Computational discovery of molecular C(60) encapsulants with an evolutionary algorithm

Recent advances in supramolecular fullerene chemistry

Fullerene chemistry has come a long way since 1990, when the first bulk production of C60 was reported. In the past decade, progress in supramolecular chemistry has opened some remarkable and previously unexpected opportunities regarding the selective (multiple) functionalization of fullerenes and their (self)assembly into larger structures and frameworks. The purpose of this review article is to provide a comprehensive overview of these recent developments. We describe how macrocycles and cages that bind strongly to C60 can be used to block undesired addition patterns and thus allow the selective preparation of single-isomer addition products. We also discuss how the emergence of highly shape-persistent macrocycles has opened opportunities for the study of photoactive fullerene dyads and triads as well as the preparation of mechanically interlocked compounds. The preparation of two- or three-dimensional fullerene materials is another research area that has seen remarkable progress over the past few years. Due to the rapidly decreasing price of C60 and C70, we believe that these achievements will translate into all fields where fullerenes have traditionally (third-generation solar cells) and more recently been applied (catalysis, spintronics).

Frontiers of molecular crystal structure prediction for pharmaceuticals and functional organic materials

The reliability of organic molecular crystal structure prediction has improved tremendously in recent years. Crystal structure predictions for small, mostly rigid molecules are quickly becoming routine. Structure predictions for larger, highly flexible molecules are more challenging, but their crystal structures can also now be predicted with increasing rates of success. These advances are ushering in a new era where crystal structure prediction drives the experimental discovery of new solid forms. After briefly discussing the computational methods that enable successful crystal structure prediction, this perspective presents case studies from the literature that demonstrate how state-of-the-art crystal structure prediction can transform how scientists approach problems involving the organic solid state. Applications to pharmaceuticals, porous organic materials, photomechanical crystals, organic semi-conductors, and nuclear magnetic resonance crystallography are included. Finally, efforts to improve our understanding of which predicted crystal structures can actually be produced experimentally and other outstanding challenges are discussed.

Reverse Designing the Wavelength-Specific Thermally Activation Delayed Fluorescent Molecules Using a Genetic Algorithm Coupled with Cheap QM Methods

Genetic algorithm (GA) optimization coupled with the semiempirical intermediate neglect of differential overlap (INDO)/CIS method is presented to inversely design the red thermally activation delayed fluorescent (TADF) molecules. According to the predefined donor-acceptor (DA) library to build an AD_n-type TADF candidate, we utilized the chemical notation language SMILES code to generate a TADF molecule and apply the RDKit program to produce the initial 3D molecular structure. A combined fitness function is proposed to evaluate the performance of the functional-lead TADF molecule. The fitness function includes three key parameters, i.e., the emission wavelength, the energy gap (ΔE_ST) between the lowest singlet (S₁)- and triplet (T₁)-excited states, and the oscillator strengths for electron transition from S₀ and S₁. A cheap QM method, i.e., INDO/CIS, on the basis of an xTB-optimized molecular geometry is applied to quickly calculate the fitness function. Finally, the GA approach is utilized to globally search for the wavelength-specific TADF molecules under our predefined DA library, and the optimum 630 nm red and 660 nm deep red TADF molecules are inversely designed according to the evolution of molecular fitness functions.

Materials Precursor Score: Modeling Chemists' Intuition for the Synthetic Accessibility of Porous Organic Cage Precursors

Computation is increasingly being used to try to accelerate the discovery of new materials. One specific example of this is porous molecular materials, specifically porous organic cages, where the porosity of the materials predominantly comes from the internal cavities of the molecules themselves. The computational discovery of novel structures with useful properties is currently hindered by the difficulty in transitioning from a computational prediction to synthetic realization. Attempts at experimental validation are often time-consuming, expensive, and frequently, the key bottleneck of material discovery. In this work, we developed a computational screening workflow for porous molecules that includes consideration of the synthetic difficulty of material precursors, aimed at easing the transition between computational prediction and experimental realization. We trained a machine learning model by first collecting data on 12,553 molecules categorized either as "easy-to-synthesize" or "difficult-to-synthesize" by expert chemists with years of experience in organic synthesis. We used an approach to address the class imbalance present in our data set, producing a binary classifier able to categorize easy-to-synthesize molecules with few false positives. We then used our model during computational screening for porous organic molecules to bias toward precursors whose easier synthesis requirements would make them promising candidates for experimental realization and material development. We found that even by limiting precursors to those that are easier-to-synthesize, we are still able to identify cages with favorable, and even some rare, properties.

