1
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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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2
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Matin S, Allen AEA, Smith J, Lubbers N, Jadrich RB, Messerly R, Nebgen B, Li YW, Tretiak S, Barros K. Machine Learning Potentials with the Iterative Boltzmann Inversion: Training to Experiment. J Chem Theory Comput 2024. [PMID: 38307009 DOI: 10.1021/acs.jctc.3c01051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2024]
Abstract
Methodologies for training machine learning potentials (MLPs) with quantum-mechanical simulation data have recently seen tremendous progress. Experimental data have a very different character than simulated data, and most MLP training procedures cannot be easily adapted to incorporate both types of data into the training process. We investigate a training procedure based on iterative Boltzmann inversion that produces a pair potential correction to an existing MLP using equilibrium radial distribution function data. By applying these corrections to an MLP for pure aluminum based on density functional theory, we observe that the resulting model largely addresses previous overstructuring in the melt phase. Interestingly, the corrected MLP also exhibits improved performance in predicting experimental diffusion constants, which are not included in the training procedure. The presented method does not require autodifferentiating through a molecular dynamics solver and does not make assumptions about the MLP architecture. Our results suggest a practical framework for incorporating experimental data into machine learning models to improve the accuracy of molecular dynamics simulations.
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Affiliation(s)
- Sakib Matin
- Department of Physics, Boston University, Boston, Massachusetts 02215, United States
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87546, United States
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87546, United States
| | - Alice E A Allen
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87546, United States
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87546, United States
| | - Justin Smith
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87546, United States
- NVIDIA Corp., Santa Clara, California 95051, United States
| | - Nicholas Lubbers
- Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Ryan B Jadrich
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87546, United States
| | - Richard Messerly
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87546, United States
| | - Benjamin Nebgen
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87546, United States
| | - Ying Wai Li
- Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Sergei Tretiak
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87546, United States
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87546, United States
- Center for Integrated Nanotechnologies, Los Alamos National Laboratory, Los Alamos, New Mexico 87546, United States
| | - Kipton Barros
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87546, United States
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87546, United States
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3
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Pan H, Dshemuchadse J. Targeted Discovery of Low-Coordinated Crystal Structures via Tunable Particle Interactions. ACS NANO 2023; 17:7157-7169. [PMID: 37042936 DOI: 10.1021/acsnano.2c09131] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Particles interacting via isotropic, multiwell pair potentials have been shown to self-assemble into a range of crystal structures, yet how the characteristics of the underlying interaction potential give rise to the resultant structure remains largely unknown. We have thus developed a functional form for the interaction potential in which all features can be tuned independently. We perform continuous parameter space searches by systematically changing pairs of parameters, controlling the various features of the interaction potential. By enforcing a repulsive first well (controlling particle interactions of the first neighbor shell), we stimulate the formation of low-coordinated assemblies. We report the self-assembly of 20 previously unknown crystal structure types, 14 of which have low coordination numbers. Despite limiting the search to a small region of the vast parameter space of possible particle interactions, a wealth of complexity and symmetry is apparent within these crystal structures, which include clathrates with empty cages and low-symmetry structures. Our findings suggest that an unknown number of previously undiscovered crystal structure configurations are possible through self-assembly, which can serve as interesting design targets for soft condensed matter synthesis.
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Affiliation(s)
- Hillary Pan
- Department of Materials Science and Engineering, Cornell University, Ithaca, New York 14853, United States
| | - Julia Dshemuchadse
- Department of Materials Science and Engineering, Cornell University, Ithaca, New York 14853, United States
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4
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Rivera-Rivera LY, Moore TC, Glotzer SC. Inverse design of triblock Janus spheres for self-assembly of complex structures in the crystallization slot via digital alchemy. SOFT MATTER 2023; 19:2726-2736. [PMID: 36974942 DOI: 10.1039/d2sm01593e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
The digital alchemy framework is an extended ensemble simulation technique that incorporates particle attributes as thermodynamic variables, enabling the inverse design of colloidal particles for desired behavior. Here, we extend the digital alchemy framework for the inverse design of patchy spheres that self-assemble into target crystal structures. To constrain the potentials to non-trivial solutions, we conduct digital alchemy simulations with constant second virial coefficient. We optimize the size, range, and strength of patchy interactions in model triblock Janus spheres to self-assemble the 2D kagome and snub square lattices and the 3D pyrochlore lattice, and demonstrate self-assembly of all three target structures with the designed models. The particles designed for the kagome and snub square lattices assemble into high quality clusters of their target structures, while competition from similar polymorphs lower the yield of the pyrochlore assemblies. We find that the alchemically designed potentials do not always match physical intuition, illustrating the ability of the method to find nontrivial solutions to the optimization problem. We identify a window of second virial coefficients that result in self-assembly of the target structures, analogous to the crystallization slot in protein crystallization.
