1
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Niblo JK, Swartley JR, Zhang Z, DuBay KH. 2D capsid formation within an oscillatory energy landscape: orderly self-assembly depends on the interplay between a dynamic potential and intrinsic relaxation times. SOFT MATTER 2024; 20:6702-6713. [PMID: 39046256 DOI: 10.1039/d4sm00455h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/25/2024]
Abstract
Multiple dissipative self-assembly protocols designed to create novel structures or to reduce kinetic traps have recently emerged. Specifically, temporal oscillations of particle interactions have been shown effective at both aims, but investigations thus far have focused on systems of simple colloids or their binary mixtures. In this work, we expand our understanding of the effect of temporally oscillating interactions to a two-dimensional coarse-grained viral capsid-like model that undergoes a self-limited assembly. This model includes multiple intrinsic relaxation times due to the internal structure of the capsid subunits and, under certain interaction regimes, proceeds via a two-step nucleation mechanism. We find that oscillations much faster than the local intrinsic relaxation times can be described via a time averaged inter-particle potential across a wide range of interaction strengths, while oscillations much slower than these relaxation times result in structures that adapt to the attraction strength of the current half-cycle. Interestingly, oscillation periods similar to these relaxation times shift the interaction window over which orderly assembly occurs by enabling error correction during the half-cycles with weaker attractions. Our results provide fundamental insights to non-equilibrium self-assembly on temporally variant energy landscapes.
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Affiliation(s)
- Jessica K Niblo
- Department of Chemistry, University of Virginia, McCormick Road, PO Box 400319, Charlottesville, VA 22903-4319, USA.
| | - Jacob R Swartley
- Department of Chemistry, University of Virginia, McCormick Road, PO Box 400319, Charlottesville, VA 22903-4319, USA.
| | - Zhongmin Zhang
- Department of Chemistry, University of North Carolina at Chapel Hill, Campus Box 3290, Chapel Hill, NC 27599-3290, USA
| | - Kateri H DuBay
- Department of Chemistry, University of Virginia, McCormick Road, PO Box 400319, Charlottesville, VA 22903-4319, USA.
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2
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Liu H, Matthies M, Russo J, Rovigatti L, Narayanan RP, Diep T, McKeen D, Gang O, Stephanopoulos N, Sciortino F, Yan H, Romano F, Šulc P. Inverse design of a pyrochlore lattice of DNA origami through model-driven experiments. Science 2024; 384:776-781. [PMID: 38753798 DOI: 10.1126/science.adl5549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 03/19/2024] [Indexed: 05/18/2024]
Abstract
Sophisticated statistical mechanics approaches and human intuition have demonstrated the possibility of self-assembling complex lattices or finite-size constructs. However, attempts so far have mostly only been successful in silico and often fail in experiment because of unpredicted traps associated with kinetic slowing down (gelation, glass transition) and competing ordered structures. Theoretical predictions also face the difficulty of encoding the desired interparticle interaction potential with the experimentally available nano- and micrometer-sized particles. To overcome these issues, we combine SAT assembly (a patchy-particle interaction design algorithm based on constrained optimization) with coarse-grained simulations of DNA nanotechnology to experimentally realize trap-free self-assembly pathways. We use this approach to assemble a pyrochlore three-dimensional lattice, coveted for its promise in the construction of optical metamaterials, and characterize it with small-angle x-ray scattering and scanning electron microscopy visualization.
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Affiliation(s)
- Hao Liu
- School of Molecular Sciences and Center for Molecular Design and Biomimetics, The Biodesign Institute, Arizona State University, 1001 South McAllister Avenue, Tempe, AZ 85281, USA
| | - Michael Matthies
- School of Molecular Sciences and Center for Molecular Design and Biomimetics, The Biodesign Institute, Arizona State University, 1001 South McAllister Avenue, Tempe, AZ 85281, USA
| | - John Russo
- Dipartimento di Fisica, Sapienza Università di Roma, P.le Aldo Moro 5, 00185 Rome, Italy
| | - Lorenzo Rovigatti
- Dipartimento di Fisica, Sapienza Università di Roma, P.le Aldo Moro 5, 00185 Rome, Italy
| | - Raghu Pradeep Narayanan
- School of Molecular Sciences and Center for Molecular Design and Biomimetics, The Biodesign Institute, Arizona State University, 1001 South McAllister Avenue, Tempe, AZ 85281, USA
- Department of Cellular and Molecular Pharmacology, University of California-San Francisco, San Francisco, CA 94143, USA
| | - Thong Diep
- School of Molecular Sciences and Center for Molecular Design and Biomimetics, The Biodesign Institute, Arizona State University, 1001 South McAllister Avenue, Tempe, AZ 85281, USA
| | - Daniel McKeen
- Department of Chemical Engineering, Columbia University, 817 SW Mudd, New York, NY 10027, USA
| | - Oleg Gang
- Department of Chemical Engineering, Columbia University, 817 SW Mudd, New York, NY 10027, USA
- Department of Applied Physics and Applied Mathematics, Columbia University, New York, NY 10027, USA
- Center for Functional Nanomaterials, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - Nicholas Stephanopoulos
- School of Molecular Sciences and Center for Molecular Design and Biomimetics, The Biodesign Institute, Arizona State University, 1001 South McAllister Avenue, Tempe, AZ 85281, USA
| | - Francesco Sciortino
- Dipartimento di Fisica, Sapienza Università di Roma, P.le Aldo Moro 5, 00185 Rome, Italy
| | - Hao Yan
- School of Molecular Sciences and Center for Molecular Design and Biomimetics, The Biodesign Institute, Arizona State University, 1001 South McAllister Avenue, Tempe, AZ 85281, USA
| | - Flavio Romano
