Force Fields for Macromolecular Assemblies Containing Diketopyrrolopyrrole and Thiophene

Accurate Quantum-Mechanically Derived Force-Fields through a Fragment-Based Approach: Balancing Specificity and Transferability in the Prediction of Self-Assembly in Soft Matter

The wide range of time/length scales covered by self-assembly in soft matter makes molecular dynamics (MD) the ideal candidate for simulating such a supramolecular phenomenon at an atomistic level. However, the reliability of MD outcomes heavily relies on the accuracy of the adopted force-field (FF). The spontaneous re-ordering in liquid crystalline materials stands as a clear example of such collective self-assembling processes, driven by a subtle and delicate balance between supramolecular interactions and single-molecule flexibility. General-purpose transferable FFs often dramatically fail to reproduce such complex phenomena, for example, the error on the transition temperatures being larger than 100 K. Conversely, quantum-mechanically derived force-fields (QMD-FFs), specifically tailored for the target system, were recently shown (J. Phys. Chem. Lett.2022,13, 243) to allow for the required accuracy as they not only well reproduced transition temperatures but also yielded a quantitative agreement with the experiment on a wealth of structural, dynamic, and thermodynamic properties. The main drawback of this strategy stands in the computational burden connected to the numerous quantum mechanical (QM) calculations usually required for a target-specific parameterization, which has undoubtedly hampered the routine application of QMD-FFs. In this work, we propose a fragment-based strategy to extend the applicability of QMD-FFs, in which the amount of QM calculations is significantly reduced, being a single-molecule-optimized geometry and its Hessian matrix the only QM information required. To validate this route, a new FF is assembled for a large mesogen, exploiting the parameters obtained for two smaller liquid crystalline molecules, in this and previous work. Lengthy MD simulations are carried out with the new transferred QMD-FF, observing again a spontaneous re-orientation in the correct range of temperatures, with good agreement with the available experimental measures. The present results strongly suggest that a partial transfer of QMD-FF parameters can be invoked without a significant loss of accuracy, thus paving the way to exploit the method's intrinsic predictive capabilities in the simulation of novel soft materials.

Predicting Spontaneous Orientational Self-Assembly: In Silico Design of Materials with Quantum Mechanically Derived Force Fields

De novo design of self-assembled materials hinges upon our ability to relate macroscopic properties to individual building blocks, thus characterizing in such supramolecular architectures a wide range of observables at varied time/length scales. This work demonstrates that quantum mechanical derived force fields (QMD-FFs) do satisfy this requisite and, most importantly, do so in a predictive manner. To this end, a specific FF, built solely based on the knowledge of the target molecular structure, is employed to reproduce the spontaneous transition to an ordered liquid crystal phase. The simulations deliver a multiscale portrait of such self-assembly processes, where conformational changes within the individual building blocks are intertwined with a 200 ns ensemble reorganization. The extensive characterization provided not only is in quantitative agreement with the experiment but also connects the time/length scales at which it was performed. Realizing QMD-FF predictive power and unmatched accuracy stands as an important leap forward for the bottom-up design of advanced supramolecular materials.

Local structuring of diketopyrrolopyrrole (DPP)-based oligomers from molecular dynamics simulations

The microscopic structure of high mobility semiconducting polymers is known to be essential for their performance but it cannot be easily deduced from the available experimental data. A series of short oligomers of diketopyrrolopyrrole (DPP)-based materials that display high charge mobility are studied by molecular dynamics simulations to understand their local structuring at an atomic level. Different analyses are proposed to compare the ability of different oligomers to form large aggregates and their driving force. The simulations show that the tendency for this class of materials to form aggregates is driven by the interaction between DPP fragments, but this is modulated by the other conjugated fragments of the materials which affect the rigidity of the polymer and, ultimately, the size of the aggregates that are formed. The main structural features and the electronic structure of the oligomers are fairly similar above the glass transition temperature and at room temperature.

Automated Parameterization of Quantum Mechanically Derived Force Fields for Soft Materials and Complex Fluids: Development and Validation

The reliability of molecular dynamics (MD) simulations in predicting macroscopic properties of complex fluids and soft materials, such as liquid crystals, colloidal suspensions, or polymers, relies on the accuracy of the adopted force field (FF). We present an automated protocol to derive specific and accurate FFs, fully based on ab initio quantum mechanical (QM) data. The integration of the Joyce and Picky procedures, recently proposed by our group to provide an accurate description of simple liquids, is here extended to larger molecules, capable of exhibiting more complex fluid phases. While the standard Joyce protocol is employed to parameterize the intramolecular FF term, a new automated procedure is here proposed to handle the computational cost of the QM calculations required for the parameterization of the intermolecular FF term. The latter is thus obtained by integrating the old Picky procedure with a fragmentation reconstruction method (FRM) that allows for a reliable, yet computationally feasible sampling of the intermolecular energy surface at the QM level. The whole FF parameterization protocol is tested on a benchmark liquid crystal, and the performances of the resulting quantum mechanically derived (QMD) FF were compared with those delivered by a general-purpose, transferable one, and by the third, "hybrid" FF, where only the bonded terms were refined against QM data. Lengthy atomistic MD simulations are carried out with each FF on extended 5CB systems in both isotropic and nematic phases, eventually validating the proposed protocol by comparing the resulting macroscopic properties with other computational models and with experiments. The QMD-FF yields the best performances, reproducing both phases in the correct range of temperatures and well describing their structure, dynamics, and thermodynamic properties, thus providing a clear protocol that may be explored to predict such properties on other complex fluids or soft materials.