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Affiliation(s)
| | - Timothy C Moore
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA.
| | - Sharon C Glotzer
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA.
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
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5
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Torquato S, Wang H. Precise determination of pair interactions from pair statistics of many-body systems in and out of equilibrium. Phys Rev E 2022; 106:044122. [PMID: 36397532 DOI: 10.1103/physreve.106.044122] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 09/22/2022] [Indexed: 06/16/2023]
Abstract
The determination of the pair potential v(r) that accurately yields an equilibrium state at positive temperature T with a prescribed pair correlation function g_{2}(r) or corresponding structure factor S(k) in d-dimensional Euclidean space R^{d} is an outstanding inverse statistical mechanics problem with far-reaching implications. Recently, Zhang and Torquato [Phys. Rev. E 101, 032124 (2020)2470-004510.1103/PhysRevE.101.032124] conjectured that any realizable g_{2}(r) or S(k) corresponding to a translationally invariant nonequilibrium system can be attained by a classical equilibrium ensemble involving only (up to) effective pair interactions. Testing this conjecture for nonequilibrium systems as well as for nontrivial equilibrium states requires improved inverse methodologies. We have devised an optimization algorithm to precisely determine effective pair potentials that correspond to pair statistics of general translationally invariant disordered many-body equilibrium or nonequilibrium systems at positive temperatures. This methodology utilizes a parameterized family of pointwise basis functions for the potential function whose initial form is informed by small-, intermediate- and large-distance behaviors dictated by statistical-mechanical theory. Subsequently, a nonlinear optimization technique is utilized to minimize an objective function that incorporates both the target pair correlation function g_{2}(r) and structure factor S(k) so that the small intermediate- and large-distance correlations are very accurately captured. To illustrate the versatility and power of our methodology, we accurately determine the effective pair interactions of the following four diverse target systems: (1) Lennard-Jones system in the vicinity of its critical point, (2) liquid under the Dzugutov potential, (3) nonequilibrium random sequential addition packing, and (4) a nonequilibrium hyperuniform "cloaked" uniformly randomized lattice. We found that the optimized pair potentials generate corresponding pair statistics that accurately match their corresponding targets with total L_{2}-norm errors that are an order of magnitude smaller than that of previous methods. The results of our investigation lend further support to the Zhang-Torquato conjecture. Furthermore, our algorithm will enable one to probe systems with identical pair statistics but different higher-body statistics, which will shed light on the well-known degeneracy problem of statistical mechanics.
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Affiliation(s)
- Salvatore Torquato
- Department of Chemistry, Department of Physics, Princeton Institute of Materials, and Program in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey 08544, USA
- School of Natural Sciences, Institute for Advanced Study, 1 Einstein Drive, Princeton, New Jersey 08540, USA
| | - Haina Wang
- Department of Chemistry, Princeton University, Princeton, New Jersey 08544, USA
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6
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Prudente FV, Marques JMC. Thermodynamic Signatures of Structural Transitions and Dissociation of Charged Colloidal Clusters: A Parallel Tempering Monte Carlo Study. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27082581. [PMID: 35458778 PMCID: PMC9032479 DOI: 10.3390/molecules27082581] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 03/30/2022] [Accepted: 04/14/2022] [Indexed: 01/05/2023]
Abstract
Computational simulation of colloidal systems make use of empirical interaction potentials that are founded in well-established theory. In this work, we have performed parallel tempering Monte Carlo (PTMC) simulations to calculate heat capacity and to assess structural transitions, which may occur in charged colloidal clusters whose effective interactions are described by a sum of pair potentials with attractive short-range and repulsive long-range components. Previous studies on these systems have shown that the global minimum structure varies from spherical-type shapes for small-size clusters to Bernal spiral and “beaded-necklace” shapes at intermediate and larger sizes, respectively. In order to study both structural transitions and dissociation, we have organized the structures appearing in the PTMC calculations by three sets according to their energy: (i) low-energy structures, including the global minimum; (ii) intermediate-energy “beaded-necklace” motifs; (iii) high-energy linear and branched structures that characterize the dissociative clusters. We observe that, depending on the cluster, either peaks or shoulders on the heat–capacity curve constitute thermodynamics signatures of dissociation and structural transitions. The dissociation occurs at T=0.20 for all studied clusters and it is characterized by the appearance of a significant number of linear structures, while the structural transitions corresponding to unrolling the Bernal spiral are quite dependent on the size of the colloidal system.