- Department of Molecular Sciences and Nanosystems, Ca' Foscari University of Venice, Via Torino 155, 30171 Venezia-Mestre, Italy
- European Centre for Living Technology (ECLT), Ca' Bottacin, 3911 Dorsoduro Calle Crosera, 30123 Venice, Italy
| | - Petr Šulc
- School of Molecular Sciences and Center for Molecular Design and Biomimetics, The Biodesign Institute, Arizona State University, 1001 South McAllister Avenue, Tempe, AZ 85281, USA
- School of Natural Sciences, Department of Bioscience, Technical University Munich, 85748 Garching, Germany
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3
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Evans J, Šulc P. Designing 3D multicomponent self-assembling systems with signal-passing building blocks. J Chem Phys 2024; 160:084902. [PMID: 38385517 DOI: 10.1063/5.0191282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 01/26/2024] [Indexed: 02/23/2024] Open
Abstract
We introduce an allostery-mimetic building block model for the self-assembly of 3D structures. We represent the building blocks as patchy particles, where each binding site (patch) can be irreversibly activated or deactivated by binding of the particle's other controlling patches to another particle. We show that these allostery-mimetic systems can be designed to increase yields of target structures by disallowing misassembled states and can further decrease the smallest number of distinct species needed to assemble a target structure. Next, we show applications to design a programmable nanoparticle swarm for multifarious assembly: a system of particles that stores multiple possible target structures and a particular structure is recalled by presenting an external trigger signal. Finally, we outline a possible pathway for realization of such structures at nanoscale using DNA nanotechnology devices.
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Affiliation(s)
- Joshua Evans
- School of Molecular Sciences and Center for Molecular Design and Biomimetics, The Biodesign Institute, Arizona State University, 1001 South McAllister Avenue, Tempe, Arizona 85281, USA
| | - Petr Šulc
- School of Molecular Sciences and Center for Molecular Design and Biomimetics, The Biodesign Institute, Arizona State University, 1001 South McAllister Avenue, Tempe, Arizona 85281, USA
- School of Natural Sciences, Department of Bioscience, Technical University Munich, 85748 Garching, Germany
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4
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Beneduce C, Sciortino F, Šulc P, Russo J. Engineering Azeotropy to Optimize the Self-Assembly of Colloidal Mixtures. ACS NANO 2023; 17:24841-24853. [PMID: 38048489 PMCID: PMC10753881 DOI: 10.1021/acsnano.3c05569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 11/08/2023] [Accepted: 11/09/2023] [Indexed: 12/06/2023]
Abstract
The goal of inverse self-assembly is to design interparticle interactions capable of assembling the units into a desired target structure. The effective assembly of complex structures often requires the use of multiple components, each new component increasing the thermodynamic degrees of freedom and, hence, the complexity of the self-assembly pathway. In this work we explore the possibility to use azeotropy, i.e., a special thermodynamic condition where the system behaves effectively as a one-component system, as a way to control the self-assembly of an arbitrary number of components. Exploiting the mass-balance equations, we show how to select patchy particle systems that exhibit azeotropic points along the desired self-assembly pathway. As an example we map the phase diagram of a binary mixture that, by design, fully assembles into cubic (and only cubic) diamond crystal via an azeotropic point. The ability to explicitly include azeotropic points in artificial designs reveals effective pathways for the self-assembly of complex structures.
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Affiliation(s)
- Camilla Beneduce
- Dipartimento
di Fisica, Sapienza Università di
Roma, P.le Aldo Moro 5, 00185 Rome, Italy
| | - Francesco Sciortino
- Dipartimento
di Fisica, Sapienza Università di
Roma, P.le Aldo Moro 5, 00185 Rome, Italy
| | - Petr Šulc
- School
of Molecular Sciences and Center for Molecular Design and Biomimetics,
The Biodesign Institute, Arizona State University, 1001 South McAllister Avenue, Tempe, Arizona 85281, United States
- School
of Natural Sciences, Department of Bioscience, TU Munich, Am Coulombwall
4a, 85748, Garching, Germany
| | - John Russo
- Dipartimento
di Fisica, Sapienza Università di
Roma, P.le Aldo Moro 5, 00185 Rome, Italy
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5
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Schmid SY, Lachowski K, Chiang HT, Pozzo L, De Yoreo J, Zhang S. Mechanisms of Biomolecular Self-Assembly Investigated Through In Situ Observations of Structures and Dynamics. Angew Chem Int Ed Engl 2023; 62:e202309725. [PMID: 37702227 DOI: 10.1002/anie.202309725] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Indexed: 09/14/2023]
Abstract
Biomolecular self-assembly of hierarchical materials is a precise and adaptable bottom-up approach to synthesizing across scales with considerable energy, health, environment, sustainability, and information technology applications. To achieve desired functions in biomaterials, it is essential to directly observe assembly dynamics and structural evolutions that reflect the underlying energy landscape and the assembly mechanism. This review will summarize the current understanding of biomolecular assembly mechanisms based on in situ characterization and discuss the broader significance and achievements of newly gained insights. In addition, we will also introduce how emerging deep learning/machine learning-based approaches, multiparametric characterization, and high-throughput methods can boost the development of biomolecular self-assembly. The objective of this review is to accelerate the development of in situ characterization approaches for biomolecular self-assembly and to inspire the next generation of biomimetic materials.
