1
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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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2
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Petix CL, Fakhraei M, Kieslich CA, Howard MP. Surrogate Modeling of the Relative Entropy for Inverse Design Using Smolyak Sparse Grids. J Chem Theory Comput 2024; 20:1538-1546. [PMID: 37703086 DOI: 10.1021/acs.jctc.3c00651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/14/2023]
Abstract
Relative entropy minimization, a statistical-mechanics approach for finding potential energy functions that produce target structural ensembles, has proven to be a powerful strategy for the inverse design of nanoparticle self-assembly. For a given target structure, the gradient of the relative entropy with respect to the adjustable parameters of the potential energy function is computed by performing a simulation, and then these parameters are updated using iterative gradient-based optimization. Small parameter updates per iteration and many iterations can be required for numerical stability, but this incurs considerable computational expense because a new simulation must be performed to reevaluate the gradient at each iteration. Here, we investigate the use of surrogate modeling to decouple the process of minimizing the relative entropy from the computationally demanding process of determining its gradient. We approximate the relative-entropy gradient using Chebyshev polynomial interpolation on Smolyak sparse grids. Our approach potentially increases the robustness and computational efficiency of using the relative entropy for inverse design, primarily for physically informed potential energy functions that have a small number of adjustable parameters.
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Affiliation(s)
- C Levi Petix
- Department of Chemical Engineering, Auburn University, Auburn, Alabama 36849, United States
| | - Mohammadreza Fakhraei
- Department of Chemical Engineering, Auburn University, Auburn, Alabama 36849, United States
| | - Chris A Kieslich
- Department of Chemical Engineering, Auburn University, Auburn, Alabama 36849, United States
| | - Michael P Howard
- Department of Chemical Engineering, Auburn University, Auburn, Alabama 36849, United States
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3
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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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4
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Schönhöfer PWA, Sun K, Mao X, Glotzer SC. Rationalizing Euclidean Assemblies of Hard Polyhedra from Tessellations in Curved Space. PHYSICAL REVIEW LETTERS 2023; 131:258201. [PMID: 38181337 DOI: 10.1103/physrevlett.131.258201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 10/20/2023] [Indexed: 01/07/2024]
Abstract
Entropic self-assembly is governed by the shape of the constituent particles, yet a priori prediction of crystal structures from particle shape alone is nontrivial for anything but the simplest of space-filling shapes. At the same time, most polyhedra are not space filling due to geometric constraints, but these constraints can be relaxed or even eliminated by sufficiently curving space. We show using Monte Carlo simulations that the majority of hard Platonic solids self-assemble entropically into space-filling crystals when constrained to the surface volume of a 3-sphere. As we gradually decrease curvature to "flatten" space and compare the local morphologies of crystals assembling in curved and flat space, we show that the Euclidean assemblies can be categorized as either remnants of tessellations in curved space (tetrahedra and dodecahedra) or nontessellation-based assemblies caused by large-scale geometric frustration (octahedra and icosahedra).
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Affiliation(s)
- Philipp W A Schönhöfer
- Department of Chemical Engineering, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Kai Sun
- Department of Physics, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Xiaoming Mao
- Department of Physics, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Sharon C Glotzer
- Department of Chemical Engineering, University of Michigan, Ann Arbor, Michigan 48109, USA
- Department of Physics, University of Michigan, Ann Arbor, Michigan 48109, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, Michigan 48109, USA
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5
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Das A, Limmer DT. Nonequilibrium design strategies for functional colloidal assemblies. Proc Natl Acad Sci U S A 2023; 120:e2217242120. [PMID: 37748070 PMCID: PMC10556551 DOI: 10.1073/pnas.2217242120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Accepted: 08/17/2023] [Indexed: 09/27/2023] Open
Abstract
We use a nonequilibrium variational principle to optimize the steady-state, shear-induced interconversion of self-assembled nanoclusters of DNA-coated colloids. Employing this principle within a stochastic optimization algorithm allows us to identify design strategies for functional materials. We find that far-from-equilibrium shear flow can significantly enhance the flux between specific colloidal states by decoupling trade-offs between stability and reactivity required by systems in equilibrium. For isolated nanoclusters, we find nonequilibrium strategies for amplifying transition rates by coupling a given reaction coordinate to the background shear flow. We also find that shear flow can be made to selectively break detailed balance and maximize probability currents by coupling orientational degrees of freedom to conformational transitions. For a microphase consisting of many nanoclusters, we study the flux of colloids hopping between clusters. We find that a shear flow can amplify the flux without a proportional compromise on the microphase structure. This approach provides a general means of uncovering design principles for nanoscale, autonomous, functional materials driven far from equilibrium.
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Affiliation(s)
- Avishek Das
- Department of Chemistry, University of California, Berkeley, CA94720
| | - David T. Limmer
- Department of Chemistry, University of California, Berkeley, CA94720
- Chemical Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA94720
- Material Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA94720
- Kavli Energy NanoSciences Institute, University of California, Berkeley, CA94720
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6
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Huang K, Li Q, Xue Y, Wang Q, Chen Z, Gu Z. Application of colloidal photonic crystals in study of organoids. Adv Drug Deliv Rev 2023; 201:115075. [PMID: 37625595 DOI: 10.1016/j.addr.2023.115075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Revised: 07/09/2023] [Accepted: 08/20/2023] [Indexed: 08/27/2023]
Abstract
As alternative disease models, other than 2D cell lines and patient-derived xenografts, organoids have preferable in vivo physiological relevance. However, both endogenous and exogenous limitations impede the development and clinical translation of these organoids. Fortunately, colloidal photonic crystals (PCs), which benefit from favorable biocompatibility, brilliant optical manipulation, and facile chemical decoration, have been applied to the engineering of organoids and have achieved the desirable recapitulation of the ECM niche, well-defined geometrical onsets for initial culture, in situ multiphysiological parameter monitoring, single-cell biomechanical sensing, and high-throughput drug screening with versatile functional readouts. Herein, we review the latest progress in engineering organoids fabricated from colloidal PCs and provide inputs for future research.
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Affiliation(s)
- Kai Huang
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
| | - Qiwei Li
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
| | - Yufei Xue
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
| | - Qiong Wang
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
| | - Zaozao Chen
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China; Institute of Biomaterials and Medical Devices, Southeast University, Suzhou, Jiangsu 215163, China.
| | - Zhongze Gu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China.