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Affiliation(s)
- Frederico V. Prudente
- Instituto de Física, Universidade Federal da Bahia, Salvador 40170-115, BA, Brazil
- Correspondence: (F.V.P.); (J.M.C.M.)
| | - Jorge M. C. Marques
- CQC–IMS, Department of Chemistry, University of Coimbra, 3004-535 Coimbra, Portugal
- Correspondence: (F.V.P.); (J.M.C.M.)
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7
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Wang J, Wang Y, Chen Y. Inverse Design of Materials by Machine Learning. MATERIALS 2022; 15:ma15051811. [PMID: 35269043 PMCID: PMC8911677 DOI: 10.3390/ma15051811] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 02/13/2022] [Accepted: 02/24/2022] [Indexed: 02/04/2023]
Abstract
It is safe to say that every invention that has changed the world has depended on materials. At present, the demand for the development of materials and the invention or design of new materials is becoming more and more urgent since peoples' current production and lifestyle needs must be changed to help mitigate the climate. Structure-property relationships are a vital paradigm in materials science. However, these relationships are often nonlinear, and the pattern is likely to change with length scales and time scales, posing a huge challenge. With the development of physics, statistics, computer science, etc., machine learning offers the opportunity to systematically find new materials. Especially by inverse design based on machine learning, one can make use of the existing knowledge without attempting mathematical inversion of the relevant integrated differential equation of the electronic structure but by using backpropagation to overcome local minimax traps and perform a fast calculation of the gradient information for a target function concerning the design variable to find the optimizations. The methodologies have been applied to various materials including polymers, photonics, inorganic materials, porous materials, 2-D materials, etc. Different types of design problems require different approaches, for which many algorithms and optimization approaches have been demonstrated in different scenarios. In this mini-review, we will not specifically sum up machine learning methodologies, but will provide a more material perspective and summarize some cut-edging studies.
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Affiliation(s)
- Jia Wang
- School of Space and Environment, Beihang University, Beijing 102206, China;
| | - Yingxue Wang
- National Engineering Laboratory for Risk Perception and Prevention, Beijing 100081, China
- Correspondence:
| | - Yanan Chen
- School of Materials Science and Engineering, Tianjin University, Tianjin 300072, China;
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8
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Lieu UT, Yoshinaga N. Inverse design of two-dimensional structure by self-assembly of patchy particles. J Chem Phys 2022; 156:054901. [DOI: 10.1063/5.0072234] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
| | - Natsuhiko Yoshinaga
- WPI Advanced Institute for Materials Research, Tohoku University - Katahira Campus, Japan
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9
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Cencer MM, Moore JS, Assary RS. Machine learning for polymeric materials: an introduction. POLYM INT 2021. [DOI: 10.1002/pi.6345] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Affiliation(s)
- Morgan M Cencer
- Department of Chemistry University of Illinois at Urbana‐Champaign Urbana IL USA
- Materials Science Division Argonne National Laboratory Lemont IL USA
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana‐Champaign Urbana IL USA
| | - Jeffrey S Moore
- Department of Chemistry University of Illinois at Urbana‐Champaign Urbana IL USA
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana‐Champaign Urbana IL USA
| | - Rajeev S Assary
- Materials Science Division Argonne National Laboratory Lemont IL USA
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10
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11
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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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12
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Goodall REA, Lee AA. Data-driven approximations to the bridge function yield improved closures for the Ornstein-Zernike equation. SOFT MATTER 2021; 17:5393-5400. [PMID: 33969369 DOI: 10.1039/d1sm00402f] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
A key challenge for soft materials design and coarse-graining simulations is determining interaction potentials between components that give rise to desired condensed-phase structures. In theory, the Ornstein-Zernike equation provides an elegant framework for solving this inverse problem. Pioneering work in liquid state theory derived analytical closures for the framework. However, these analytical closures are approximations, valid only for specific classes of interaction potentials. In this work, we combine the physics of liquid state theory with machine learning to infer a closure directly from simulation data. The resulting closure is more accurate than commonly used closures across a broad range of interaction potentials.