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Affiliation(s)
- Sakshi Yadav Schmid
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Kacper Lachowski
- Chemical Engineering, University of Washington, Seattle, WA 98105, USA
- Molecular Engineering and Sciences Institute, University of Washington, Seattle, WA 98105, USA
| | - Huat Thart Chiang
- Chemical Engineering, University of Washington, Seattle, WA 98105, USA
| | - Lilo Pozzo
- Chemical Engineering, University of Washington, Seattle, WA 98105, USA
- Molecular Engineering and Sciences Institute, University of Washington, Seattle, WA 98105, USA
- Materials Science and Engineering, University of Washington, Seattle, WA 98105, USA
| | - Jim De Yoreo
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
- Materials Science and Engineering, University of Washington, Seattle, WA 98105, USA
| | - Shuai Zhang
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
- Molecular Engineering and Sciences Institute, University of Washington, Seattle, WA 98105, USA
- Materials Science and Engineering, University of Washington, Seattle, WA 98105, USA
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6
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Gorbunov VA, Uliankina AI, Akimenko SS, Myshlyavtsev AV. Tensor renormalization group study of orientational ordering in simple models of adsorption monolayers. Phys Rev E 2023; 108:014133. [PMID: 37583228 DOI: 10.1103/physreve.108.014133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Accepted: 07/06/2023] [Indexed: 08/17/2023]
Abstract
A simple lattice model of the orientational ordering in organic adsorption layers that considers the directionality of intermolecular interactions is proposed. The symmetry and the number of rotational states of the adsorbed molecule are the main parameters of the model. The model takes into account both the isotropic and directional contributions to the molecule-molecule interaction potential. Using several special cases of this model, we have shown that the tensor renormalization group (TRG) approach can be successfully used for the analysis of orientational ordering in organic adsorption layers with directed intermolecular interactions. Adsorption isotherms, potential energy, and entropy have been calculated for the model adsorption layers differing in the molecule symmetry and the number of rotational states. The calculated thermodynamic characteristics show that entropy effects play a significant role in the self-assembly of dense phases of the molecular layers. All the results obtained with the TRG have been verified by the standard Monte Carlo method. The proposed model reproduces the main features of the phase behavior of the real adsorption layers of benzoic, terephthalic, and trimesic acids on a homogeneous surface of metal single crystals and graphite.
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Affiliation(s)
- V A Gorbunov
- Department of Chemistry and Chemical Engineering, Omsk State Technical University, 11 Mira Avenue, Omsk 644050, Russian Federation
| | - A I Uliankina
- Department of Chemistry and Chemical Engineering, Omsk State Technical University, 11 Mira Avenue, Omsk 644050, Russian Federation
| | - S S Akimenko
- Department of Chemistry and Chemical Engineering, Omsk State Technical University, 11 Mira Avenue, Omsk 644050, Russian Federation
| | - A V Myshlyavtsev
- Department of Chemistry and Chemical Engineering, Omsk State Technical University, 11 Mira Avenue, Omsk 644050, Russian Federation
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7
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Takahashi K, Takahashi L. Toward the Golden Age of Materials Informatics: Perspective and Opportunities. J Phys Chem Lett 2023; 14:4726-4733. [PMID: 37172318 DOI: 10.1021/acs.jpclett.3c00648] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
Materials informatics is reaching the transition point and is evolving from its early stages of adoption and development and moving toward its golden age. Here, the transformation of the early stage of materials informatics toward the next level of materials informatics is explored. In particular, it has become crucial to be able to manipulate materials synthesis data, materials properties data, and materials characterization data. Through the use of ontology, material design and understanding can be carried out simultaneously in a whitebox manner. Here, a perspective on the ultimate goal of materials informatics along with potential key components is discussed.
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Affiliation(s)
- Keisuke Takahashi
- Department of Chemistry, Hokkaido University, North 10, West 8, Sapporo 060-0810, Japan
| | - Lauren Takahashi
- Department of Chemistry, Hokkaido University, North 10, West 8, Sapporo 060-0810, Japan
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8
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McDonald MN, Zhu Q, Paxton WF, Peterson CK, Tree DR. Active control of equilibrium, near-equilibrium, and far-from-equilibrium colloidal systems. SOFT MATTER 2023; 19:1675-1694. [PMID: 36790855 DOI: 10.1039/d2sm01447e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
The development of top-down active control over bottom-up colloidal assembly processes has the potential to produce materials, surfaces, and objects with applications in a wide range of fields spanning from computing to materials science to biomedical engineering. In this review, we summarize recent progress in the field using a taxonomy based on how active control is used to guide assembly. We find there are three distinct scenarios: (1) navigating kinetic pathways to reach a desirable equilibrium state, (2) the creation of a desirable metastable, kinetically trapped, or kinetically arrested state, and (3) the creation of a desirable far-from-equilibrium state through continuous energy input. We review seminal works within this framework, provide a summary of important application areas, and present a brief introduction to the fundamental concepts of control theory that are necessary for the soft materials community to understand this literature. In addition, we outline current and potential future applications of actively-controlled colloidal systems, and we highlight important open questions and future directions.