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7
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Liu Z, Liu YX, Yang Y, Li J. Template Design for Complex Block Copolymer Patterns Using a Machine Learning Method. ACS APPLIED MATERIALS & INTERFACES 2023. [PMID: 37335810 DOI: 10.1021/acsami.3c05018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2023]
Abstract
This study represents the first attempt to address the inverse design problem of the guiding template for directed self-assembly (DSA) patterns using solely machine learning methods. By formulating the problem as a multi-label classification task, the study shows that it is possible to predict templates without requiring any forward simulations. A series of neural network (NN) models, ranging from the basic two-layer convolutional neural network (CNN) to the large NN models (32-layer CNN with 8 residual blocks), have been trained using simulated pattern samples generated by thousands of self-consistent field theory (SCFT) calculations; a number of augmentation techniques, especially suitable for predicting morphologies, have been also proposed to enhance the performance of the NN model. The exact match accuracy of the model in predicting the template of simulated patterns was significantly improved from 59.8% for the baseline model to 97.1% for the best model of this study. The best model also demonstrates an excellent generalization ability in predicting the template for human-designed DSA patterns, while the simplest baseline model is ineffective in this task.
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Affiliation(s)
- Zhihan Liu
- The State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, Fudan University, Shanghai 200433, China
| | - Yi-Xin Liu
- The State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, Fudan University, Shanghai 200433, China
| | - Yuliang Yang
- The State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, Fudan University, Shanghai 200433, China
| | - Jianfeng Li
- The State Key Laboratory of Molecular Engineering of Polymers, Department of Macromolecular Science, Fudan University, Shanghai 200433, China
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8
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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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9
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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: 23] [Impact Index Per Article: 11.5] [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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10
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Yoshinaga N, Tokuda S. Bayesian modeling of pattern formation from one snapshot of pattern. Phys Rev E 2022; 106:065301. [PMID: 36671103 DOI: 10.1103/physreve.106.065301] [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: 01/12/2022] [Accepted: 11/07/2022] [Indexed: 06/17/2023]
Abstract
Partial differential equations (PDEs) have been widely used to reproduce patterns in nature and to give insight into the mechanism underlying pattern formation. Although many PDE models have been proposed, they rely on the pre-request knowledge of physical laws and symmetries, and developing a model to reproduce a given desired pattern remains difficult. We propose a method, referred to as Bayesian modeling of PDEs (BM-PDEs), to estimate the best dynamical PDE for one snapshot of a objective pattern under the stationary state without ground truth. We apply BM-PDEs to nontrivial patterns, such as quasicrystals (QCs), a double gyroid, and Frank-Kasper structures. We also generate three-dimensional dodecagonal QCs from a PDE model. This is done by using the estimated parameters for the Frank-Kasper A15 structure, which closely approximates the local structures of QCs. Our method works for noisy patterns and the pattern synthesized without the ground-truth parameters, which are required for the application toward experimental data.
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Affiliation(s)
- Natsuhiko Yoshinaga
- WPI-Advanced Institute for Materials Research, Tohoku University, Sendai 980-8577, Japan
- MathAM-OIL, AIST, Sendai 980-8577, Japan
| | - Satoru Tokuda
- MathAM-OIL, AIST, Sendai 980-8577, Japan
- Research Institute for Information Technology, Kyushu University, Kasuga 816-8580, Japan
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11
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Russo J, Romano F, Kroc L, Sciortino F, Rovigatti L, Šulc P. SAT-assembly: a new approach for designing self-assembling systems. JOURNAL OF PHYSICS. CONDENSED MATTER : AN INSTITUTE OF PHYSICS JOURNAL 2022; 34:354002. [PMID: 35148521 DOI: 10.1088/1361-648x/ac5479] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Accepted: 02/11/2022] [Indexed: 06/14/2023]
Abstract
We propose a general framework for solving inverse self-assembly problems, i.e. designing interactions between elementary units such that they assemble spontaneously into a predetermined structure. Our approach uses patchy particles as building blocks, where the different units bind at specific interaction sites (the patches), and we exploit the possibility of having mixtures with several components. The interaction rules between the patches is determined by transforming the combinatorial problem into a Boolean satisfiability problem (SAT) which searches for solutions where all bonds are formed in the target structure. Additional conditions, such as the non-satisfiability of competing structures (e.g. metastable states) can be imposed, allowing to effectively design the assembly path in order to avoid kinetic traps. We demonstrate this approach by designing and numerically simulating a cubic diamond structure from four particle species that assembles without competition from other polymorphs, including the hexagonal structure.
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Affiliation(s)
- 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
| | - Lukáš Kroc
- School of Molecular Sciences and Center for Molecular Design and Biomimetics, The Biodesign Institute, Arizona State University, 1001 South McAllister Avenue, Tempe, AZ 85281, United States of America
| | - Francesco Sciortino
- 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
- Institute for Complex Systems, Uos Sapienza, CNR, Rome 00185, 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, United States of America
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12
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Coli GM, Boattini E, Filion L, Dijkstra M. Inverse design of soft materials via a deep learning-based evolutionary strategy. SCIENCE ADVANCES 2022; 8:eabj6731. [PMID: 35044828 PMCID: PMC8769546 DOI: 10.1126/sciadv.abj6731] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 11/22/2021] [Indexed: 05/19/2023]
Abstract
Colloidal self-assembly—the spontaneous organization of colloids into ordered structures—has been considered key to produce next-generation materials. However, the present-day staggering variety of colloidal building blocks and the limitless number of thermodynamic conditions make a systematic exploration intractable. The true challenge in this field is to turn this logic around and to develop a robust, versatile algorithm to inverse design colloids that self-assemble into a target structure. Here, we introduce a generic inverse design method to efficiently reverse-engineer crystals, quasicrystals, and liquid crystals by targeting their diffraction patterns. Our algorithm relies on the synergetic use of an evolutionary strategy for parameter optimization, and a convolutional neural network as an order parameter, and provides a way forward for the inverse design of experimentally feasible colloidal interactions, specifically optimized to stabilize the desired structure.