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Affiliation(s)
| | - Alpha A Lee
- Cavendish Laboratory, University of Cambridge, Cambridge, UK.
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13
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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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14
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Shen K, Sherck N, Nguyen M, Yoo B, Köhler S, Speros J, Delaney KT, Fredrickson GH, Shell MS. Learning composition-transferable coarse-grained models: Designing external potential ensembles to maximize thermodynamic information. J Chem Phys 2020; 153:154116. [DOI: 10.1063/5.0022808] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Affiliation(s)
- Kevin Shen
- Department of Chemical Engineering, University of California, Santa Barbara, California 93106, USA
- Materials Research Laboratory, University of California, Santa Barbara, California 93106, USA
| | - Nicholas Sherck
- Department of Chemical Engineering, University of California, Santa Barbara, California 93106, USA
| | - My Nguyen
- Department of Chemical Engineering, University of California, Santa Barbara, California 93106, USA
| | - Brian Yoo
- BASF Corporation, Tarrytown, New York 10591, USA
| | | | - Joshua Speros
- California Research Alliance (CARA) by BASF, Berkeley, California 94720, USA
| | - Kris T. Delaney
- Materials Research Laboratory, University of California, Santa Barbara, California 93106, USA
| | - Glenn H. Fredrickson
- Department of Chemical Engineering, University of California, Santa Barbara, California 93106, USA
- Materials Research Laboratory, University of California, Santa Barbara, California 93106, USA
- Department of Materials Engineering, University of California, Santa Barbara, California 93106, USA
| | - M. Scott Shell
- Department of Chemical Engineering, University of California, Santa Barbara, California 93106, USA
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15
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Role of Entropy in Colloidal Self-Assembly. ENTROPY 2020; 22:e22080877. [PMID: 33286648 PMCID: PMC7517482 DOI: 10.3390/e22080877] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2020] [Revised: 08/03/2020] [Accepted: 08/05/2020] [Indexed: 12/13/2022]
Abstract
Entropy plays a key role in the self-assembly of colloidal particles. Specifically, in the case of hard particles, which do not interact or overlap with each other during the process of self-assembly, the free energy is minimized due to an increase in the entropy of the system. Understanding the contribution of entropy and engineering it is increasingly becoming central to modern colloidal self-assembly research, because the entropy serves as a guide to design a wide variety of self-assembled structures for many technological and biomedical applications. In this work, we highlight the importance of entropy in different theoretical and experimental self-assembly studies. We discuss the role of shape entropy and depletion interactions in colloidal self-assembly. We also highlight the effect of entropy in the formation of open and closed crystalline structures, as well as describe recent advances in engineering entropy to achieve targeted self-assembled structures.
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16
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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: 21] [Impact Index Per Article: 5.3] [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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17
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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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18
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Mahynski NA, Mao R, Pretti E, Shen VK, Mittal J. Grand canonical inverse design of multicomponent colloidal crystals. SOFT MATTER 2020; 16:3187-3194. [PMID: 32134420 DOI: 10.1039/c9sm02426c] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Inverse design methods are powerful computational approaches for creating colloidal systems which self-assemble into a target morphology by reverse engineering the Hamiltonian of the system. Despite this, these optimization procedures tend to yield Hamiltonians which are too complex to be experimentally realized. An alternative route to complex structures involves the use of several different components, however, conventional inverse design methods do not explicitly account for the possibility of phase separation into compositionally distinct structures. Here, we present an inverse design scheme for multicomponent colloidal systems by combining active learning with a method to directly compute their ground state phase diagrams. This explicitly accounts for phase separation and can locate stable regions of Hamiltonian parameter space which grid-based surveys are prone to miss. Using this we design low-density, binary structures with Lennard-Jones-like pairwise interactions that are simpler than in the single component case and potentially realizable in an experimental setting. This reinforces the concept that ground states of simple, multicomponent systems might be rich with previously unappreciated diversity, enabling the assembly of non-trivial structures with only few simple components instead of a single complex one.
