skip to main content


Title: Moving beyond the constraints of chemistry via crystal structure discovery with isotropic multiwell pair potentials

The rigid constraints of chemistry—dictated by quantum mechanics and the discrete nature of the atom—limit the set of observable atomic crystal structures. What structures are possible in the absence of these constraints? Here, we systematically crystallize one-component systems of particles interacting with isotropic multiwell pair potentials. We investigate two tunable families of pairwise interaction potentials. Our simulations self-assemble a multitude of crystal structures ranging from basic lattices to complex networks. Sixteen of the structures have natural analogs spanning all coordination numbers found in inorganic chemistry. Fifteen more are hitherto unknown and occupy the space between covalent and metallic coordination environments. The discovered crystal structures constitute targets for self-assembly and expand our understanding of what a crystal structure can look like.

 
more » « less
NSF-PAR ID:
10228922
Author(s) / Creator(s):
; ; ; ;
Publisher / Repository:
Proceedings of the National Academy of Sciences
Date Published:
Journal Name:
Proceedings of the National Academy of Sciences
Volume:
118
Issue:
21
ISSN:
0027-8424
Page Range / eLocation ID:
Article No. e2024034118
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. 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. 
    more » « less
  2. null (Ed.)
    Abstract This paper introduces the use of topological data analysis (TDA) as an unsupervised machine learning tool to uncover classification criteria in complex inorganic crystal chemistries. Using the apatite chemistry as a template, we track through the use of persistent homology the topological connectivity of input crystal chemistry descriptors on defining similarity between different stoichiometries of apatites. It is shown that TDA automatically identifies a hierarchical classification scheme within apatites based on the commonality of the number of discrete coordination polyhedra that constitute the structural building units common among the compounds. This information is presented in the form of a visualization scheme of a barcode of homology classifications, where the persistence of similarity between compounds is tracked. Unlike traditional perspectives of structure maps, this new “Materials Barcode” schema serves as an automated exploratory machine learning tool that can uncover structural associations from crystal chemistry databases, as well as to achieve a more nuanced insight into what defines similarity among homologous compounds. 
    more » « less
  3. Local atomic environment descriptors (LAEDs) are used in the materials science and chemistry communities, for example, for the development of machine learning interatomic potentials. Despite the fact that LAEDs have been extensively studied and benchmarked for various applications, global structure descriptors (GSDs), i.e., descriptors for entire molecules or crystal structures, have been mostly developed independently based on other approaches. Here, we propose a systematically improvable methodology for constructing a space of representations of GSDs from LAEDs by incorporating statistical information and information about chemical elements. We apply the method to construct GSDs of varying complexity for lithium thiophosphate structures that are of interest as solid electrolytes and use an information-theoretic approach to obtain an optimally compressed GSD. Finally, we report the performance of the compressed GSD for energy prediction tasks. 
    more » « less
  4. Abstract

    Naturally occurring polymeric structures often consist of 1D polymer chains intricately folded and entwined through non‐covalent bonds, adopting precise topologies crucial for their functionality. The exploration of crystalline 1D polymers through dynamic covalent chemistry (DCvC) and supramolecular interactions represents a novel approach for developing crystalline polymers. This study shows that sub‐angstrom differences in the counter‐ion size can lead to various helical covalent polymer (HCP) topologies, including a novel metal‐coordination HCP (m‐HCP) motif. Single‐crystal X‐ray diffraction (SCXRD) analysis of HCP−Na revealed that double helical pairs are formed by sodium ions coordinating to spiroborate linkages to form rectangular pores. The double helices are interpenetrated by the unreacted diols coordinating sodium ions. The reticulation of the m‐HCP structure was demonstrated by the successful synthesis of HCP−K. Finally, ion‐exchange studies were conducted to show the interconversion between HCP structures. This research illustrates how seemingly simple modifications, such as changes in counter‐ion size, can significantly influence the polymer topology and determine which supramolecular interactions dominate the crystal lattice.

     
    more » « less
  5. Metal–organic frameworks (MOFs) are crystalline materials that self-assemble from inorganic nodes and organic linkers, and isoreticular chemistry allows for modular and synthetic reagents of various sizes. In this study, a MOF’s components—metal nodes and organic linkers—are constructed in a coarse-grained model from isotropic beads, retaining the basic symmetries of the molecular components. Lennard-Jones and Weeks– Chandler–Andersen pair potentials are used to model attractive and repulsive particle interactions, respectively. We analyze the crystallinity of the self-assembled products and explore the role of modulators—molecules that compete with the organic linkers in binding to the metal nodes, and which we construct analogously—during the selfassembly process of defect-engineered MOFs. Coarse-grained simulation allows for the uncoupling of experimentally interdependent variables to broadly map and determine essential MOF self-assembly conditions, among which are properties of the modulator: binding strength, size (steric hindrance), and concentration. Of these, the simulated modulator’s binding strength has the most pronounced effect on the resulting MOF’s crystal size. 
    more » « less