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13
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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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14
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Case LJ, Delaney KT, Fredrickson GH, Bates FS, Dorfman KD. Open-source platform for block polymer formulation design using particle swarm optimization. THE EUROPEAN PHYSICAL JOURNAL. E, SOFT MATTER 2021; 44:115. [PMID: 34532757 DOI: 10.1140/epje/s10189-021-00123-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 09/06/2021] [Indexed: 06/13/2023]
Abstract
Facile exploration of large design spaces is critical to the development of new functional soft materials, including self-assembling block polymers, and computational inverse design methodologies are a promising route to initialize this task. We present here an open-source software package coupling particle swarm optimization (PSO) with an existing open-source self-consistent field theory (SCFT) software for the inverse design of self-assembling block polymers to target bulk morphologies. To lower the barrier to use of the software and facilitate exploration of novel design spaces, the underlying SCFT calculations are seeded with algorithmically generated initial fields for four typical morphologies: lamellae, network phases, cylindrical phases, and spherical phases. In addition to its utility within PSO, the initial guess tool also finds generic applicability for stand-alone SCFT calculations. The robustness of the software is demonstrated with two searches for classical phases in the conformationally symmetric diblock system, as well as one search for the Frank-Kasper [Formula: see text] phase in conformationally asymmetric diblocks. The source code for both the initial guess generation and the PSO wrapper is publicly available.
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Affiliation(s)
- Logan J Case
- Department of Chemical Engineering and Materials Science, University of Minnesota - Twin Cities, 421 Washington Avenue SE, Minneapolis, MN, 55455, USA
| | - Kris T Delaney
- Department of Chemical Engineering and Materials Research Laboratory, University of California, Santa Barbara, Santa Barbara, CA, 93106, USA
| | - Glenn H Fredrickson
- Department of Chemical Engineering and Materials Research Laboratory, University of California, Santa Barbara, Santa Barbara, CA, 93106, USA
| | - Frank S Bates
- Department of Chemical Engineering and Materials Science, University of Minnesota - Twin Cities, 421 Washington Avenue SE, Minneapolis, MN, 55455, USA
| | - Kevin D Dorfman
- Department of Chemical Engineering and Materials Science, University of Minnesota - Twin Cities, 421 Washington Avenue SE, Minneapolis, MN, 55455, USA.
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15
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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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16
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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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17
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Dijkstra M, Luijten E. From predictive modelling to machine learning and reverse engineering of colloidal self-assembly. NATURE MATERIALS 2021; 20:762-773. [PMID: 34045705 DOI: 10.1038/s41563-021-01014-2] [Citation(s) in RCA: 53] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Accepted: 04/19/2021] [Indexed: 06/12/2023]
Abstract
An overwhelming diversity of colloidal building blocks with distinct sizes, materials and tunable interaction potentials are now available for colloidal self-assembly. The application space for materials composed of these building blocks is vast. To make progress in the rational design of new self-assembled materials, it is desirable to guide the experimental synthesis efforts by computational modelling. Here, we discuss computer simulation methods and strategies used for the design of soft materials created through bottom-up self-assembly of colloids and nanoparticles. We describe simulation techniques for investigating the self-assembly behaviour of colloidal suspensions, including crystal structure prediction methods, phase diagram calculations and enhanced sampling techniques, as well as their limitations. We also discuss the recent surge of interest in machine learning and reverse-engineering methods. Although their implementation in the colloidal realm is still in its infancy, we anticipate that these data-science tools offer new paradigms in understanding, predicting and (inverse) design of novel colloidal materials.
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Affiliation(s)
- Marjolein Dijkstra
- Soft Condensed Matter, Debye Institute for Nanomaterial Science, Department of Physics, Utrecht University, Utrecht, The Netherlands.
| | - Erik Luijten
- Departments of Materials Science and Engineering, Engineering Sciences & Applied Mathematics, Chemistry and Physics & Astronomy, Northwestern University, Evanston, IL, USA.
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18
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Lindquist BA. Inverse design of equilibrium cluster fluids applied to a physically informed model. J Chem Phys 2021; 154:174907. [PMID: 34241069 DOI: 10.1063/5.0048812] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Inverse design strategies have proven highly useful for the discovery of interaction potentials that prompt self-assembly of a variety of interesting structures. However, often the optimized particle interactions do not have a direct relationship to experimental systems. In this work, we show that Relative Entropy minimization is able to discover physically meaningful parameter sets for a model interaction built from depletion attraction and electrostatic repulsion that yield self-assembly of size-specific clusters. We then explore the sensitivity of the optimized interaction potentials with respect to deviations in the underlying physical quantities, showing that clustering behavior is largely preserved even as the optimized parameters are perturbed.
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Affiliation(s)
- Beth A Lindquist
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
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19
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Piñeros WD, Tlusty T. Inverse design of nonequilibrium steady states: A large-deviation approach. Phys Rev E 2021; 103:022101. [PMID: 33735990 DOI: 10.1103/physreve.103.022101] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Accepted: 01/12/2021] [Indexed: 06/12/2023]
Abstract
The design of small scale nonequilibrium steady states (NESS) is a challenging, open ended question. While similar equilibrium problems are tractable using standard thermodynamics, a generalized description for nonequilibrium systems is lacking, making the design problem particularly difficult. Here we show we can exploit the large-deviation behavior of a Brownian particle and design a variety of geometrically complex steady-state density distributions and flux field flows. We achieve this design target from direct knowledge of the joint large-deviation functional for the empirical density and flow, and a "relaxation" algorithm on the desired target states via adjustable force field parameters. We validate the method by replicating analytical results, and demonstrate its capacity to yield complex prescribed targets, such as rose-curve or polygonal shapes on the plane. We consider this dynamical fluctuation approach a first step towards the design of more complex NESS where general frameworks are otherwise still lacking.
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Affiliation(s)
- William D Piñeros
- Center for Soft and Living Matter, Institute for Basic Science (IBS), Ulsan 44919, Republic of Korea
| | - Tsvi Tlusty
- Center for Soft and Living Matter, Institute for Basic Science (IBS), Ulsan 44919, Republic of Korea
- Department of Physics and Department of Chemistry, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea
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20
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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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21
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Tu KH, Huang H, Lee S, Lee W, Sun Z, Alexander-Katz A, Ross CA. Machine Learning Predictions of Block Copolymer Self-Assembly. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2020; 32:e2005713. [PMID: 33206426 DOI: 10.1002/adma.202005713] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Revised: 10/15/2020] [Indexed: 06/11/2023]
Abstract
Directed self-assembly of block copolymers is a key enabler for nanofabrication of devices with sub-10 nm feature sizes, allowing patterning far below the resolution limit of conventional photolithography. Among all the process steps involved in block copolymer self-assembly, solvent annealing plays a dominant role in determining the film morphology and pattern quality, yet the interplay of the multiple parameters during solvent annealing, including the initial thickness, swelling, time, and solvent ratio, makes it difficult to predict and control the resultant self-assembled pattern. Here, machine learning tools are applied to analyze the solvent annealing process and predict the effect of process parameters on morphology and defectivity. Two neural networks are constructed and trained, yielding accurate prediction of the final morphology in agreement with experimental data. A ridge regression model is constructed to identify the critical parameters that determine the quality of line/space patterns. These results illustrate the potential of machine learning to inform nanomanufacturing processes.
