stk: A python toolkit for supramolecular assembly

Construction of stable porous organic cages: from the perspective of chemical bonds

Porous organic cages (POCs) are constructed from purely organic synthons by covalent linkages with intrinsic cavities and have shown potential applications in many areas. However, the majority of POC synthesis methods reported thus far have relied on dynamically reversible imine linkages, which can be metastable and unstable under humid or harsh chemical conditions. This instability significantly hampers their research prospects and practical applications. Consequently, strategies to enhance the chemical stability of POCs by modifying imine bonds and developing robust covalent linkages are imperative for realizing the full potential of these materials. In this review, we aim to highlight recent advancements in synthesizing chemical-stable POCs through these approaches and their associated applications. Additionally, we propose further strategies for creating stable POCs and discuss future opportunities for practical applications.

CageCavityCalc (C3): A Computational Tool for Calculating and Visualizing Cavities in Molecular Cages

Organic(porous) and metal-organic cages are promising biomimetic platforms with diverse applications spanning recognition, sensing, and catalysis. The key to the emergence of these functions is the presence of well-defined inner cavities capable of binding a wide range of guest molecules and modulating their properties. However, despite the myriad cage architectures currently available, the rational design of structurally diverse and functional cages with specific host-guest properties remains challenging. Efficiently predicting such properties is critical for accelerating the discovery of novel functional cages. Herein, we introduce CageCavityCalc (C3), a Python-based tool for calculating the cavity size of molecular cages. The code is available on GitHub at https://github.com/VicenteMartiCentelles/CageCavityCalc. C3 utilizes a novel algorithm that enables the rapid calculation of cavity sizes for a wide range of molecular structures and porous systems. Moreover, C3 facilitates easy visualization of the computed cavity size alongside hydrophobic and electrostatic potentials, providing insights into host-guest interactions within the cage. Furthermore, the calculated cavity can be visualized using widely available visualization software, such as PyMol, VMD, or ChimeraX. To enhance user accessibility, a PyMol plugin has been created, allowing nonspecialists to use this tool without requiring computer programming expertise. We anticipate that the deployment of this computational tool will significantly streamline cage cavity calculations, thereby accelerating the discovery of functional cages.

Python tools for structural tasks in chemistry

In recent decades, the use of computational approaches and artificial intelligence in the scientific environment has become more widespread. In this regard, the popular and versatile programming language Python has attracted considerable attention from scientists in the field of chemistry. It is used to solve a variety of chemical and structural problems, including calculating descriptors, molecular fingerprints, graph construction, and computing chemical reaction networks. Python offers high-quality visualization tools for analyzing chemical spaces and compound libraries. This review is a list of tools for the above tasks, including scripts, libraries, ready-made programs, and web interfaces. Inevitably this manuscript does not claim to be an all-encompassing handbook including all the existing Python-based structural chemistry codes. The review serves as a starting point for scientists wishing to apply automatization or optimization to routine chemistry problems.

Streamlining the automated discovery of porous organic cages

Self-assembly through dynamic covalent chemistry (DCC) can yield a range of multi-component organic assemblies. The reversibility and dynamic nature of DCC has made prediction of reaction outcome particularly difficult and thus slows the discovery rate of new organic materials. In addition, traditional experimental processes are time-consuming and often rely on serendipity. Here, we present a streamlined hybrid workflow that combines automated high-throughput experimentation, automated data analysis, and computational modelling, to accelerate the discovery process of one particular subclass of molecular organic materials, porous organic cages. We demonstrate how the design and implementation of this workflow aids in the identification of organic cages with desirable properties. The curation of a precursor library of 55 tri- and di-topic aldehyde and amine precursors enabled the experimental screening of 366 imine condensation reactions experimentally, and 1464 hypothetical organic cage outcomes to be computationally modelled. From the screen, 225 cages were identified experimentally using mass spectrometry, 54 of which were cleanly formed as a single topology as determined by both turbidity measurements and 1H NMR spectroscopy. Integration of these characterisation methods into a fully automated Python pipeline, named cagey, led to over a 350-fold decrease in the time required for data analysis. This work highlights the advantages of combining automated synthesis, characterisation, and analysis, for large-scale data curation towards an accessible data-driven materials discovery approach.

