Inverse Design of Self-Assembling Frank-Kasper Phases and Insights Into Emergent Quasicrystals

Lindquist, Beth A.; Jadrich, Ryan B.; Piñeros, William D.; Truskett, Thomas M.

doi:10.1021/acs.jpcb.7b11841

Cited by 30 publications

(26 citation statements)

References 80 publications

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“…Among these are so-called Laves phases, found in intermetallic compounds of the type AB 2 (such as MgCu 2 ) with relatively small ratios of atomic radius (up to around 1.67). Laves phases have also been seen in simulations of hard spheres with 12% dispersity 4 .…”

mentioning

confidence: 71%

How all sizes fit together

Ball

2019

Nat. Mater.

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confidence: 71%

How all sizes fit together

Ball

2019

Nat. Mater.

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“…This method has been used to design isotropic pair potentials for assembling phases with complex morphologies. 46,47 Adapted from Ref. 46, with the permission of AIP Publishing.…”

Section: A Thermodynamic Descriptorsmentioning

confidence: 99%

“…Adapted with permission from Ref. 47, Copyright 2018 American Chemical Society. D: Forward simulations generate an ensemble of data that is used to skew the probability distribution toward configurations that contribute more toward a targeted structure or property than average.…”

Section: A Thermodynamic Descriptorsmentioning

confidence: 99%

Inverse methods for design of soft materials

Sherman

Howard

Lindquist

et al. 2020

The Journal of Chemical Physics

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Functional soft materials, comprising colloidal and molecular building blocks that self-organize into complex structures as a result of their tunable interactions, enable a wide array of technological applications. Inverse methods provide systematic means for navigating their inherently high-dimensional design spaces to create materials with targeted properties. While multiple physically motivated inverse strategies have been successfully implemented in silico, their translation to guiding experimental materials discovery has thus far been limited to a handful of proof-of-concept studies. In this Perspective, we discuss recent advances in inverse methods for design of soft materials that address two challenges: (1) methodological limitations that prevent such approaches from satisfying design constraints and (2) computational challenges that limit the size and complexity of systems that can be addressed. Strategies that leverage machine learning have proven particularly effective, including methods to discover order parameters that characterize complex structural motifs and schemes to efficiently compute macroscopic properties from the underlying structure. We also highlight promising opportunities to improve the experimental realizability of materials designed computationally, including discovery of materials with functionality at multiple thermodynamic states, design of externally directed assembly protocols that are simple to implement in experiments, and strategies to improve the accuracy and computational efficiency of experimentally relevant models. arXiv:2004.00181v1 [cond-mat.soft] 1 Apr 2020

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“…31 Overfitting may also be prevented with early stopping for more broadly constrained search spaces, for example when the solutions are limited to a specific class of functions, such as repulsive, monotonically decreasing functions 22,32 or parameterized splines that effectively implement a lower limit on all feature length scales. 33 The FF-REM method does not rely on such constraints, but instead steers the optimization towards smoother and simpler solutions by the repeated application of a filter function in Fourier space (k-space). This approach is especially advantageous during early exploration, e.g., to determine whether any solution exists at all, or when there is no specific desired functional form.…”

Section: Iterationsmentioning

confidence: 99%

“…To apply this algorithm to the derivation of IPPs for the assembly of solid structures, we compute the RDF from position distributions of harmonic crystals, where particles are bound to their ideal crystal sites through harmonic bonds similarly to methods previously described in the literature. 22,33 The harmonic bond constant K was chosen such that the peaks within the measured RDF are sufficiently distinct to reliably characterize the structure, usually in a range of K = [100, 800], but always low enough to avoid singularities.…”

Section: Fourier-filtered Relative Entropy Minimizationmentioning

confidence: 99%

Inverse design of simple pair potentials for the self-assembly of complex structures

Adorf

Antonaglia

Dshemuchadse

et al. 2018

The Journal of Chemical Physics

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The synthesis of complex materials through the self-assembly of particles at the nanoscale provides opportunities for the realization of novel material properties. However, the inverse design process to create experimentally feasible interparticle interaction strategies is uniquely challenging. Standard methods for the optimization of isotropic pair potentials tend toward overfitting, resulting in solutions with too many features and length scales that are challenging to map to mechanistic models. Here we introduce a method for the optimization of simple pair potentials that minimizes the relative entropy of the complex target structure while directly considering only those length scales most relevant for self-assembly. Our approach maximizes the relative information of a target pair distribution function with respect to an ansatz distribution function via an iterative update process. During this process, we filter high frequencies from the Fourier spectrum of the pair potential, resulting in interaction potentials that are smoother and simpler in real space, and therefore likely easier to make. We show that pair potentials obtained by this method assemble their target structure more robustly with respect to optimization method parameters than potentials optimized without filtering.

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Inverse Design of Self-Assembling Frank-Kasper Phases and Insights Into Emergent Quasicrystals

Cited by 30 publications

References 80 publications

How all sizes fit together

How all sizes fit together

Inverse methods for design of soft materials

Inverse design of simple pair potentials for the self-assembly of complex structures

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