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Affiliation(s)
- Kun-Hua Tu
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Hejin Huang
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Sangho Lee
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Wonmoo Lee
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Zehao Sun
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Alfredo Alexander-Katz
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Caroline A Ross
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
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22
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Holmes-Cerfon M. Simulating sticky particles: A Monte Carlo method to sample a stratification. J Chem Phys 2020; 153:164112. [PMID: 33138386 DOI: 10.1063/5.0019550] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Abstract
Many problems in materials science and biology involve particles interacting with strong, short-ranged bonds that can break and form on experimental timescales. Treating such bonds as constraints can significantly speed up sampling their equilibrium distribution, and there are several methods to sample probability distributions subject to fixed constraints. We introduce a Monte Carlo method to handle the case when constraints can break and form. More generally, the method samples a probability distribution on a stratification: a collection of manifolds of different dimensions, where the lower-dimensional manifolds lie on the boundaries of the higher-dimensional manifolds. We show several applications of the method in polymer physics, self-assembly of colloids, and volume calculation in high dimensions.
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Affiliation(s)
- Miranda Holmes-Cerfon
- Courant Institute of Mathematical Sciences, New York University, New York, New York 10012, USA
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23
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Romano F, Russo J, Kroc L, Šulc P. Designing Patchy Interactions to Self-Assemble Arbitrary Structures. PHYSICAL REVIEW LETTERS 2020; 125:118003. [PMID: 32975991 DOI: 10.1103/physrevlett.125.118003] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2020] [Revised: 06/21/2020] [Accepted: 08/10/2020] [Indexed: 06/11/2023]
Abstract
One of the fundamental goals of nanotechnology is to exploit selective and directional interactions between molecules to design particles that self-assemble into desired structures, from capsids, to nanoclusters, to fully formed crystals with target properties (e.g., optical, mechanical, etc.). Here, we provide a general framework which transforms the inverse problem of self-assembly of colloidal crystals into a Boolean satisfiability problem for which solutions can be found numerically. Given a reference structure and the desired number of components, our approach produces designs for which the target structure is an energy minimum, and also allows us to exclude solutions that correspond to competing structures. We demonstrate the effectiveness of our approach by designing model particles that spontaneously nucleate milestone structures such as the cubic diamond, the pyrochlore, and the clathrate lattices.
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Affiliation(s)
- 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
| | - John Russo
- Dipartimento di Scienze Molecolari e Nanosistemi, Università Ca' Foscari di Venezia Campus Scientifico, Edificio Alfa, via Torino 155, 30170 Venezia Mestre, Italy
- Dipartimento di Fisica, Sapienza Università di Roma, Piazzale le Aldo Moro 5, 00185 Rome, Italy
| | - 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
- Dipartimento di Scienze Molecolari e Nanosistemi, Università Ca' Foscari di Venezia Campus Scientifico, Edificio Alfa, via Torino 155, 30170 Venezia Mestre, Italy
- 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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24
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Schönhöfer PWA, Marechal M, Cleaver DJ, Schröder-Turk GE. Self-assembly and entropic effects in pear-shaped colloid systems. II. Depletion attraction of pear-shaped particles in a hard-sphere solvent. J Chem Phys 2020; 153:034904. [PMID: 32716194 DOI: 10.1063/5.0007287] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
We consider depletion effects of a pear-shaped colloidal particle in a hard-sphere solvent for two different model realizations of the pear-shaped colloidal particle. The two models are the pear hard Gaussian overlap (PHGO) particles and the hard pears of revolution (HPR). The motivation for this study is to provide a microscopic understanding for the substantially different mesoscopic self-assembly properties of these pear-shaped colloids, in dense suspensions, that have been reported in the previous studies. This is done by determining their differing depletion attractions via Monte Carlo simulations of PHGO and HPR particles in a pool of hard spheres and comparing them with excluded volume calculations of numerically obtained ideal configurations on the microscopic level. While the HPR model behaves as predicted by the analysis of excluded volumes, the PHGO model showcases a preference for splay between neighboring particles, which can be attributed to the special non-additive characteristics of the PHGO contact function. Lastly, we propose a potentially experimentally realizable pear-shaped particle model, the non-additive hard pear of revolution model, which is based on the HPR model but also features non-additive traits similar to those of PHGO particles to mimic their depletion behavior.
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Affiliation(s)
- Philipp W A Schönhöfer
- College of Science, Health, Engineering and Education, Mathematics and Statistics, Murdoch University, 90 South Street, Murdoch WA 6150, Australia
| | - Matthieu Marechal
- Institut für Theoretische Physik I, Friedrich-Alexander-Universität Erlangen-Nürnberg, Staudtstraße 7, 91058 Erlangen, Germany
| | - Douglas J Cleaver
- Materials and Engineering Research Institute, Sheffield Hallam University, Sheffield S1 1WB, United Kingdom
| | - Gerd E Schröder-Turk
- College of Science, Health, Engineering and Education, Mathematics and Statistics, Murdoch University, 90 South Street, Murdoch WA 6150, Australia
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25
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Whitelam S, Tamblyn I. Learning to grow: Control of material self-assembly using evolutionary reinforcement learning. Phys Rev E 2020; 101:052604. [PMID: 32575260 DOI: 10.1103/physreve.101.052604] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2019] [Accepted: 03/29/2020] [Indexed: 06/11/2023]
Abstract
We show that neural networks trained by evolutionary reinforcement learning can enact efficient molecular self-assembly protocols. Presented with molecular simulation trajectories, networks learn to change temperature and chemical potential in order to promote the assembly of desired structures or choose between competing polymorphs. In the first case, networks reproduce in a qualitative sense the results of previously known protocols, but faster and with higher fidelity; in the second case they identify strategies previously unknown, from which we can extract physical insight. Networks that take as input the elapsed time of the simulation or microscopic information from the system are both effective, the latter more so. The evolutionary scheme we have used is simple to implement and can be applied to a broad range of examples of experimental self-assembly, whether or not one can monitor the experiment as it proceeds. Our results have been achieved with no human input beyond the specification of which order parameter to promote, pointing the way to the design of synthesis protocols by artificial intelligence.