Design and assembly of porous organic cages

Porous organic cages (POCs) represent a notable category of porous materials, showing remarkable material properties due to their inherent porosity. Unlike extended frameworks which are constructed by strong covalent or coordination bonds, POCs are composed of discrete molecular units held together by weak intermolecular forces. Their structure and chemical traits can be systematically tailored, making them suitable for a range of applications including gas storage and separation, molecular separation and recognition, catalysis, and proton and ion conduction. This review provides a comprehensive overview of POCs, covering their synthesis methods, structure and properties, computational approaches, and applications, serving as a primer for those who are new to the domain. A special emphasis is placed on the growing role of computational methods, highlighting how advanced data-driven techniques and automation are increasingly aiding the rapid exploration and understanding of POCs. We conclude by addressing the prevailing challenges and future prospects in the field.

High-throughput virtual screening of organic second-order nonlinear optical chromophores within the donor-π-bridge-acceptor framework

In view of the theoretical importance and huge application potential of second-order nonlinear optical (NLO) materials, it is of great significance to conduct high-throughput virtual screening (HTVS) on a compound library to find candidate NLO chromophores. Under the donor-π-bridge-acceptor structural framework, a virtual compound library (size = 27 090) was constructed by enumeration of structural fragments. The kernel property adopted for optimization is the static first hyperpolarizability (β0). By combining machine learning and quantum chemical calculations, we have performed an HTVS procedure to sieve NLO chromophores out, and the response mechanism of the selected optimal NLO chromophores was examined. We have found: (a) The multi-layer perceptron/extended connectivity fingerprint combination with 20% selection ratio gives the highest prediction accuracy for the studied systems. (b) The two optimal donors are bis(4-diphenylaminophenyl)aminyl and bis(4-tert-butylphenyl)aminyl; the optimal π-bridges are composed of two thiophenyl, selenophenyl or furanyl units; and the two optimal acceptors are tri-s-triazinyl and 2,3-dicyanopyrazinyl. (c) The no. 1 candidate molecule can exhibit a calculated β0 equal to 8.55 × 104 a.u. (d) The difference in NLO responses of the optimal 16 molecules comes from the synergistic interaction of ES1, Δμ and f, by employing the two-level model. In addition, the sizable Δμ and f allow the studied optimal molecules to obtain a large NLO response in the meantime keeping a not-too-low excitation energy (retaining good optical transparency in the restricted range of the visible spectrum region). (e) With further modification on the acceptor, the designed DPA-π-TRZ-A' (A' = CN or NO2, π = oligo-thiophenyl or selenophenyl) systems can exhibit a rather large NLO response (maximum β0 = 3.17 × 105 a.u.), hence should have considerable potential as second-order NLO chromophores. With the above observations, we expect to provide some insight for the research community into the HTVS of organic second-order NLO chromophores.

Deep generative design of porous organic cages via a variational autoencoder

Porous organic cages (POCs) are a class of porous molecular materials characterised by their tunable, intrinsic porosity; this functional property makes them candidates for applications including guest storage and separation. Typically formed via dynamic covalent chemistry reactions from multifunctionalised molecular precursors, there is an enormous potential chemical space for POCs due to the fact they can be formed by combining two relatively small organic molecules, which themselves have an enormous chemical space. However, identifying suitable molecular precursors for POC formation is challenging, as POCs often lack shape persistence (the cage collapses upon solvent removal with loss of its cavity), thus losing a key functional property (porosity). Generative machine learning models have potential for targeted computational design of large functional molecular systems such as POCs. Here, we present a deep-learning-enabled generative model, Cage-VAE, for the targeted generation of shape-persistent POCs. We demonstrate the capacity of Cage-VAE to propose novel, shape-persistent POCs, via integration with multiple efficient sampling methods, including Bayesian optimisation and spherical linear interpolation.