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Affiliation(s)
- Mark N McDonald
- Department of Chemical Engineering, Brigham Young University, Provo, Utah, USA.
| | - Qinyu Zhu
- Department of Chemical Engineering, Brigham Young University, Provo, Utah, USA.
| | - Walter F Paxton
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah, USA
| | - Cameron K Peterson
- Department of Electrical and Computer Engineering, Brigham Young University, Provo, Utah, USA
| | - Douglas R Tree
- Department of Chemical Engineering, Brigham Young University, Provo, Utah, USA.
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9
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Trubiano A, Hagan MF. Optimization of non-equilibrium self-assembly protocols using Markov state models. J Chem Phys 2022; 157:244901. [PMID: 36586982 PMCID: PMC9788858 DOI: 10.1063/5.0130407] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 12/04/2022] [Indexed: 12/12/2022] Open
Abstract
The promise of self-assembly to enable the bottom-up formation of materials with prescribed architectures and functions has driven intensive efforts to uncover rational design principles for maximizing the yield of a target structure. Yet, despite many successful examples of self-assembly, ensuring kinetic accessibility of the target structure remains an unsolved problem in many systems. In particular, long-lived kinetic traps can result in assembly times that vastly exceed experimentally accessible timescales. One proposed solution is to design non-equilibrium assembly protocols in which system parameters change over time to avoid such kinetic traps. Here, we develop a framework to combine Markov state model (MSM) analysis with optimal control theory to compute a time-dependent protocol that maximizes the yield of the target structure at a finite time. We present an adjoint-based gradient descent method that, in conjunction with MSMs for a system as a function of its control parameters, enables efficiently optimizing the assembly protocol. We also describe an interpolation approach to significantly reduce the number of simulations required to construct the MSMs. We demonstrate our approach with two examples; a simple semi-analytic model for the folding of a polymer of colloidal particles, and a more complex model for capsid assembly. Our results show that optimizing time-dependent protocols can achieve significant improvements in the yields of selected structures, including equilibrium free energy minima, long-lived metastable structures, and transient states.
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Affiliation(s)
- Anthony Trubiano
- Martin Fisher School of Physics, Brandeis University, Waltham, Massachusetts 02454, USA
| | - Michael F. Hagan
- Martin Fisher School of Physics, Brandeis University, Waltham, Massachusetts 02454, USA
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10
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Shao L, Ma J, Prelesnik JL, Zhou Y, Nguyen M, Zhao M, Jenekhe SA, Kalinin SV, Ferguson AL, Pfaendtner J, Mundy CJ, De Yoreo JJ, Baneyx F, Chen CL. Hierarchical Materials from High Information Content Macromolecular Building Blocks: Construction, Dynamic Interventions, and Prediction. Chem Rev 2022; 122:17397-17478. [PMID: 36260695 DOI: 10.1021/acs.chemrev.2c00220] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Hierarchical materials that exhibit order over multiple length scales are ubiquitous in nature. Because hierarchy gives rise to unique properties and functions, many have sought inspiration from nature when designing and fabricating hierarchical matter. More and more, however, nature's own high-information content building blocks, proteins, peptides, and peptidomimetics, are being coopted to build hierarchy because the information that determines structure, function, and interfacial interactions can be readily encoded in these versatile macromolecules. Here, we take stock of recent progress in the rational design and characterization of hierarchical materials produced from high-information content blocks with a focus on stimuli-responsive and "smart" architectures. We also review advances in the use of computational simulations and data-driven predictions to shed light on how the side chain chemistry and conformational flexibility of macromolecular blocks drive the emergence of order and the acquisition of hierarchy and also on how ionic, solvent, and surface effects influence the outcomes of assembly. Continued progress in the above areas will ultimately usher in an era where an understanding of designed interactions, surface effects, and solution conditions can be harnessed to achieve predictive materials synthesis across scale and drive emergent phenomena in the self-assembly and reconfiguration of high-information content building blocks.