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Affiliation(s)
- Nathan A Mahynski
- Chemical Sciences Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899-8320, USA.
| | - Runfang Mao
- Department of Chemical and Biomolecular Engineering, Lehigh University, 111 Research Dr., Bethlehem, Pennsylvania 18015-4791, USA
| | - Evan Pretti
- Department of Chemical and Biomolecular Engineering, Lehigh University, 111 Research Dr., Bethlehem, Pennsylvania 18015-4791, USA
| | - Vincent K Shen
- Chemical Sciences Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899-8320, USA.
| | - Jeetain Mittal
- Department of Chemical and Biomolecular Engineering, Lehigh University, 111 Research Dr., Bethlehem, Pennsylvania 18015-4791, USA
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19
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Zhang G, Torquato S. Realizable hyperuniform and nonhyperuniform particle configurations with targeted spectral functions via effective pair interactions. Phys Rev E 2020; 101:032124. [PMID: 32289971 DOI: 10.1103/physreve.101.032124] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Accepted: 02/24/2020] [Indexed: 11/07/2022]
Abstract
The capacity to identify realizable many-body configurations associated with targeted functional forms for the pair correlation function g_{2}(r) or its corresponding structure factor S(k) is of great fundamental and practical importance. While there are obvious necessary conditions that a prescribed structure factor at number density ρ must satisfy to be configurationally realizable, sufficient conditions are generally not known due to the infinite degeneracy of configurations with different higher-order correlation functions. A major aim of this paper is to expand our theoretical knowledge of the class of pair correlation functions or structure factors that are realizable by classical disordered ensembles of particle configurations, including exotic "hyperuniform" varieties. We first introduce a theoretical formalism that provides a means to draw classical particle configurations from canonical ensembles with certain pairwise-additive potentials that could correspond to targeted analytical functional forms for the structure factor. This formulation enables us to devise an improved algorithm to construct systematically canonical-ensemble particle configurations with such targeted pair statistics, whenever realizable. As a proof of concept, we test the algorithm by targeting several different structure factors across dimensions that are known to be realizable and one hyperuniform target that is known to be nontrivially unrealizable. Our algorithm succeeds for all realizable targets and appropriately fails for the unrealizable target, demonstrating the accuracy and power of the method to numerically investigate the realizability problem. Subsequently, we also target several families of structure-factor functions that meet the known necessary realizability conditions but are not known to be realizable by disordered hyperuniform point configurations, including d-dimensional Gaussian structure factors, d-dimensional generalizations of the two-dimensional one-component plasma (OCP), and the d-dimensional Fourier duals of the previous OCP cases. Moreover, we also explore unusual nonhyperuniform targets, including "hyposurficial" and "antihyperuniform" examples. In all of these instances, the targeted structure factors are achieved with high accuracy, suggesting that they are indeed realizable by equilibrium configurations with pairwise interactions at positive temperatures. Remarkably, we also show that the structure factor of nonequilibrium perfect glass, specified by two-, three-, and four-body interactions, can also be realized by equilibrium pair interactions at positive temperatures. Our findings lead us to the conjecture that any realizable structure factor corresponding to either a translationally invariant equilibrium or nonequilibrium system can be attained by an equilibrium ensemble involving only effective pair interactions. Our investigation not only broadens our knowledge of analytical functional forms for g_{2}(r) and S(k) associated with disordered point configurations across dimensions but also deepens our understanding of many-body physics. Moreover, our work can be applied to the design of materials with desirable physical properties that can be tuned by their pair statistics.
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Affiliation(s)
- Ge Zhang
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Salvatore Torquato
- Department of Chemistry, Princeton University, Princeton, New Jersey 08544, USA; Department of Physics, Princeton University, Princeton, New Jersey 08544, USA; Princeton Institute for the Science and Technology of Materials, Princeton University, Princeton, New Jersey 08544, USA; and Program in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey 08544, USA
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20
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Nowak C, Misra M, Escobedo FA. Framework for Inverse Mapping Chemistry-Agnostic Coarse-Grained Simulation Models into Chemistry-Specific Models. J Chem Inf Model 2019; 59:5045-5056. [DOI: 10.1021/acs.jcim.9b00232] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Christian Nowak
- School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, New York 14853, United States
| | - Mayank Misra
- School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, New York 14853, United States
| | - Fernando A. Escobedo
- School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, New York 14853, United States
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21
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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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22
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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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23
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Mahynski NA, Pretti E, Shen VK, Mittal J. Using symmetry to elucidate the importance of stoichiometry in colloidal crystal assembly. Nat Commun 2019; 10:2028. [PMID: 31048700 PMCID: PMC6497718 DOI: 10.1038/s41467-019-10031-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2018] [Accepted: 04/09/2019] [Indexed: 01/05/2023] Open
Abstract
We demonstrate a method based on symmetry to predict the structure of self-assembling, multicomponent colloidal mixtures. This method allows us to feasibly enumerate candidate structures from all symmetry groups and is many orders of magnitude more computationally efficient than combinatorial enumeration of these candidates. In turn, this permits us to compute ground-state phase diagrams for multicomponent systems. While tuning the interparticle potentials to produce potentially complex interactions represents the conventional route to designing exotic lattices, we use this scheme to demonstrate that simple potentials can also give rise to such structures which are thermodynamically stable at moderate to low temperatures. Furthermore, for a model two-dimensional colloidal system, we illustrate that lattices forming a complete set of 2-, 3-, 4-, and 6-fold rotational symmetries can be rationally designed from certain systems by tuning the mixture composition alone, demonstrating that stoichiometric control can be a tool as powerful as directly tuning the interparticle potentials themselves.