Picking the lock of coordination cage catalysis

The design principles of metallo-organic assembly reactions have facilitated access to hundreds of coordination cages of varying size and shape. Many of these assemblies possess a well-defined cavity capable of hosting a guest, pictorially mimicking the action of a substrate binding to the active site of an enzyme. While there are now a growing collection of coordination cages that show highly proficient catalysis, exhibiting both excellent activity and efficient turnover, this number is still small compared to the vast library of metal-organic structures that are known. In this review, we will attempt to unpick and discuss the key features that make an effective coordination cage catalyst, linking structure to activity (and selectivity) using lessons learnt from both experimental and computational analysis of the most notable exemplars. We will also provide an outlook for this area, reasoning why coordination cages have the potential to become the gold-standard in (synthetic) non-covalent catalysis.

Computational modeling to assist in the discovery of supramolecular materials

Computational modeling is increasingly used to assist in the discovery of supramolecular materials. Supramolecular materials are typically primarily built from organic components that are self-assembled through noncovalent bonding and have potential applications, including in selective binding, sorption, molecular separations, catalysis, optoelectronics, sensing, and as molecular machines. In this review, the key areas where computational prediction can assist in the discovery of supramolecular materials, including in structure prediction, property prediction, and the prediction of how to synthesize a hypothetical material are discussed, before exploring the potential impact of artificial intelligence techniques on the field. Throughout, the importance of close integration with experimental materials discovery programs will be highlighted. A series of case studies from the author's work across some different supramolecular material classes will be discussed, before finishing with a discussion of the outlook for the field.

Combining machine learning and quantum chemical calculations for high-throughput virtual screening of thermally activated delayed fluorescence molecular materials: the impact of selection strategy and structural mutations

In view of the theoretical importance and huge application potential of Thermally Activated Delayed Fluorescence (TADF) materials, it is of great significance to conduct High-Throughput Virtual Screening (HTVS) on compound libraries to find TADF candidate molecules. This research focuses on the computational design of pure organic TADF molecules. By combining machine learning and quantum chemical calculations, using cheminformatics tools, and introducing the concept of selection and mutation from evolutionary theory, we have designed a computational program for HTVS of TADF molecular materials, especially the impact of selection strategy and structural mutations on the results of HTVS was explored. An initial compound library (size = 103) constructed by enumeration of typical donors and acceptors was used to evolve by successively applying selection and 10 different structural mutations. And a group fingerprint similarity (ΔMSPR) index was proposed to account for the similarity between two compound libraries with comparable sizes. Based on the computed data, we have found that the mix of selection and mutations into the evolution map does have great impact on the HTVS results: (a) except the fast mutation Sub2, all the rest of the mutations can effectively concentrate 'good' molecules in a compound library, and hence give large material abundance (typically >0.8) for high mutation generations (n g ≥ 6). (b) The mean energy gap can exhibit a fast convergent trend toward very low values, hence the studied mutations (except Sub2) can cooperate very well with the studied DA substrates to generate optimal molecules, and the group fingerprint similarity can retain high enough values for large n g, which can be associated with the apparent convergence in molecular skeletons as n g increases. (c) The distribution of skeleton frequencies for a specific mutation is generally uneven with one dominant skeleton. The overall numbers of common and generic cores for all mutations are 11 and 7 as n g = 9. Hence, in a sense, the 'optimal' skeletons seem unique and useful in realizing low energy gaps. With these observations and the development of related HTVS software, we expect to provide insight and tools to the research community of HTVS of molecular (TADF) materials.

