdthesis{10422,
  abstract     = {Those who aim to devise new materials with desirable properties usually examine present methods first. However, they will find out that some approaches can exist only conceptually without high chances to become practically useful. It seems that a numerical technique called automatic differentiation together with increasing supply of computational accelerators will soon shift many methods of the material design from the category ”unimaginable” to the category ”expensive but possible”. Approach we suggest is not an exception. Our overall goal is to have an efficient and generalizable approach allowing to solve inverse design problems. In this thesis we scratch its surface. We consider jammed systems of identical particles. And ask ourselves how the shape of those particles (or the parameters codifying it) may affect mechanical properties of the system. An indispensable part of reaching the answer is an appropriate particle parametrization. We come up with a simple, yet generalizable and purposeful scheme for it. Using our generalizable shape parameterization, we simulate the formation of a solid composed of pentagonal-like particles and measure anisotropy in the resulting elastic response. Through automatic differentiation techniques, we directly connect the shape parameters with the elastic response. Interestingly, for our system we find that less isotropic particles lead to a more isotropic elastic response. Together with other results known about our method it seems that it can be successfully generalized for different inverse design problems.},
  author       = {Piankov, Anton},
  issn         = {2791-4585},
  publisher    = {Institute of Science and Technology Austria},
  title        = {{Towards designer materials using customizable particle shape}},
  doi          = {10.15479/at:ista:10422},
  year         = {2021},
}