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Affiliation(s)
- Stephen Whitelam
- Molecular Foundry, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, California 94720, USA
| | - Isaac Tamblyn
- National Research Council of Canada, Ottawa, Ontario, Canada and Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada
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26
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Sherman ZM, Howard MP, Lindquist BA, Jadrich RB, Truskett TM. Inverse methods for design of soft materials. J Chem Phys 2020; 152:140902. [DOI: 10.1063/1.5145177] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023] Open
Affiliation(s)
- Zachary M. Sherman
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, Texas 78712, USA
| | - Michael P. Howard
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, Texas 78712, USA
| | - Beth A. Lindquist
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - Ryan B. Jadrich
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - Thomas M. Truskett
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, Texas 78712, USA
- Department of Physics, University of Texas at Austin, Austin, Texas 78712, USA
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27
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Cheong GK, Chawla A, Morse DC, Dorfman KD. Open-source code for self-consistent field theory calculations of block polymer phase behavior on graphics processing units. THE EUROPEAN PHYSICAL JOURNAL. E, SOFT MATTER 2020; 43:15. [PMID: 32086593 DOI: 10.1140/epje/i2020-11938-y] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2019] [Accepted: 02/11/2020] [Indexed: 06/10/2023]
Abstract
Self-consistent field theory (SCFT) is a powerful approach for computing the phase behavior of block polymers. We describe a fast version of the open-source Polymer Self-Consistent Field (PSCF) code that takes advantage of the massive parallelization provided by a graphical processing unit (GPU). Benchmarking double-precision calculations indicate up to 30× reduction in time to converge SCFT calculations of various diblock copolymer phases when compared to the Fortran CPU version of PSCF using the same algorithms, with the speed-up increasing with increasing unit cell size for the diblock polymer problems examined here. Where double-precision accuracy is not needed, single-precision calculations can provide speed-up of up to 60× in convergence time. These improvements in speed within an open-source format open up new vistas for SCFT-driven block polymer materials discovery by the community at large.
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Affiliation(s)
- Guo Kang Cheong
- Department of Chemical Engineering and Materials Science, University of Minnesota - Twin Cities, 421 Washington Avenue SE, 55455, Minneapolis, MN, USA
| | - Anshul Chawla
- Department of Chemical Engineering and Materials Science, University of Minnesota - Twin Cities, 421 Washington Avenue SE, 55455, Minneapolis, MN, USA
| | - David C Morse
- Department of Chemical Engineering and Materials Science, University of Minnesota - Twin Cities, 421 Washington Avenue SE, 55455, Minneapolis, MN, USA
| | - Kevin D Dorfman
- Department of Chemical Engineering and Materials Science, University of Minnesota - Twin Cities, 421 Washington Avenue SE, 55455, Minneapolis, MN, USA.
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28
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Das A, Limmer DT. Variational control forces for enhanced sampling of nonequilibrium molecular dynamics simulations. J Chem Phys 2019; 151:244123. [DOI: 10.1063/1.5128956] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Affiliation(s)
- Avishek Das
- Department of Chemistry, University of California, Berkeley, California 94720, USA
| | - David T. Limmer
- Department of Chemistry, University of California, Berkeley, California 94720, USA
- Kavli Energy NanoScience Institute, Berkeley, California 94720, USA
- Materials Science Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
- Chemical Science Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
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29
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Affiliation(s)
- Pengji Zhou
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - James C. Proctor
- Department of Materials Science and Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Greg van Anders
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA
- Department of Physics, University of Michigan, Ann Arbor, MI, USA
- Department of Physics, Engineering Physics, and Astronomy, Queen's University, Kingston, Ontario, Canada
| | - Sharon C. Glotzer
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA
- Department of Materials Science and Engineering, University of Michigan, Ann Arbor, MI, USA
- Department of Physics, University of Michigan, Ann Arbor, MI, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
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30
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Lindquist BA, Jadrich RB, Howard MP, Truskett TM. The role of pressure in inverse design for assembly. J Chem Phys 2019; 151:104104. [DOI: 10.1063/1.5112766] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Affiliation(s)
- Beth A. Lindquist
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, Texas 78712, USA
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - Ryan B. Jadrich
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, Texas 78712, USA
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
| | - Michael P. Howard
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, Texas 78712, USA
| | - Thomas M. Truskett
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, Texas 78712, USA
- Department of Physics, University of Texas at Austin, Austin, Texas 78712, USA
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31
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Kumar R, Coli GM, Dijkstra M, Sastry S. Inverse design of charged colloidal particle interactions for self assembly into specified crystal structures. J Chem Phys 2019; 151:084109. [DOI: 10.1063/1.5111492] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Rajneesh Kumar
- Theoretical Sciences Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Bengaluru 560064, India
| | - Gabriele M. Coli
- Soft Condensed Matter, Debye Institute for Nanomaterials Science, Utrecht University, Princetonplein 5, 3584 CC Utrecht, The Netherlands
| | - Marjolein Dijkstra
- Soft Condensed Matter, Debye Institute for Nanomaterials Science, Utrecht University, Princetonplein 5, 3584 CC Utrecht, The Netherlands
| | - Srikanth Sastry
- Theoretical Sciences Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Bengaluru 560064, India
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32
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Walsh DJ, Dutta S, Sing CE, Guironnet D. Engineering of Molecular Geometry in Bottlebrush Polymers. Macromolecules 2019. [DOI: 10.1021/acs.macromol.9b00845] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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33
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Zhang R, Zhang L, Lin J, Lin S. Customizing topographical templates for aperiodic nanostructures of block copolymers via inverse design. Phys Chem Chem Phys 2019; 21:7781-7788. [PMID: 30931439 DOI: 10.1039/c9cp00712a] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The limited complexity of self-assembled nanostructures of block copolymers seriously impedes their potential utility in the semiconductor industry. Therefore, the customizability of complex nanostructures has been a long-standing goal for the utilization of directed self-assembly in nanolithography. Herein, we integrated an advanced inverse design algorithm with a well-developed theoretical model to deduce inverse solutions of topographical templates to direct the self-assembly of block copolymers into reproducible target structures. The deduced templates were optimized by finely tuning the input parameters of the inverse design algorithm and through symmetric operation as well as nanopost elimination. More importantly, our developed algorithm has the capability to search inverse solutions of topographical templates for aperiodic nanostructures over exceptionally large areas. These results reveal design rules for guiding templates for the device-oriented nanostructures of block copolymers with prospective applications in nanolithography.