stk: An extendable Python framework for automated molecular and supramolecular structure assembly and discovery

Computational software workflows are emerging as all-in-one solutions to speed up the discovery of new materials. Many computational approaches require the generation of realistic structural models for property prediction and candidate screening. However, molecular and supramolecular materials represent classes of materials with many potential applications for which there is no go-to database of existing structures or general protocol for generating structures. Here, we report a new version of the supramolecular toolkit, stk, an open-source, extendable, and modular Python framework for general structure generation of (supra)molecular structures. Our construction approach works on arbitrary building blocks and topologies and minimizes the input required from the user, making stk user-friendly and applicable to many material classes. This version of stk includes metal-containing structures and rotaxanes as well as general implementation and interface improvements. Additionally, this version includes built-in tools for exploring chemical space with an evolutionary algorithm and tools for database generation and visualization. The latest version of stk is freely available at github.com/lukasturcani/stk.

Structural, Electronic, and Nonlinear Optical Properties of C66H4 and C70Cl6 Encapsulating Li and F Atoms

Recently, nonclassical fullerene derivatives C66H4 and C70Cl6, which both contain two negatively curved moieties of heptagons, have been successfully synthesized. Inspired by these experimental achievements, the structural and electronic properties of C66H4, C70Cl6, Li@C66H4, F@C66H4, Li@C70Cl6, and F@C70Cl6 were systematical studied through density functional theory calculations in this work. Our results show that the reduction of the front molecular orbital gap of fullerene derivatives occurs with the introduction of Li and F atoms. After quantitative analysis of back-donations of charge between an encapsulated atom and an external carbon cage, it is found that C66H4 and C70Cl6 prefer to act as electron acceptors. It is interesting to note that the strong covalent nature of the interactions between a F atom and a carbon cage is observed, whereas the weak covalent and strong ionic interactions occur between a Li atom and a carbon cage. On the other hand, according to the first hyperpolarizability results, the encapsulation of the Li atom enhances the nonlinear optical response of fullerene derivatives. This work provides a strategy to improve nonlinear optical properties of C66H4 and C70Cl6, reveals the internal mechanism of the contribution from Li and F atoms to endohedral fullerene derivatives, and will contribute to the designation of endohedral fullerene derivative devices.

Integrating Computational and Experimental Workflows for Accelerated Organic Materials Discovery

Organic materials find application in a range of areas, including optoelectronics, sensing, encapsulation, molecular separations, and photocatalysis. The discovery of materials is frustratingly slow however, particularly when contrasted to the vast chemical space of possibilities based on the near limitless options for organic molecular precursors. The difficulty in predicting the material assembly, and consequent properties, of any molecule is another significant roadblock to targeted materials design. There has been significant progress in the development of computational approaches to screen large numbers of materials, for both their structure and properties, helping guide synthetic researchers toward promising materials. In particular, artificial intelligence techniques have the potential to make significant impact in many elements of the discovery process. Alongside this, automation and robotics are increasing the scale and speed with which materials synthesis can be realized. Herein, the focus is on demonstrating the power of integrating computational and experimental materials discovery programmes, including both a summary of key situations where approaches can be combined and a series of case studies that demonstrate recent successes.

High-Throughput Approaches for the Discovery of Supramolecular Organic Cages

The assembly of complex molecules, such as organic cages, can be achieved through supramolecular and dynamic covalent strategies. Their use in a range of applications has been demonstrated, including gas uptake, molecular separations, and in catalysis. However, the targeted design and synthesis of new species for particular applications is challenging, particularly as the systems become more complex. High-throughput computation-only and experiment-only approaches have been developed to streamline the discovery process, although are still not widely implemented. Additionally, combined hybrid workflows can dramatically accelerate the discovery process and lead to the serendipitous discovery and rationalisation of new supramolecular assemblies that would not have been designed based on intuition alone. This Minireview focuses on the advances in high-throughput approaches that have been developed and applied in the discovery of supramolecular organic cages.

Supramolecular Metallacycles and Their Binding of Fullerenes

The synthesis of a new triaminoguanidinium-based ligand with three tris-chelating [NNO]-binding pockets and C3 symmetry is described. The reaction of tris-(2-pyridinylene-N-oxide)triaminoguanidinium salts with zinc(II) formate leads to the formation of cyclic supramolecular coordination compounds which in solution bind fullerenes in their spherical cavities. The rapid encapsulation of C60 can be observed by NMR spectroscopy and single-crystal X-ray diffraction and is verified using computation.