Graph-based Automated Macro-Molecule Assembly

We present a general molecular framework assembly algorithm that takes a largely arbitrary molecular fragment database and a user-supplied target template graph as input. Automatic assembly of molecular fragments from the database, following a prescribed, user-supplied set of connection rules, then turns the template graph into an actual, chemically reasonable molecular framework. Assembly capabilities of our algorithm are tested by producing several abstract, closed-loop shapes. To indicate a few of many possible application areas we demonstrate a host-guest complex and a road toward catalysis. Postassembly substituent exchange can be used to produce electric fields of desired values at desired points inside the framework or at its surface as a stepping stone toward rationally designed, artificial heterogeneous catalysts.

Computational Modeling of Supramolecular Metallo-organic Cages-Challenges and Opportunities

Self-assembled metallo-organic cages have emerged as promising biomimetic platforms that can encapsulate whole substrates akin to an enzyme active site. Extensive experimental work has enabled access to a variety of structures, with a few notable examples showing catalytic behavior. However, computational investigations of metallo-organic cages are scarce, not least due to the challenges associated with their modeling and the lack of accurate and efficient protocols to evaluate these systems. In this review, we discuss key molecular principles governing the design of functional metallo-organic cages, from the assembly of building blocks through binding and catalysis. For each of these processes, computational protocols will be reviewed, considering their inherent strengths and weaknesses. We will demonstrate that while each approach may have its own specific pitfalls, they can be a powerful tool for rationalizing experimental observables and to guide synthetic efforts. To illustrate this point, we present several examples where modeling has helped to elucidate fundamental principles behind molecular recognition and reactivity. We highlight the importance of combining computational and experimental efforts to speed up supramolecular catalyst design while reducing time and resources.

Porous Liquids: Computational Design for Targeted Gas Adsorption

In this Perspective, we present the unique gas adsorption capabilities of porous liquids (PLs) and the value of complex computational methods in the design of PL compositions. Traditionally, liquids only contain transient pore space between molecules that limit long-term gas capture. However, PLs are stable fluids that that contain permanent porosity due to the combination of a rigid porous host structure and a solvent. PLs exhibit remarkable adsorption and separation properties, including increased solubility and selectivity. The unique gas adsorption properties of PLs are based on their structure, which exhibits multiple gas binding sites in the pore and on the cage surface, varying binding mechanisms including hydrogen-bonding and π-π interactions, and selective diffusion in the solvent. Tunable PL compositions will require fundamental investigations of competitive gas binding mechanisms, thermal effects on binding site stability, and the role of nanoconfinement on gas and solvent diffusion that can be accelerated through molecular modeling. With these new insights PLs promise to be an exceptional material class with tunable properties for targeted gas adsorption.

RSC CICAG Open Chemical Science meeting: integrating chemical data from two symposia and a series of workshops

In November 2020 the Royal Society of Chemistry Chemical Information and Computer Applications interest group (RSC CICAG) ran a five-day meeting entitled Open Chemical Science (https://www.rsc.org/events/detail/42090/open-chemical-science). This event had three intertwined themes, Open Data, Open Access publishing and a series of workshops highlighting a variety of Open-Source tools for chemistry. The online event proved to be enormously popular, with attendees from 45 different countries. The challenges involved in converting what was planned as a three-day physical event into a five day virtual event with three intertwined strands was recognised by the RSC with the award of the “2021 Inspirational Committee Award” (https://www.rsc.org/prizes-funding/prizes/2021-winners/rsc-chemical-information-and-computer-applications-group/). The workshops in particular proved to be enormously popular and spawned a year long series of further workshops.

Polymer Structure Predictor (PSP): A Python Toolkit for Predicting Atomic-Level Structural Models for a Range of Polymer Geometries

Three-dimensional atomic-level models of polymers are the starting points for physics-based simulation studies. A capability to generate reasonable initial structural models is highly desired for this purpose. We have developed a python toolkit, namely, polymer structure predictor (psp), to generate a hierarchy of polymer models, ranging from oligomers to infinite chains to crystals to amorphous models, using a simplified molecular-input line-entry system (SMILES) string of the polymer repeat unit as the primary input. This toolkit allows users to tune several parameters to manage the quality and scale of models and computational cost. The output structures and accompanying force field (GAFF2/OPLS-AA) parameter files can be used for downstream ab initio and molecular dynamics simulations. The psp package includes a Colab notebook where users can go through several examples, building their own models, visualizing them, and downloading them for later use. The psp toolkit, being a first of its kind, will facilitate automation in polymer property prediction and design.