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Affiliation(s)
- Nathan A Mahynski
- Chemical Sciences Division, National Institute of Standards and Technology, Gaithersburg, MD, 20899-8320, USA.
| | - Evan Pretti
- Department of Chemical and Biomolecular Engineering, Lehigh University, 111 Research Drive, Bethlehem, PA, 18015-4791, USA
| | - Vincent K Shen
- Chemical Sciences Division, National Institute of Standards and Technology, Gaithersburg, MD, 20899-8320, USA
| | - Jeetain Mittal
- Department of Chemical and Biomolecular Engineering, Lehigh University, 111 Research Drive, Bethlehem, PA, 18015-4791, USA.
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24
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Banerjee D, Lindquist BA, Jadrich RB, Truskett TM. Assembly of particle strings via isotropic potentials. J Chem Phys 2019; 150:124903. [DOI: 10.1063/1.5088604] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- D. Banerjee
- 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
| | - R. B. Jadrich
- 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
- Department of Physics, University of Texas at Austin, Austin, Texas 78712, USA
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25
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Adorf CS, Antonaglia J, Dshemuchadse J, Glotzer SC. Inverse design of simple pair potentials for the self-assembly of complex structures. J Chem Phys 2018; 149:204102. [DOI: 10.1063/1.5063802] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Affiliation(s)
- Carl S. Adorf
- Department of Chemical Engineering, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - James Antonaglia
- Department of Physics, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Julia Dshemuchadse
- Department of Chemical Engineering, 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
- Department of Materials Science and Engineering, University of Michigan, Ann Arbor, Michigan 48109, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, Michigan 48109, USA
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26
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Colla T, Blaak R, Likos CN. Quenching of fully symmetric mixtures of oppositely charged microgels: the role of soft stiffness. SOFT MATTER 2018; 14:5106-5120. [PMID: 29876574 DOI: 10.1039/c8sm00441b] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Using molecular dynamics simulations, we investigate the self-assembly of a coarse-grained binary system of oppositely charged microgels, symmetric in size and concentration. The microgel pair interactions are described by an effective pair potential which implicitly accounts for the averaged ionic contributions, in addition to a short-range elastic repulsion that accounts for the overlapping of the polymer chains, the latter being described by the Hertzian interaction. Particular emphasis is placed on the role played by the strength of the soft repulsive interaction on the resulting particle aggregation. It is found that the possibility of particle inter-penetration in oppositely charged soft particles results in a much wider variety of cluster morphologies in comparison with their hard-spheres counterparts. Specifically, the softness of the steric interactions enhances the competition between repulsive and attractive electrostatic interactions, leading to the formation of aggregates that are comprised of strongly bounded charged particles displaying a low degree of charge ordering.