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Affiliation(s)
- Runrong Zhang
- Shanghai Key Laboratory of Advanced Polymeric Materials, State Key Laboratory of Bioreactor Engineering, Key Laboratory for Ultrafine Materials of Ministry of Education, School of Materials Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China.
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34
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Venkatasubramanian V. The promise of artificial intelligence in chemical engineering: Is it here, finally? AIChE J 2018. [DOI: 10.1002/aic.16489] [Citation(s) in RCA: 271] [Impact Index Per Article: 45.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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35
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Freedman SL, Hocky GM, Banerjee S, Dinner AR. Nonequilibrium phase diagrams for actomyosin networks. SOFT MATTER 2018; 14:7740-7747. [PMID: 30204203 PMCID: PMC6192427 DOI: 10.1039/c8sm00741a] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
Living cells dynamically modulate the local morphologies of their actin networks to perform biological functions, including force transduction, intracellular transport, and cell division. A major challenge is to understand how diverse structures of the actin cytoskeleton are assembled from a limited set of molecular building blocks. Here we study the spontaneous self-assembly of a minimal model of cytoskeletal materials, consisting of semiflexible actin filaments, crosslinkers, and molecular motors. Using coarse-grained simulations, we demonstrate that by changing concentrations and kinetics of crosslinkers and motors, as well as filament lengths, we can generate three distinct structural phases of actomyosin assemblies: bundled, polarity-sorted, and contracted. We introduce new metrics to distinguish these structural phases and demonstrate their functional roles. We find that the binding kinetics of motors and crosslinkers can be tuned to optimize contractile force generation, motor transport, and mechanical response. By quantitatively characterizing the relationships between the modes of cytoskeletal self-assembly, the resulting structures, and their functional consequences, our work suggests new principles for the design of active materials.
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Affiliation(s)
- Simon L. Freedman
- Department of Physics, The University of Chicago, 929 East 57th Street, Chicago, IL 60637, USA
| | - Glen M. Hocky
- James Franck Institute & Department of Chemistry, The University of Chicago, 929 East 57th Street, Chicago, IL 60637, USA, Chicago, IL, USA;
| | - Shiladitya Banerjee
- Department of Physics and Astronomy, University College London, Gower Street, London, WC1E-6BT
| | - Aaron R. Dinner
- James Franck Institute & Department of Chemistry, The University of Chicago, 929 East 57th Street, Chicago, IL 60637, USA, Chicago, IL, USA;
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36
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Jiang X, Li J, Lee V, Jaeger HM, Heinonen OG, de Pablo JJ. Evolutionary strategy for inverse charge measurements of dielectric particles. J Chem Phys 2018; 148:234302. [DOI: 10.1063/1.5027435] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Xikai Jiang
- Institute for Molecular Engineering, University of Chicago, Chicago, Illinois 60637, USA
| | - Jiyuan Li
- Institute for Molecular Engineering, University of Chicago, Chicago, Illinois 60637, USA
| | - Victor Lee
- James Franck Institute and Department of Physics, University of Chicago, Chicago, Illinois 60637, USA
| | - Heinrich M. Jaeger
- James Franck Institute and Department of Physics, University of Chicago, Chicago, Illinois 60637, USA
| | - Olle G. Heinonen
- Materials Science Division, Argonne National Laboratory, Lemont, Illinois 60439, USA
- Northwestern-Argonne Institute for Science and Engineering, Evanston, Illinois 60208, USA
| | - Juan J. de Pablo
- Institute for Molecular Engineering, University of Chicago, Chicago, Illinois 60637, USA
- Materials Science Division, Argonne National Laboratory, Lemont, Illinois 60439, USA
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37
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Hannon AF, Sunday DF, Bowen A, Khaira G, Ren J, Nealey PF, de Pablo JJ, Kline RJ. Optimizing self-consistent field theory block copolymer models with X-ray metrology. MOLECULAR SYSTEMS DESIGN & ENGINEERING 2018; 3:376-389. [PMID: 29892480 PMCID: PMC5992623 DOI: 10.1039/c7me00098g] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
A block copolymer self-consistent field theory (SCFT) model is used for direct analysis of experimental X-ray scattering data obtained from thin films of polystyrene-b-poly(methyl methacrylate) (PS-b-PMMA) made from directed self-assembly. In a departure from traditional approaches, which reconstruct the real space structure using simple geometric shapes, we build on recent work that has relied on physics-based models to determine shape profiles and extract thermodynamic processing information from the scattering data. More specifically, an SCFT model, coupled to a covariance matrix adaptation evolutionary strategy (CMAES), is used to find the set of simulation parameters for the model that best reproduces the scattering data. The SCFT model is detailed enough to capture the essential physics of the copolymer self-assembly, but sufficiently simple to rapidly produce structure profiles needed for interpreting the scattering data. The ability of the model to produce a matching scattering profile is assessed, and several improvements are proposed in order to more accurately recreate the experimental observations. The predicted parameters are compared to those extracted from model fits via additional experimental methods and with predicted parameters from direct particle-based simulations of the same model, which incorporate the effects of fluctuations. The Flory-Huggins interaction parameter for PS-b-PMMA is found to be in agreement with reported ranges for this material. These results serve to strengthen the case for relying on physics-based models for direct analysis of scattering and light signal based experiments.