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Affiliation(s)
- Li Shao
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Jinrong Ma
- Molecular Engineering and Sciences Institute, University of Washington, Seattle, Washington 98195, United States
| | - Jesse L Prelesnik
- Department of Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Yicheng Zhou
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Mary Nguyen
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States.,Department of Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Mingfei Zhao
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States
| | - Samson A Jenekhe
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States.,Department of Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Sergei V Kalinin
- Department of Materials Science and Engineering, University of Tennessee, Knoxville, Tennessee 37996, United States
| | - Andrew L Ferguson
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States
| | - Jim Pfaendtner
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.,Materials Science and Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Christopher J Mundy
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.,Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
| | - James J De Yoreo
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.,Materials Science and Engineering, University of Washington, Seattle, Washington 98195, United States
| | - François Baneyx
- Molecular Engineering and Sciences Institute, University of Washington, Seattle, Washington 98195, United States.,Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Chun-Long Chen
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.,Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
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11
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Whitelam S, Schmit JD. Low-dissipation self-assembly protocols of active sticky particles. JOURNAL OF CRYSTAL GROWTH 2022; 600:126912. [PMID: 36968622 PMCID: PMC10035568 DOI: 10.1016/j.jcrysgro.2022.126912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
We use neuroevolutionary learning to identify time-dependent protocols for low-dissipation self-assembly in a model of generic active particles with interactions. When the time allotted for assembly is sufficiently long, low-dissipation protocols use only interparticle attractions, producing an amount of entropy that scales as the number of particles. When time is too short to allow assembly to proceed via diffusive motion, low-dissipation assembly protocols instead require particle self-propulsion, producing an amount of entropy that scales with the number of particles and the swim length required to cause assembly. Self-propulsion therefore provides an expensive but necessary mechanism for inducing assembly when time is of the essence.
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Affiliation(s)
- Stephen Whitelam
- Molecular Foundry, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA
| | - Jeremy D. Schmit
- Department of Physics, Kansas State University, Manhattan, KS, 66506, USA
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12
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Rovigatti L, Russo J, Romano F, Matthies M, Kroc L, Šulc P. A simple solution to the problem of self-assembling cubic diamond crystals. NANOSCALE 2022; 14:14268-14275. [PMID: 36129342 DOI: 10.1039/d2nr03533b] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The self-assembly of colloidal diamond (CD) crystals is considered as one of the most coveted goals of nanotechnology, both from the technological and fundamental points of view. For applications, colloidal diamond is a photonic crystal which can open new possibilities of manipulating light for information processing. From a fundamental point of view, its unique symmetry exacerbates a series of problems that are commonly faced during the self-assembly of target structures, such as the presence of kinetic traps and the formation of crystalline defects and alternative structures (polymorphs). Here we demonstrate that all these problems can be systematically addressed via SAT-assembly, a design framework that converts self-assembly into a Boolean satisfiability problem (SAT). Contrary to previous solutions (requiring four or more components), we prove that the assembly of the CD crystal only requires a binary mixture. Moreover, we use molecular dynamics simulations of a system composed by nearly a million nucleotides to test a DNA nanotechnology design that constitutes a promising candidate for experimental realization.
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Affiliation(s)
- Lorenzo Rovigatti
- Dipartimento di Fisica, Sapienza Università di Roma, P.le Aldo Moro 5, 00185 Rome, Italy.
- CNR-ISC Uos Sapienza, Piazzale A. Moro 2, IT-00185 Roma, Italy
| | - John Russo
- Dipartimento di Fisica, Sapienza Università di Roma, P.le Aldo Moro 5, 00185 Rome, Italy.
| | - Flavio Romano
- Dipartimento di Scienze Molecolari e Nanosistemi, Università Ca' Foscari di Venezia Campus Scientifico, Edificio Alfa, via Torino 155, 30170 Venezia Mestre, Italy
- European Centre for Living Technology (ECLT) Ca' Bottacin, 3911 Dorsoduro Calle Crosera, 30123 Venice, Italy
| | - Michael Matthies
- School of Molecular Sciences and Center for Molecular Design and Biomimetics, The Biodesign Institute, Arizona State University, 1001 South McAllister Avenue, Tempe, Arizona 85281, USA.
| | - Lukáš Kroc
- School of Molecular Sciences and Center for Molecular Design and Biomimetics, The Biodesign Institute, Arizona State University, 1001 South McAllister Avenue, Tempe, Arizona 85281, USA.
| | - Petr Šulc
- School of Molecular Sciences and Center for Molecular Design and Biomimetics, The Biodesign Institute, Arizona State University, 1001 South McAllister Avenue, Tempe, Arizona 85281, USA.
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13
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Wang Z, Sun Z, Yin H, Liu X, Wang J, Zhao H, Pang CH, Wu T, Li S, Yin Z, Yu XF. Data-Driven Materials Innovation and Applications. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2104113. [PMID: 35451528 DOI: 10.1002/adma.202104113] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Revised: 03/19/2022] [Indexed: 05/07/2023]
Abstract
Owing to the rapid developments to improve the accuracy and efficiency of both experimental and computational investigative methodologies, the massive amounts of data generated have led the field of materials science into the fourth paradigm of data-driven scientific research. This transition requires the development of authoritative and up-to-date frameworks for data-driven approaches for material innovation. A critical discussion on the current advances in the data-driven discovery of materials with a focus on frameworks, machine-learning algorithms, material-specific databases, descriptors, and targeted applications in the field of inorganic materials is presented. Frameworks for rationalizing data-driven material innovation are described, and a critical review of essential subdisciplines is presented, including: i) advanced data-intensive strategies and machine-learning algorithms; ii) material databases and related tools and platforms for data generation and management; iii) commonly used molecular descriptors used in data-driven processes. Furthermore, an in-depth discussion on the broad applications of material innovation, such as energy conversion and storage, environmental decontamination, flexible electronics, optoelectronics, superconductors, metallic glasses, and magnetic materials, is provided. Finally, how these subdisciplines (with insights into the synergy of materials science, computational tools, and mathematics) support data-driven paradigms is outlined, and the opportunities and challenges in data-driven material innovation are highlighted.
