--- _id: '14710' abstract: - lang: eng text: 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. acknowledgement: 'We thank Lucy Colwell for suggesting that we use covariance based methods to predict contacts and Yang Hsia, Scott Boyken, Zibo Chen, and David Baker for collaborations on designed protein complexes. We also thank Ned Wingreen for suggesting the alternative derivation of (11). This research was supported by the Office of Naval Research through ONR N00014-17-1-3029, the Simons Foundation the NSF-Simons Center for Mathematical and Statistical Analysis of Biology at Harvard (award number #1764269), the Peter B. Lewis ’55 Lewis-Sigler Institute/Genomics Fund through the Lewis-Sigler Institute of Integrative Genomics at Princeton University, and the National Science Foundation through the Center for the Physics of Biological Function (PHY-1734030).' article_number: '8328' article_processing_charge: Yes article_type: original author: - first_name: Agnese I. full_name: Curatolo, Agnese I. last_name: Curatolo - first_name: Ofer full_name: Kimchi, Ofer last_name: Kimchi - first_name: Carl Peter full_name: Goodrich, Carl Peter id: EB352CD2-F68A-11E9-89C5-A432E6697425 last_name: Goodrich orcid: 0000-0002-1307-5074 - first_name: Ryan K. full_name: Krueger, Ryan K. last_name: Krueger - first_name: Michael P. full_name: Brenner, Michael P. last_name: Brenner citation: ama: Curatolo AI, Kimchi O, Goodrich CP, Krueger RK, Brenner MP. A computational toolbox for the assembly yield of complex and heterogeneous structures. Nature Communications. 2023;14. doi:10.1038/s41467-023-43168-4 apa: Curatolo, A. I., Kimchi, O., Goodrich, C. P., Krueger, R. K., & Brenner, M. P. (2023). A computational toolbox for the assembly yield of complex and heterogeneous structures. Nature Communications. Springer Nature. https://doi.org/10.1038/s41467-023-43168-4 chicago: Curatolo, Agnese I., Ofer Kimchi, Carl Peter Goodrich, Ryan K. Krueger, and Michael P. Brenner. “A Computational Toolbox for the Assembly Yield of Complex and Heterogeneous Structures.” Nature Communications. Springer Nature, 2023. https://doi.org/10.1038/s41467-023-43168-4. ieee: A. I. Curatolo, O. Kimchi, C. P. Goodrich, R. K. Krueger, and M. P. Brenner, “A computational toolbox for the assembly yield of complex and heterogeneous structures,” Nature Communications, vol. 14. Springer Nature, 2023. ista: Curatolo AI, Kimchi O, Goodrich CP, Krueger RK, Brenner MP. 2023. A computational toolbox for the assembly yield of complex and heterogeneous structures. Nature Communications. 14, 8328. mla: Curatolo, Agnese I., et al. “A Computational Toolbox for the Assembly Yield of Complex and Heterogeneous Structures.” Nature Communications, vol. 14, 8328, Springer Nature, 2023, doi:10.1038/s41467-023-43168-4. short: A.I. Curatolo, O. Kimchi, C.P. Goodrich, R.K. Krueger, M.P. Brenner, Nature Communications 14 (2023). date_created: 2023-12-24T23:00:53Z date_published: 2023-12-01T00:00:00Z date_updated: 2024-01-02T11:36:46Z day: '01' ddc: - '530' department: - _id: CaGo doi: 10.1038/s41467-023-43168-4 file: - access_level: open_access checksum: fd9e9d527c2691f03fbc24031a75a3b3 content_type: application/pdf creator: kschuh date_created: 2023-12-27T08:40:43Z date_updated: 2023-12-27T08:40:43Z file_id: '14714' file_name: 2023_NatureComm_Curatolo.pdf file_size: 1342319 relation: main_file success: 1 file_date_updated: 2023-12-27T08:40:43Z has_accepted_license: '1' intvolume: ' 14' language: - iso: eng month: '12' oa: 1 oa_version: Published Version publication: Nature Communications publication_identifier: eissn: - '20411723' publication_status: published publisher: Springer Nature quality_controlled: '1' scopus_import: '1' status: public title: A computational toolbox for the assembly yield of complex and heterogeneous structures tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 14 year: '2023' ... --- _id: '9257' abstract: - lang: eng text: '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.' acknowledgement: We thank Agnese Curatolo, Megan Engel, Ofer Kimchi, Seong Ho Pahng, and Roy Frostig for helpful discussions. This material is based on work supported by NSF Graduate Research Fellowship Grant DGE1745303. This research was funded by NSF Grant DMS-1715477, Materials Research Science and Engineering Centers Grant DMR-1420570, and Office of Naval Research Grant N00014-17-1-3029. M.P.B. is an investigator of the Simons Foundation. article_number: e2024083118 article_processing_charge: No article_type: original author: - first_name: Carl Peter full_name: Goodrich, Carl Peter id: EB352CD2-F68A-11E9-89C5-A432E6697425 last_name: Goodrich orcid: 0000-0002-1307-5074 - first_name: Ella M. full_name: King, Ella M. last_name: King - first_name: Samuel S. full_name: Schoenholz, Samuel S. last_name: Schoenholz - first_name: Ekin D. full_name: Cubuk, Ekin D. last_name: Cubuk - first_name: Michael P. full_name: Brenner, Michael P. last_name: Brenner citation: ama: Goodrich CP, King EM, Schoenholz SS, Cubuk ED, Brenner MP. Designing self-assembling kinetics with differentiable statistical physics models. Proceedings of the National Academy of Sciences. 