Paul M. Dodd
University of Michigan
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Featured researches published by Paul M. Dodd.
Proceedings of the National Academy of Sciences of the United States of America | 2018
Paul M. Dodd; Pablo F. Damasceno; Sharon C. Glotzer
Significance What makes an object successful at thermal folding? Protein scientists study how sequence affects the pathways by which chained amino acids fold and the structures into which they fold. Here we investigate the inverse problem: Starting with a 3D object as a polyhedron we ask, which ones, among the many choices of 2D unfoldings, are able to fold most consistently? We find that these “nets” follow a universal balance between entropy loss and potential energy gain, allowing us to explain why some of their geometrical attributes (such as compactness) represent a good predictor for the folding propensity of a given shape. Our results can be used to guide the stochastic folding of nanoscale objects into drug-delivery devices and thermally folded robots. Low-dimensional objects such as molecular strands, ladders, and sheets have intrinsic features that affect their propensity to fold into 3D objects. Understanding this relationship remains a challenge for de novo design of functional structures. Using molecular dynamics simulations, we investigate the refolding of the 24 possible 2D unfoldings (“nets”) of the three simplest Platonic shapes and demonstrate that attributes of a net’s topology—net compactness and leaves on the cutting graph—correlate with thermodynamic folding propensity. To explain these correlations we exhaustively enumerate the pathways followed by nets during folding and identify a crossover temperature Tx below which nets fold via nonnative contacts (bonds must break before the net can fold completely) and above which nets fold via native contacts (newly formed bonds are also present in the folded structure). Folding above Tx shows a universal balance between reduction of entropy via the elimination of internal degrees of freedom when bonds are formed and gain in potential energy via local, cooperative edge binding. Exploiting this universality, we devised a numerical method to efficiently compute all high-temperature folding pathways for any net, allowing us to predict, among the combined 86,760 nets for the remaining Platonic solids, those with highest folding propensity. Our results provide a general heuristic for the design of 2D objects to stochastically fold into target 3D geometries and suggest a mechanism by which geometry and folding propensity are related above Tx, where native bonds dominate folding.
Proceedings of the National Academy of Sciences of the United States of America | 2018
Rose K. Cersonsky; Greg van Anders; Paul M. Dodd; Sharon C. Glotzer
Significance Understanding how structural order forms in matter is a key challenge in designing materials. In the 1920s, Pauling proposed packing as a mechanism for driving structural order based on observed correlations between the structure of crystals and the mathematical packing of hard spheres. We study the ordering of several systems of hard colloids in which structural order correlates with mathematical packing and find, surprisingly, that structural order cannot arise from packing. Our approach provides statistical mechanics approaches for investigating the mathematics of packing and raises questions about the role of packing in determining the structural order of matter. Since the 1920s, packing arguments have been used to rationalize crystal structures in systems ranging from atomic mixtures to colloidal crystals. Packing arguments have recently been applied to complex nanoparticle structures, where they often, but not always, work. We examine when, if ever, packing is a causal mechanism in hard particle approximations of colloidal crystals. We investigate three crystal structures composed of their ideal packing shapes. We show that, contrary to expectations, the ordering mechanism cannot be packing, even when the thermodynamically self-assembled structure is the same as that of the densest packing. We also show that the best particle shapes for hard particle colloidal crystals at any finite pressure are imperfect versions of the ideal packing shape.
Computational Materials Science | 2018
Carl S. Adorf; Paul M. Dodd; Vyas Ramasubramani; Sharon C. Glotzer
Researchers in the field of computational physics, chemistry, and materials science are regularly posed with the challenge of managing large and heterogeneous data spaces. The amount of data increases in lockstep with computational efficiency multiplied by the amount of available computational resources, which shifts the bottleneck within the scientific process from data acquisition to data post-processing and analysis. We present a framework designed to aid in the integration of various specialized data formats, tools and workflows. The signac framework provides all basic components required to create a well-defined and thus collectively accessible data space, simplifying data access and modification through a homogeneous data interface, largely agnostic of the data source, i.e., computation or experiment. The framework’s data model is designed not to require absolute commitment to the presented implementation, simplifying adaption into existing data sets and workflows. This approach not only increases the efficiency with which scientific results can be produced, but also significantly lowers barriers for collaborations requiring shared data access. E-mail addresses: csadorf@umich.edu, pdodd@umich.edu, sglotzer@umich.edu. 1 ar X iv :1 61 1. 03 54 3v 1 [ cs .D B ] 1 0 N ov 2 01 6 2 SIGNAC: A SIMPLE DATA MANAGEMENT FRAMEWORK
Nature Materials | 2015
Terry Shyu; Pablo F. Damasceno; Paul M. Dodd; Aaron Lamoureux; Lizhi Xu; Matthew Shlian; Max Shtein; Sharon C. Glotzer; Nicholas A. Kotov
Physical Review Letters | 2013
Tyler B. Martin; Paul M. Dodd; Arthi Jayaraman
Journal of Polymer Science Part B | 2012
Paul M. Dodd; Arthi Jayaraman
ACS Nano | 2015
Greg van Anders; Daphne Klotsa; Andrew S. Karas; Paul M. Dodd; Sharon C. Glotzer
Archive | 2017
Yina Geng; Greg van Anders; Paul M. Dodd; Julia Dshemuchadse; Sharon C. Glotzer
Archive | 2016
Carl S. Adorf; Paul M. Dodd; Sharon C. Glotzer
arXiv: Soft Condensed Matter | 2015
Greg van Anders; Daphne Klotsa; Andrew S. Karas; Paul M. Dodd; Sharon C. Glotzer