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Affiliation(s)
- Zhuo Wang
- Materials Interfaces Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, P. R. China
- Department of Chemical and Environmental Engineering, University of Nottingham Ningbo China, Ningbo, 315100, P. R. China
| | - Zhehao Sun
- Research School of Chemistry, The Australian National University, ACT, 2601, Australia
| | - Hang Yin
- Research School of Chemistry, The Australian National University, ACT, 2601, Australia
| | - Xinghui Liu
- Department of Chemistry, Sungkyunkwan University (SKKU), 2066 Seoburo, Jangan-Gu, Suwon, 16419, Republic of Korea
| | - Jinlan Wang
- School of Physics, Southeast University, Nanjing, 211189, P. R. China
| | - Haitao Zhao
- Materials Interfaces Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, P. R. China
| | - Cheng Heng Pang
- Department of Chemical and Environmental Engineering, University of Nottingham Ningbo China, Ningbo, 315100, P. R. China
- Municipal Key Laboratory of Clean Energy Conversion Technologies, University of Nottingham Ningbo China, Ningbo, 315100, P. R. China
| | - Tao Wu
- Key Laboratory for Carbonaceous Wastes Processing and Process Intensification Research of Zhejiang Province, University of Nottingham Ningbo China, Ningbo, 315100, P. R. China
- New Materials Institute, University of Nottingham, Ningbo, China, Ningbo, 315100, P. R. China
| | - Shuzhou Li
- School of Materials Science and Engineering, Nanyang Technological University, Singapore, 639798, Singapore
| | - Zongyou Yin
- Research School of Chemistry, The Australian National University, ACT, 2601, Australia
| | - Xue-Feng Yu
- Materials Interfaces Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, P. R. China
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14
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Yan J, Rotskoff G. Physics-informed graph neural networks enhance scalability of variational nonequilibrium optimal control. J Chem Phys 2022; 157:074101. [DOI: 10.1063/5.0095593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
When a physical system is driven away from equilibrium, the statistical distribution of its dynamical trajectories informs many of its physical properties. Characterizing the nature of the distribution of dynamical observables, such as a current or entropy production rate, has become a central problem in nonequilibrium statistical mechanics. Asymptotically, for a broad class of observables, the distribution of a given observable satisfies a large deviation principle when the dynamics is Markovian, meaning that fluctuations can be characterized in the long-time limit by computing a scaled cumulant generating function. Calculating this function is not tractable analytically (nor often numerically) for complex, interacting systems, so the development of robust numerical techniques to carry out this computation is needed to probe the properties of nonequilibrium materials. Here, we describe an algorithm that recasts this task as an optimal control problem that can be solved variationally. We solve for optimal control forces using neural network ansätze that are tailored to the physical systems to which the forces are applied. We demonstrate that this approach leads to transferable and accurate solutions in two systems featuring large numbers of interacting particles.
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Affiliation(s)
- Jiawei Yan
- Stanford University Department of Chemistry, United States of America
| | - Grant Rotskoff
- Chemistry, Stanford University Department of Chemistry, United States of America
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15
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Lieu UT, Yoshinaga N. Inverse design of two-dimensional structure by self-assembly of patchy particles. J Chem Phys 2022; 156:054901. [DOI: 10.1063/5.0072234] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
| | - Natsuhiko Yoshinaga
- WPI Advanced Institute for Materials Research, Tohoku University - Katahira Campus, Japan
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16
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Whitelam S, Selin V, Park SW, Tamblyn I. Correspondence between neuroevolution and gradient descent. Nat Commun 2021; 12:6317. [PMID: 34728632 PMCID: PMC8563972 DOI: 10.1038/s41467-021-26568-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Accepted: 10/04/2021] [Indexed: 11/10/2022] Open
Abstract
We show analytically that training a neural network by conditioned stochastic mutation or neuroevolution of its weights is equivalent, in the limit of small mutations, to gradient descent on the loss function in the presence of Gaussian white noise. Averaged over independent realizations of the learning process, neuroevolution is equivalent to gradient descent on the loss function. We use numerical simulation to show that this correspondence can be observed for finite mutations, for shallow and deep neural networks. Our results provide a connection between two families of neural-network training methods that are usually considered to be fundamentally different. Gradient-based and non-gradient-based methods for training neural networks are usually considered to be fundamentally different. The authors derive, and illustrate numerically, an analytic equivalence between the dynamics of neural network training under conditioned stochastic mutations, and under gradient descent.
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Affiliation(s)
- Stephen Whitelam
- Molecular Foundry, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, 94720, USA.
| | - Viktor Selin
- Department of Physics, University of Ottawa, Ottawa, ON, K1N 6N5, Canada
| | - Sang-Won Park
- Molecular Foundry, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, 94720, USA
| | - Isaac Tamblyn
- Department of Physics, University of Ottawa, Ottawa, ON, K1N 6N5, Canada. .,National Research Council of Canada, Ottawa, ON, K1N 5A2, Canada. .,Vector Institute for Artificial Intelligence, Toronto, ON, M5G 1M1, Canada.