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Affiliation(s)
- Thiago Colla
- Instituto de Física, Universidade Federal de Ouro Preto, CEP 35400-000, Ouro Preto, MG, Brazil
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27
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Kumar A, Molinero V. Why Is Gyroid More Difficult to Nucleate from Disordered Liquids than Lamellar and Hexagonal Mesophases? J Phys Chem B 2018; 122:4758-4770. [PMID: 29620902 DOI: 10.1021/acs.jpcb.8b02381] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Block copolymers, surfactants, and biomolecules form lamellar, hexagonal, and gyroid mesophases. Across these systems, the nucleation of lamellar from the disordered liquid is the easiest and the nucleation of gyroid the most challenging. This poses the question of what are the factors that determine the rates of nucleation of the mesophases and whether they are controlled by the complexity of the structures or the thermodynamics of nucleation. Here, we use molecular simulations to investigate the nucleation and thermodynamics of lamellar, hexagonal, and gyroid in a binary mixture of particles that produces the same mesophases as those of surfactants and block copolymers. We demonstrate that a combination of averaged bond-order parameters q̅2 and q̅8 identifies and distinguishes the three mesophases. We use these parameters to track the microscopic process of nucleation of each mesophase and investigate the existence of heterogeneous nucleation (cross-nucleation) between mesophases. We estimate the surface tensions of the liquid/mesophase interfaces from nucleation rates using classical nucleation theory and find that they are comparable for the three mesophases with values that are about a third of those expected for liquid-crystal interfaces. The driving forces for nucleation, on the other hand, are quite different and increase in the order gyroid < hexagonal < lamellar at any temperature. We find that the nucleation rates of the mesophases follow the order of their driving forces. We conclude that the difficulty to nucleate the gyroid originates in its lower temperature of melting and extremely low entropy of melting compared to those of the hexagonal and lamellar mesophases.
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Affiliation(s)
- Abhinaw Kumar
- Department of Chemistry , The University of Utah , 315 South 1400 East , Salt Lake City , Utah 84112-0850 , United States
| | - Valeria Molinero
- Department of Chemistry , The University of Utah , 315 South 1400 East , Salt Lake City , Utah 84112-0850 , United States
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28
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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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29
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Lindquist BA, Jadrich RB, Piñeros WD, Truskett TM. Inverse Design of Self-Assembling Frank-Kasper Phases and Insights Into Emergent Quasicrystals. J Phys Chem B 2018; 122:5547-5556. [DOI: 10.1021/acs.jpcb.7b11841] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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30
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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: 49] [Impact Index Per Article: 8.2] [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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31
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Gobbo G, Bellucci MA, Tribello GA, Ciccotti G, Trout BL. Nucleation of Molecular Crystals Driven by Relative Information Entropy. J Chem Theory Comput 2018; 14:959-972. [DOI: 10.1021/acs.jctc.7b01027] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Affiliation(s)
- Gianpaolo Gobbo
- Department
of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Michael A. Bellucci
- Department
of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Gareth A. Tribello
- Atomistic
Simulation Centre, School of Mathematics and Physics, Queen’s University Belfast, Belfast BT7 1NN, United Kingdom
| | - Giovanni Ciccotti
- Università di Roma La Sapienza, Piazzale Aldo Moro 5, 00185 Roma, Italy
- School
of Physics, University College of Dublin (UCD), Belfield, Dublin 4, Ireland
| | - Bernhardt L. Trout
- Department
of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
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32
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Escobedo FA. Optimizing the formation of colloidal compounds with components of different shapes. J Chem Phys 2017; 147:214501. [DOI: 10.1063/1.5006047] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Fernando A. Escobedo
- School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, New York 14853, USA
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33
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Schoelz JE, Leisner S. Setting Up Shop: The Formation and Function of the Viral Factories of Cauliflower mosaic virus. FRONTIERS IN PLANT SCIENCE 2017; 8:1832. [PMID: 29163571 PMCID: PMC5670102 DOI: 10.3389/fpls.2017.01832] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2017] [Accepted: 10/10/2017] [Indexed: 05/23/2023]
Abstract
Similar to cells, viruses often compartmentalize specific functions such as genome replication or particle assembly. Viral compartments may contain host organelle membranes or they may be mainly composed of viral proteins. These compartments are often termed: inclusion bodies (IBs), viroplasms or viral factories. The same virus may form more than one type of IB, each with different functions, as illustrated by the plant pararetrovirus, Cauliflower mosaic virus (CaMV). CaMV forms two distinct types of IBs in infected plant cells, those composed mainly of the viral proteins P2 (which are responsible for transmission of CaMV by insect vectors) and P6 (required for viral intra-and inter-cellular infection), respectively. P6 IBs are the major focus of this review. Much of our understanding of the formation and function of P6 IBs comes from the analyses of their major protein component, P6. Over time, the interactions and functions of P6 have been gradually elucidated. Coupled with new technologies, such as fluorescence microscopy with fluorophore-tagged viral proteins, these data complement earlier work and provide a clearer picture of P6 IB formation. As the activities and interactions of the viral proteins have gradually been determined, the functions of P6 IBs have become clearer. This review integrates the current state of knowledge on the formation and function of P6 IBs to produce a coherent model for the activities mediated by these sophisticated virus-manufacturing machines.