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Affiliation(s)
- Adam F Hannon
- Materials Science and Engineering Division, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, MD 20899, USA
| | - Daniel F Sunday
- Materials Science and Engineering Division, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, MD 20899, USA
| | - Alec Bowen
- Institute for Molecular Engineering, University of Chicago, 5801 S Ellis Ave, Chicago, IL 60637, USA
| | - Gurdaman Khaira
- Mentor Graphics Corporation, 8005 Boeckman Rd, Wilsonville, OR 97070, USA
| | - Jiaxing Ren
- Institute for Molecular Engineering, University of Chicago, 5801 S Ellis Ave, Chicago, IL 60637, USA
| | - Paul F Nealey
- Institute for Molecular Engineering, University of Chicago, 5801 S Ellis Ave, Chicago, IL 60637, USA
- Argonne National Laboratory, 9700 Cass Ave, Lemont, IL 60439, USA
| | - Juan J de Pablo
- Institute for Molecular Engineering, University of Chicago, 5801 S Ellis Ave, Chicago, IL 60637, USA
- Argonne National Laboratory, 9700 Cass Ave, Lemont, IL 60439, USA
| | - R Joseph Kline
- Materials Science and Engineering Division, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, MD 20899, USA
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38
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Piñeros WD, Lindquist BA, Jadrich RB, Truskett TM. Inverse design of multicomponent assemblies. J Chem Phys 2018; 148:104509. [DOI: 10.1063/1.5021648] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Affiliation(s)
- William D. Piñeros
- Department of Chemistry, University of Texas at Austin, Austin, Texas 78712, USA
| | - Beth A. Lindquist
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, Texas 78712, USA
| | - Ryan B. Jadrich
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, Texas 78712, USA
| | - Thomas M. Truskett
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, Texas 78712, USA
- Department of Physics, University of Texas at Austin, Austin, Texas 78712, USA
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39
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Ferguson AL. Machine learning and data science in soft materials engineering. JOURNAL OF PHYSICS. CONDENSED MATTER : AN INSTITUTE OF PHYSICS JOURNAL 2018; 30:043002. [PMID: 29111979 DOI: 10.1088/1361-648x/aa98bd] [Citation(s) in RCA: 67] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
In many branches of materials science it is now routine to generate data sets of such large size and dimensionality that conventional methods of analysis fail. Paradigms and tools from data science and machine learning can provide scalable approaches to identify and extract trends and patterns within voluminous data sets, perform guided traversals of high-dimensional phase spaces, and furnish data-driven strategies for inverse materials design. This topical review provides an accessible introduction to machine learning tools in the context of soft and biological materials by 'de-jargonizing' data science terminology, presenting a taxonomy of machine learning techniques, and surveying the mathematical underpinnings and software implementations of popular tools, including principal component analysis, independent component analysis, diffusion maps, support vector machines, and relative entropy. We present illustrative examples of machine learning applications in soft matter, including inverse design of self-assembling materials, nonlinear learning of protein folding landscapes, high-throughput antimicrobial peptide design, and data-driven materials design engines. We close with an outlook on the challenges and opportunities for the field.
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Affiliation(s)
- Andrew L Ferguson
- Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign, 1304 West Green Street, Urbana, IL 61801, United States of America. Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, 600 South Mathews Avenue, Urbana, IL 61801, United States of America. Department of Physics, University of Illinois at Urbana-Champaign, 1110 West Green Street, Urbana, IL 61801, United States of America. Frederick Seitz Materials Research Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States of America. Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States of America
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40
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Kádár V, Danku Z, Kun F. Size scaling of failure strength with fat-tailed disorder in a fiber bundle model. Phys Rev E 2018; 96:033001. [PMID: 29346894 DOI: 10.1103/physreve.96.033001] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2017] [Indexed: 11/07/2022]
Abstract
We investigate the size scaling of the macroscopic fracture strength of heterogeneous materials when microscopic disorder is controlled by fat-tailed distributions. We consider a fiber bundle model where the strength of single fibers is described by a power law distribution over a finite range. Tuning the amount of disorder by varying the power law exponent and the upper cutoff of fibers' strength, in the limit of equal load sharing an astonishing size effect is revealed: For small system sizes the bundle strength increases with the number of fibers, and the usual decreasing size effect of heterogeneous materials is restored only beyond a characteristic size. We show analytically that the extreme order statistics of fibers' strength is responsible for this peculiar behavior. Analyzing the results of computer simulations we deduce a scaling form which describes the dependence of the macroscopic strength of fiber bundles on the parameters of microscopic disorder over the entire range of system sizes.
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Affiliation(s)
- Viktória Kádár
- Department of Theoretical Physics, University of Debrecen, P.O. Box 5, H-4010 Debrecen, Hungary
| | - Zsuzsa Danku
- Department of Theoretical Physics, University of Debrecen, P.O. Box 5, H-4010 Debrecen, Hungary
| | - Ferenc Kun
- Department of Theoretical Physics, University of Debrecen, P.O. Box 5, H-4010 Debrecen, Hungary
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41
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Madge J, Miller MA. Optimising minimal building blocks for addressable self-assembly. SOFT MATTER 2017; 13:7780-7792. [PMID: 29018850 DOI: 10.1039/c7sm01646h] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Addressable structures are characterised by the set of unique components from which they are built and by the specific location that each component occupies. For an addressable structure to self-assemble, its constituent building blocks must be encoded with sufficient information to define their positions with respect to each other and to enable them to navigate to those positions. DNA, with its vast scope for encoding specific interactions, has been successfully used to synthesise addressable systems of several hundred components. In this work we examine the complementary question of the minimal requirements for building blocks to undergo addressable self-assembly driven by a controlled temperature quench. Our testbed is an idealised model of cubic particles patterned with attractive interactions. We introduce a scheme for optimising the interactions using a variant of basin-hopping and a negative design principle. The designed building blocks are tested dynamically in simple target structures to establish how their complexity affects the limits of reliable self-assembly.
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Affiliation(s)
- Jim Madge
- Department of Chemistry, Durham University, South Road, Durham DH1 3LE, UK.