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17
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Whitelam S, Harrowell P. Deposition control of model glasses with surface-mediated orientational order. J Chem Phys 2021; 155:124502. [PMID: 34598548 DOI: 10.1063/5.0061042] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
We introduce a minimal model of solid-forming anisotropic molecules that displays, in thermal equilibrium, surface orientational order without bulk orientational order. The model reproduces the nonequilibrium behavior of recent experiments in which a bulk nonequilibrium structure grown by deposition contains regions of orientational order characteristic of the surface equilibrium. This order is deposited, in general, in a nonuniform way because of the emergence of a growth-poisoning mechanism that causes equilibrated surfaces to grow slower than non-equilibrated surfaces. We use evolutionary methods to design oscillatory protocols able to grow nonequilibrium structures with uniform order, demonstrating the potential of protocol design for the fabrication of this class of materials.
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Affiliation(s)
- Stephen Whitelam
- Molecular Foundry, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, California 94720, USA
| | - Peter Harrowell
- School of Chemistry, University of Sydney, Sydney, New South Wales 2006, Australia
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18
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Trubiano A, Holmes-Cerfon M. Thermodynamic stability versus kinetic accessibility: Pareto fronts for programmable self-assembly. SOFT MATTER 2021; 17:6797-6807. [PMID: 34223604 DOI: 10.1039/d1sm00681a] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
A challenge in designing self-assembling building blocks is to ensure the target state is both thermodynamically stable and kinetically accessible. These two objectives are known to be typically in competition, but it is not known how to simultaneously optimize them. We consider this problem through the lens of multi-objective optimization theory: we develop a genetic algorithm to compute the Pareto fronts characterizing the tradeoff between equilibrium probability and folding rate, for a model system of small polymers of colloids with tunable short-ranged interaction energies. We use a coarse-grained model for the particles' dynamics that allows us to efficiently search over parameters, for systems small enough to be enumerated. For most target states there is a tradeoff when the number of types of particles is small, with medium-weak bonds favouring fast folding, and strong bonds favouring high equilibrium probability. The tradeoff disappears when the number of particle types reaches a value m*, that is usually much less than the total number of particles. This general approach of computing Pareto fronts allows one to identify the minimum number of design parameters to avoid a thermodynamic-kinetic tradeoff. However, we argue, by contrasting our coarse-grained model's predictions with those of Brownian dynamics simulations, that particles with short-ranged isotropic interactions should generically have a tradeoff, and avoiding it in larger systems will require orientation-dependent interactions.
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Affiliation(s)
- Anthony Trubiano
- Courant Institute of Mathematical Sciences, New York University, New York, New York 10012, USA.
| | - Miranda Holmes-Cerfon
- Courant Institute of Mathematical Sciences, New York University, New York, New York 10012, USA.
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19
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Whitelam S, Tamblyn I. Neuroevolutionary Learning of Particles and Protocols for Self-Assembly. PHYSICAL REVIEW LETTERS 2021; 127:018003. [PMID: 34270312 DOI: 10.1103/physrevlett.127.018003] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Accepted: 05/25/2021] [Indexed: 06/13/2023]
Abstract
Within simulations of molecules deposited on a surface we show that neuroevolutionary learning can design particles and time-dependent protocols to promote self-assembly, without input from physical concepts such as thermal equilibrium or mechanical stability and without prior knowledge of candidate or competing structures. The learning algorithm is capable of both directed and exploratory design: it can assemble a material with a user-defined property, or search for novelty in the space of specified order parameters. In the latter mode it explores the space of what can be made, rather than the space of structures that are low in energy but not necessarily kinetically accessible.
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Affiliation(s)
- Stephen Whitelam
- Molecular Foundry, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, Califronia 94720, USA
| | - Isaac Tamblyn
- National Research Council of Canada Ottawa, Ontario K1N 5A2, Canada Vector Institute for Artificial Intelligence Toronto, Ontario M5G 1M1, Canada
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20
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Gutiérrez R, Pérez-Espigares C. Generalized optimal paths and weight distributions revealed through the large deviations of random walks on networks. Phys Rev E 2021; 103:022319. [PMID: 33735982 DOI: 10.1103/physreve.103.022319] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 02/03/2021] [Indexed: 01/18/2023]
Abstract
Numerous problems of both theoretical and practical interest are related to finding shortest (or otherwise optimal) paths in networks, frequently in the presence of some obstacles or constraints. A somewhat related class of problems focuses on finding optimal distributions of weights which, for a given connection topology, maximize some kind of flow or minimize a given cost function. We show that both sets of problems can be approached through an analysis of the large-deviation functions of random walks. Specifically, a study of ensembles of trajectories allows us to find optimal paths, or design optimal weighted networks, by means of an auxiliary stochastic process (the generalized Doob transform). The paths are not limited to shortest paths, and the weights must not necessarily optimize a given function. Paths and weights can in fact be tailored to a given statistics of a time-integrated observable, which may be an activity or current, or local functions marking the passing of the random walker through a given node or link. We illustrate this idea with an exploration of optimal paths in the presence of obstacles, and networks that optimize flows under constraints on local observables.
