Atomistic Perspective of the Crystallization of Naphthodithiophene in Two Hydrocarbon Solvents

Abstract

A priori control over crystallization, whether one attempts to induce or prevent crystallization, is critical across many areas of materials chemistry. The capacity to regulate crystallization in solution requires a comprehensive understanding of the deceivingly simple interrelationships among the chemical compositions and structures of the solute and solvent and external environmental parameters such as temperature and pressure. While there have been tremendous advances in crystal structure prediction based solely on chemical composition and molecular structure, there remains insufficient knowledge, especially at the atomic scale, to regulate crystallinity with precision during solution processing. Here, we make use of the constant chemical potential molecular dynamics (CμMD) algorithm to explore the growth of naptho[1,2-b:5,6-b′]dithiophene (NDT), a molecule with structural anisotropy and no specific chemical / noncovalent intermolecular interaction directors (e.g., no hydrogen bonding or pendant functional groups) that packs in the commonly found herringbone (two-dimensional layer) packing motif. We demonstrate at the atomic scale how variations in NDT (solute) concentration, solvent, and temperature impact both the initiation of crystal formation (nucleation) and the propagation of crystals (growth) along different crystallographic directions. In total, this investigation reveals atomic-scale thermodynamic and kinetic details important to crystallization that can be exploited to control the processing of organic materials.

Machine-Learning Discovered Crystallization Model for Two Dimensional Covalent Organic Frameworks: Towards Precise Control of the Crystal Quality

“…Exerting precise control over the crystallization process is critical in improving material quality and performance, yet often being a formidable challenge. [1][2][3][4][5][6] It has been observed that crystallization via particle attachment is prevalent across various materials, rendering the classical nucleation and growth theory inapplicable. [7][8][9][10][11] An example is the two-dimensional covalent organic frameworks (2D COFs), which have attracted significant attention in recent years owing to their promising applications in catalysis, [12][13][14][15][16] optoelectronics, [17][18][19][20][21][22][23] energy storage devices, [24][25][26][27][28] and molecular separation [29][30][31][32][33] .…”

Machine-Learning Discovered Crystallization Model for Two-Dimensional Covalent Organic Frameworks: Towards Precise Control of the Crystal Quality

“…Exerting precise control over the crystallization process is critical in improving material quality and performance, yet often being a formidable challenge. [1][2][3][4][5][6] It has been observed that crystallization via particle attachment is prevalent across various materials, rendering the classical nucleation and growth theory inapplicable. [7][8][9][10][11] An example is the two-dimensional covalent organic frameworks (2D COFs), which have attracted significant attention in recent years owing to their promising applications in catalysis, [12][13][14][15][16] optoelectronics, [17][18][19][20][21][22][23] energy storage devices, [24][25][26][27][28] and molecular separation.…”