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42
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Khaira G, Doxastakis M, Bowen A, Ren J, Suh HS, Segal-Peretz T, Chen X, Zhou C, Hannon AF, Ferrier NJ, Vishwanath V, Sunday DF, Gronheid R, Kline RJ, de Pablo JJ, Nealey PF. Derivation of Multiple Covarying Material and Process Parameters Using Physics-Based Modeling of X-ray Data. Macromolecules 2017. [DOI: 10.1021/acs.macromol.7b00691] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Affiliation(s)
- Gurdaman Khaira
- Mentor: A Siemens Business, Wilsonville, Oregon 97070, United States
| | - Manolis Doxastakis
- Department
of Chemical and Biomolecular Engineering, University of Tennessee, Knoxville, Tennessee 37996, United States
| | - Alec Bowen
- Institute for Molecular Engineering, The University of Chicago, Chicago, Illinois 60637, United States
| | - Jiaxing Ren
- Institute for Molecular Engineering, The University of Chicago, Chicago, Illinois 60637, United States
| | - Hyo Seon Suh
- Institute for Molecular Engineering, The University of Chicago, Chicago, Illinois 60637, United States
| | - Tamar Segal-Peretz
- Department
of Chemical Engineering, Technion - Israel Institute of Technology, Haifa 32000, Israel
| | - Xuanxuan Chen
- Institute for Molecular Engineering, The University of Chicago, Chicago, Illinois 60637, United States
| | - Chun Zhou
- Institute for Molecular Engineering, The University of Chicago, Chicago, Illinois 60637, United States
| | - Adam F. Hannon
- Material
Science and Engineering Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States
| | | | | | - Daniel F. Sunday
- Material
Science and Engineering Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States
| | | | - R. Joseph Kline
- Material
Science and Engineering Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States
| | - Juan J. de Pablo
- Institute for Molecular Engineering, The University of Chicago, Chicago, Illinois 60637, United States
- Argonne National Laboratory, Argonne, Illinois 60439, United States
| | - Paul F. Nealey
- Institute for Molecular Engineering, The University of Chicago, Chicago, Illinois 60637, United States
- Argonne National Laboratory, Argonne, Illinois 60439, United States
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43
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Jadrich RB, Lindquist BA, Truskett TM. Probabilistic inverse design for self-assembling materials. J Chem Phys 2017. [DOI: 10.1063/1.4981796] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Affiliation(s)
- R. B. Jadrich
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, Texas 78712, USA
| | - B. A. Lindquist
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, Texas 78712, USA
| | - T. M. Truskett
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, Texas 78712, USA
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44
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Lindquist BA, Jadrich RB, Truskett TM. Communication: Inverse design for self-assembly via on-the-fly optimization. J Chem Phys 2016. [DOI: 10.1063/1.4962754] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Affiliation(s)
- Beth A. Lindquist
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, Texas 78712, USA
| | - Ryan B. Jadrich
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, Texas 78712, USA
| | - Thomas M. Truskett
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, Texas 78712, USA
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45
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Paradiso SP, Delaney KT, Fredrickson GH. Swarm Intelligence Platform for Multiblock Polymer Inverse Formulation Design. ACS Macro Lett 2016; 5:972-976. [PMID: 35607214 DOI: 10.1021/acsmacrolett.6b00494] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Multiblock polymers (MBPs) show great promise as a platform for synthesizing materials with highly customized nanostructures. In these systems, the subtle balance of chain configurational entropy and interactions between dissimilar block chemistries emerge a rich palette of self-assembled structural motifs that may be leveraged to impart novel functional, mechanical, or optical properties to the final material. Unfortunately, extensive study of MBP self-assembly has yielded few reliable heuristics for intuitively navigating the enormous molecular design space made available by modern polymer synthesis techniques. In order to make progress, new methods must be developed that allow researchers to efficiently screen molecular designs for achieving a desired state of self-assembly. Here, we introduce a platform for the automated discovery of tailored MBP formulations based on the Particle Swarm Optimization (PSO) method and a linear multiblock chain parametrization that enables continuous optimization of chain architecture. We apply the method to thin-film blends of linear ABC triblock polymers subject to lateral confinement, allowing all polymer and blend parameters to freely optimize in search of a prespecified, nontrivial target morphology. While we focus on pattern selection as a proof of principle, any computable equilibrium property can be optimized using the methods described here.
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Affiliation(s)
- Sean P. Paradiso
- Department of Chemical
Engineering, ‡Materials Research Laboratory, and §Materials Department, University of California, Santa
Barbara, California 93016, United States
| | - Kris T. Delaney
- Department of Chemical
Engineering, ‡Materials Research Laboratory, and §Materials Department, University of California, Santa
Barbara, California 93016, United States
| | - Glenn H. Fredrickson
- Department of Chemical
Engineering, ‡Materials Research Laboratory, and §Materials Department, University of California, Santa
Barbara, California 93016, United States
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46
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Ball P. Material witness: A new design course. NATURE MATERIALS 2016; 15:130. [PMID: 26796728 DOI: 10.1038/nmat4547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
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47
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van Anders G, Klotsa D, Karas AS, Dodd PM, Glotzer SC. Digital Alchemy for Materials Design: Colloids and Beyond. ACS NANO 2015; 9:9542-9553. [PMID: 26401754 DOI: 10.1021/acsnano.5b04181] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Starting with the early alchemists, a holy grail of science has been to make desired materials by modifying the attributes of basic building blocks. Building blocks that show promise for assembling new complex materials can be synthesized at the nanoscale with attributes that would astonish the ancient alchemists in their versatility. However, this versatility means that making a direct connection between building-block attributes and bulk structure is both necessary for rationally engineering materials and difficult because building block attributes can be altered in many ways. Here we show how to exploit the malleability of the valence of colloidal nanoparticle "elements" to directly and quantitatively link building-block attributes to bulk structure through a statistical thermodynamic framework we term "digital alchemy". We use this framework to optimize building blocks for a given target structure and to determine which building-block attributes are most important to control for self-assembly, through a set of novel thermodynamic response functions, moduli, and susceptibilities. We thereby establish direct links between the attributes of colloidal building blocks and the bulk structures they form. Moreover, our results give concrete solutions to the more general conceptual challenge of optimizing emergent behaviors in nature and can be applied to other types of matter. As examples, we apply digital alchemy to systems of truncated tetrahedra, rhombic dodecahedra, and isotropically interacting spheres that self-assemble diamond, fcc, and icosahedral quasicrystal structures, respectively. Although our focus is on colloidal systems, our methods generalize to any building blocks with adjustable interactions.
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Affiliation(s)
- Greg van Anders
- Department of Chemical Engineering, University of Michigan , Ann Arbor, Michigan 48109-2136, United States
| | - Daphne Klotsa
- Department of Chemical Engineering, University of Michigan , Ann Arbor, Michigan 48109-2136, United States
- School of Engineering and Applied Sciences, Harvard University , Cambridge, Massachusetts 02138, United States
- Department of Chemistry, University of Cambridge , Lensfield Road, Cambridge CB2 1EW, United Kingdom
| | - Andrew S Karas
- Department of Chemical Engineering, University of Michigan , Ann Arbor, Michigan 48109-2136, United States
| | - Paul M Dodd
- Department of Chemical Engineering, University of Michigan , Ann Arbor, Michigan 48109-2136, United States
| | - Sharon C Glotzer
- Department of Chemical Engineering, University of Michigan , Ann Arbor, Michigan 48109-2136, United States
- Department of Materials Science and Engineering, University of Michigan , Ann Arbor, Michigan 48109-2136, United States
- Biointerfaces Institute, University of Michigan , Ann Arbor, Michigan 48109-2800, United States
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