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Affiliation(s)
- Ricardo Gutiérrez
- Complex Systems Interdisciplinary Group, Department of Mathematics, Universidad Carlos III de Madrid, 28911 Leganés, Madrid, Spain
| | - Carlos Pérez-Espigares
- Departamento de Electromagnetismo y Física de la Materia, Universidad de Granada, Granada 18071, Spain.,Institute Carlos I for Theoretical and Computational Physics, Universidad de Granada, Granada 18071, Spain
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21
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Whitelam S. Improving the Accuracy of Nearest-Neighbor Classification Using Principled Construction and Stochastic Sampling of Training-Set Centroids. ENTROPY (BASEL, SWITZERLAND) 2021; 23:149. [PMID: 33530507 PMCID: PMC7911166 DOI: 10.3390/e23020149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/03/2021] [Accepted: 01/21/2021] [Indexed: 11/16/2022]
Abstract
A conceptually simple way to classify images is to directly compare test-set data and training-set data. The accuracy of this approach is limited by the method of comparison used, and by the extent to which the training-set data cover configuration space. Here we show that this coverage can be substantially increased using coarse-graining (replacing groups of images by their centroids) and stochastic sampling (using distinct sets of centroids in combination). We use the MNIST and Fashion-MNIST data sets to show that a principled coarse-graining algorithm can convert training images into fewer image centroids without loss of accuracy of classification of test-set images by nearest-neighbor classification. Distinct batches of centroids can be used in combination as a means of stochastically sampling configuration space, and can classify test-set data more accurately than can the unaltered training set. On the MNIST and Fashion-MNIST data sets this approach converts nearest-neighbor classification from a mid-ranking- to an upper-ranking member of the set of classical machine-learning techniques.
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Affiliation(s)
- Stephen Whitelam
- Molecular Foundry, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA
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22
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Das A, Limmer DT. Variational design principles for nonequilibrium colloidal assembly. J Chem Phys 2021; 154:014107. [DOI: 10.1063/5.0038652] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Affiliation(s)
- Avishek Das
- Department of Chemistry, University of California, Berkeley, California 94609, USA
| | - David T. Limmer
- Department of Chemistry, University of California, Berkeley, California 94609, USA
- Kavli Energy NanoScience Institute, Berkeley, California 94609, USA
- Materials Science Division, Lawrence Berkeley National Laboratory, Berkeley, California 94609, USA
- Chemical Science Division, Lawrence Berkeley National Laboratory, Berkeley, California 94609, USA
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23
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Insights into the Structure and Energy of DNA Nanoassemblies. Molecules 2020; 25:molecules25235466. [PMID: 33255286 PMCID: PMC7727707 DOI: 10.3390/molecules25235466] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 11/14/2020] [Accepted: 11/16/2020] [Indexed: 11/16/2022] Open
Abstract
Since the pioneering work of Ned Seeman in the early 1980s, the use of the DNA molecule as a construction material experienced a rapid growth and led to the establishment of a new field of science, nowadays called structural DNA nanotechnology. Here, the self-recognition properties of DNA are employed to build micrometer-large molecular objects with nanometer-sized features, thus bridging the nano- to the microscopic world in a programmable fashion. Distinct design strategies and experimental procedures have been developed over the years, enabling the realization of extremely sophisticated structures with a level of control that approaches that of natural macromolecular assemblies. Nevertheless, our understanding of the building process, i.e., what defines the route that goes from the initial mixture of DNA strands to the final intertwined superstructure, is, in some cases, still limited. In this review, we describe the main structural and energetic features of DNA nanoconstructs, from the simple Holliday junction to more complicated DNA architectures, and present the theoretical frameworks that have been formulated until now to explain their self-assembly. Deeper insights into the underlying principles of DNA self-assembly may certainly help us to overcome current experimental challenges and foster the development of original strategies inspired to dissipative and evolutive assembly processes occurring in nature.
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24
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Whitelam S, Jacobson D, Tamblyn I. Evolutionary reinforcement learning of dynamical large deviations. J Chem Phys 2020; 153:044113. [PMID: 32752661 DOI: 10.1063/5.0015301] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
We show how to bound and calculate the likelihood of dynamical large deviations using evolutionary reinforcement learning. An agent, a stochastic model, propagates a continuous-time Monte Carlo trajectory and receives a reward conditioned upon the values of certain path-extensive quantities. Evolution produces progressively fitter agents, potentially allowing the calculation of a piece of a large-deviation rate function for a particular model and path-extensive quantity. For models with small state spaces, the evolutionary process acts directly on rates, and for models with large state spaces, the process acts on the weights of a neural network that parameterizes the model's rates. This approach shows how path-extensive physics problems can be considered within a framework widely used in machine learning.
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Affiliation(s)
- Stephen Whitelam
- Molecular Foundry, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, California 94720, USA
| | - Daniel Jacobson
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, USA
| | - Isaac Tamblyn
- National Research Council of Canada, Ottawa, Ontario K1N 5A2, Canada
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