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Affiliation(s)
- James E. Schoelz
- Division of Plant Sciences, University of Missouri, Columbia, MO, United States
| | - Scott Leisner
- Department of Biological Sciences, University of Toledo, Toledo, OH, United States
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34
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Zanjani MB, Crocker JC, Sinno T. Self-assembly with colloidal clusters: facile crystal design using connectivity landscape analysis. SOFT MATTER 2017; 13:7098-7105. [PMID: 28850137 DOI: 10.1039/c7sm01407d] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Recent experimental and theoretical studies demonstrate that prefabricated micron-scale colloidal clusters functionalized with DNA oligomers offer a practical way for introducing anisotropic interactions, significantly extending the scope of DNA-mediated colloidal assembly, and enabling the formation of interesting crystalline superstructures that are otherwise inaccessible with short-ranged, spherically symmetric interactions. However, it is apparent that the high-dimensional parameter space that defines the geometric and interaction properties of such systems poses an obstacle to assembly design and optimization. Here, we present a geometrical analysis that generates connectivity landscapes for target superstructures, greatly reducing the space over which subsequent experimental trials must search. We focus on several superstructures that are assembled from binary systems comprised of 'merged' or 'sintered' tetrahedral clusters and single spheres. We also validate and extend the analytical constraint approach with direct MD simulations of superstructure nucleation and growth.
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Affiliation(s)
- Mehdi B Zanjani
- Department of Mechanical and Manufacturing Engineering, Miami University, Oxford, OH 45056, USA.
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35
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Khadilkar MR, Paradiso S, Delaney KT, Fredrickson GH. Inverse Design of Bulk Morphologies in Multiblock Polymers Using Particle Swarm Optimization. Macromolecules 2017. [DOI: 10.1021/acs.macromol.7b01204] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Affiliation(s)
- Mihir R. Khadilkar
- Materials
Research Laboratory, ‡Department of Chemical Engineering, and §Materials Department, University of California, Santa Barbara, Santa Barbara, California 93106, United States
| | - Sean Paradiso
- Materials
Research Laboratory, ‡Department of Chemical Engineering, and §Materials Department, University of California, Santa Barbara, Santa Barbara, California 93106, United States
| | - Kris T. Delaney
- Materials
Research Laboratory, ‡Department of Chemical Engineering, and §Materials Department, University of California, Santa Barbara, Santa Barbara, California 93106, United States
| | - Glenn H. Fredrickson
- Materials
Research Laboratory, ‡Department of Chemical Engineering, and §Materials Department, University of California, Santa Barbara, Santa Barbara, California 93106, United States
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36
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Mahynski NA, Zerze H, Hatch HW, Shen VK, Mittal J. Assembly of multi-flavored two-dimensional colloidal crystals. SOFT MATTER 2017; 13:5397-5408. [PMID: 28702631 PMCID: PMC5828173 DOI: 10.1039/c7sm01005b] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
We systematically investigate the assembly of binary multi-flavored colloidal mixtures in two dimensions. In these mixtures all pairwise interactions between species may be tuned independently. This introduces an additional degree of freedom over more traditional binary mixtures with fixed mixing rules, which is anticipated to open new avenues for directed self-assembly. At present, colloidal self-assembly into non-trivial lattices tends to require either high pressures for isotropically interacting particles, or the introduction of directionally anisotropic interactions. Here we demonstrate tunable assembly into a plethora of structures which requires neither of these conditions. We develop a minimal model that defines a three-dimensional phase space containing one dimension for each pairwise interaction, then employ various computational techniques to map out regions of this phase space in which the system self-assembles into these different morphologies. We then present a mean-field model that is capable of reproducing these results for size-symmetric mixtures, which reveals how to target different structures by tuning pairwise interactions, solution stoichiometry, or both. Concerning particle size asymmetry, we find that domains in this model's phase space, corresponding to different morphologies, tend to undergo a continuous "rotation" whose magnitude is proportional to the size asymmetry. Such continuity enables one to estimate the relative stability of different lattices for arbitrary size asymmetries. Owing to its simplicity and accuracy, we expect this model to serve as a valuable design tool for engineering binary colloidal crystals from multi-flavored components.
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Affiliation(s)
- Nathan A Mahynski
- Chemical Sciences Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899-8320, USA.
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