2021;118(10). doi:10.1073/pnas.2024083118 apa: Goodrich, C. P., King, E. M., Schoenholz, S. S., Cubuk, E. D., & Brenner, M. P. (2021). Designing self-assembling kinetics with differentiable statistical physics models. Proceedings of the National Academy of Sciences. National Academy of Sciences. https://doi.org/10.1073/pnas.2024083118 chicago: 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.” Proceedings of the National Academy of Sciences. National Academy of Sciences, 2021. https://doi.org/10.1073/pnas.2024083118. ieee: 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,” Proceedings of the National Academy of Sciences, vol. 118, no. 10. National Academy of Sciences, 2021. ista: 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. 118(10), e2024083118. mla: Goodrich, Carl Peter, et al. “Designing Self-Assembling Kinetics with Differentiable Statistical Physics Models.” Proceedings of the National Academy of Sciences, vol. 118, no. 10, e2024083118, National Academy of Sciences, 2021, doi:10.1073/pnas.2024083118. short: C.P. Goodrich, E.M. King, S.S. Schoenholz, E.D. Cubuk, M.P. Brenner, Proceedings of the National Academy of Sciences 118 (2021). date_created: 2021-03-21T23:01:20Z date_published: 2021-03-09T00:00:00Z date_updated: 2023-08-07T14:19:34Z day: '09' ddc: - '530' department: - _id: CaGo doi: 10.1073/pnas.2024083118 external_id: isi: - '000627429100097' pmid: - '33653960' file: - access_level: open_access checksum: 5be8da2b1c0757feb1057f1a515cf9e0 content_type: application/pdf creator: dernst date_created: 2021-03-22T12:23:54Z date_updated: 2021-03-22T12:23:54Z file_id: '9278' file_name: 2021_PNAS_Goodrich.pdf file_size: 1047954 relation: main_file success: 1 file_date_updated: 2021-03-22T12:23:54Z has_accepted_license: '1' intvolume: ' 118' isi: 1 issue: '10' language: - iso: eng month: '03' oa: 1 oa_version: Published Version pmid: 1 publication: Proceedings of the National Academy of Sciences publication_identifier: eissn: - 1091-6490 issn: - 0027-8424 publication_status: published publisher: National Academy of Sciences quality_controlled: '1' scopus_import: '1' status: public title: Designing self-assembling kinetics with differentiable statistical physics models tmp: image: /images/cc_by_nc_nd.png legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) short: CC BY-NC-ND (4.0) type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 118 year: '2021' ... --- _id: '10422' abstract: - lang: eng text: 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. alternative_title: - ISTA Master's Thesis article_processing_charge: No author: - first_name: Anton full_name: Piankov, Anton id: 865E3C26-AA8C-11E9-A409-C4C4E5697425 last_name: Piankov citation: ama: Piankov A. Towards designer materials using customizable particle shape. 2021. doi:10.15479/at:ista:10422 apa: Piankov, A. (2021). Towards designer materials using customizable particle shape. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:10422 chicago: Piankov, Anton. “Towards Designer Materials Using Customizable Particle Shape.” Institute of Science and Technology Austria, 2021. https://doi.org/10.15479/at:ista:10422. ieee: A. Piankov, “Towards designer materials using customizable particle shape,” Institute of Science and Technology Austria, 2021. ista: Piankov A. 2021. Towards designer materials using customizable particle shape. Institute of Science and Technology Austria. mla: Piankov, Anton. Towards Designer Materials Using Customizable Particle Shape. Institute of Science and Technology Austria, 2021, doi:10.15479/at:ista:10422. short: A. Piankov, Towards Designer Materials Using Customizable Particle Shape, Institute of Science and Technology Austria, 2021. date_created: 2021-12-07T10:48:06Z date_published: 2021-12-07T00:00:00Z date_updated: 2023-09-07T13:34:12Z day: '07' ddc: - '530' degree_awarded: MS department: - _id: GradSch - _id: CaGo doi: 10.15479/at:ista:10422 file: - access_level: closed checksum: 114e8f4b2c002c6c352416c12de2c695 content_type: application/x-zip-compressed creator: cchlebak date_created: 2021-12-07T11:13:52Z date_updated: 2022-03-10T12:10:25Z file_id: '10424' file_name: Thesis.zip file_size: 394018 relation: source_file - access_level: closed checksum: cd15ae991ced352a9959815f794e657c content_type: application/vnd.openxmlformats-officedocument.wordprocessingml.document creator: cchlebak date_created: 2021-12-07T11:14:01Z date_updated: 2022-03-10T12:10:25Z file_id: '10425' file_name: Preliminary_pages_Piankov.docx file_size: 47638 relation: source_file - access_level: open_access checksum: e6899c798b75ba42fab9822bce309050 content_type: application/pdf creator: cchlebak date_created: 2021-12-07T11:20:35Z date_updated: 2021-12-07T11:20:35Z file_id: '10426' file_name: 2021_Piankov_combined.pdf file_size: 484965 relation: main_file success: 1 file_date_updated: 2022-03-10T12:10:25Z has_accepted_license: '1' language: - iso: eng month: '12' oa: 1 oa_version: Published Version publication_identifier: issn: - 2791-4585 publication_status: published publisher: Institute of Science and Technology Austria status: public supervisor: - first_name: Carl Peter full_name: Goodrich, Carl Peter id: EB352CD2-F68A-11E9-89C5-A432E6697425 last_name: Goodrich orcid: 0000-0002-1307-5074 title: Towards designer materials using customizable particle shape type: dissertation user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 year: '2021' ...