---
res:
  bibo_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.@eng'
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Carl Peter
      foaf_name: Goodrich, Carl Peter
      foaf_surname: Goodrich
      foaf_workInfoHomepage: http://www.librecat.org/personId=EB352CD2-F68A-11E9-89C5-A432E6697425
    orcid: 0000-0002-1307-5074
  - foaf_Person:
      foaf_givenName: Ella M.
      foaf_name: King, Ella M.
      foaf_surname: King
  - foaf_Person:
      foaf_givenName: Samuel S.
      foaf_name: Schoenholz, Samuel S.
      foaf_surname: Schoenholz
  - foaf_Person:
      foaf_givenName: Ekin D.
      foaf_name: Cubuk, Ekin D.
      foaf_surname: Cubuk
  - foaf_Person:
      foaf_givenName: Michael P.
      foaf_name: Brenner, Michael P.
      foaf_surname: Brenner
  bibo_doi: 10.1073/pnas.2024083118
  bibo_issue: '10'
  bibo_volume: 118
  dct_date: 2021^xs_gYear
  dct_identifier:
  - UT:000627429100097
  dct_isPartOf:
  - http://id.crossref.org/issn/0027-8424
  - http://id.crossref.org/issn/1091-6490
  dct_language: eng
  dct_publisher: National Academy of Sciences@
  dct_title: Designing self-assembling kinetics with differentiable statistical physics
    models@
  fabio_hasPubmedId: '33653960'
...