CellSys: An open-source tool for building initial structures for bio-membranes and drug-delivery systems

Since phospholipids are the most important components in the structure of biomembranes, they deserve to be considered with a lot of attention in both experimental and computational theoretical studies using molecular simulation methods related to the research in the fields of drug design and drug delivery where they involve knowledge about the interactions of drug molecules with cell membranes. To employ the molecular simulation approach for this purpose the essential requirement is having information about the initial structure of phospholipids and how they interact with the drugs. Therefore in this article, we introduce an open-source software package in Python programming language for utilizing data manipulation for generation and developing the initial structure of biomolecular cells to provide the needed information for investigation in drug delivery systems. In addition, the proposed software package can be used for the efficient storage of membrane structural data to be exploited in designing new drug delivery systems. To verify the performance of the code and the results of the simulations, several analyses have been done, such as the calculation of area per lipid and self-diffusion coefficient, in addition to lipid order parameter. The results were in complete agreement with the references.

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.

High-Throughput Computational Evaluation of Low Symmetry Pd2 L4 Cages to Aid in System Design*

Unsymmetrical ditopic ligands can self-assemble into reduced-symmetry Pd2 L4 metallo-cages with anisotropic cavities, with implications for high specificity and affinity guest-binding. Mixtures of cage isomers can form, however, resulting in undesirable system heterogeneity. It is paramount to be able to design components that preferentially form a single isomer. Previous data suggested that computational methods could predict with reasonable accuracy whether unsymmetrical ligands would preferentially self-assemble into single cage isomers under constraints of geometrical mismatch. We successfully apply a collaborative computational and experimental workflow to mitigate costly trial-and-error synthetic approaches. Our rapid computational workflow constructs unsymmetrical ligands and their Pd2 L4 cage isomers, ranking the likelihood for exclusively forming cis-Pd2 L4 assemblies. From this narrowed search space, we successfully synthesised four new, low-symmetry, cis-Pd2 L4 cages.

Machine learning and big data provide crucial insight for future biomaterials discovery and research

Machine learning have been widely adopted in a variety of fields including engineering, science, and medicine revolutionizing how data is collected, used, and stored. Their implementation has led to a drastic increase in the number of computational models for the prediction of various numerical, categorical, or association events given input variables. We aim to examine recent advances in the use of machine learning when applied to the biomaterial field. Specifically, quantitative structure properties relationships offer the unique ability to correlate microscale molecular descriptors to larger macroscale material properties. These new models can be broken down further into four categories: regression, classification, association, and clustering. We examine recent approaches and new uses of machine learning in the three major categories of biomaterials: metals, polymers, and ceramics for rapid property prediction and trend identification. While current research is promising, limitations in the form of lack of standardized reporting and available databases complicates the implementation of described models. Herein, we hope to provide a snapshot of the current state of the field and a beginner's guide to navigating the intersection of biomaterials research and machine learning. STATEMENT OF SIGNIFICANCE: Machine learning and its methods have found a variety of uses beyond the field of computer science but have largely been neglected by those in realm of biomaterials. Through the use of more computational methods, biomaterials development can be expediated while reducing the need for standard trial and error methods. Within, we introduce four basic models that readers can potentially apply to their current research as well as current applications within the field. Furthermore, we hope that this article may act as a "call to action" for readers to realize and address the current lack of implementation within the biomaterials field.

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.

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.

Forecasting System of Computational Time of DFT/TDDFT Calculations under the Multiverse Ansatz via Machine Learning and Cheminformatics

With the view of achieving a better performance in task assignment and load-balancing, a top-level designed forecasting system for predicting computational times of density-functional theory (DFT)/time-dependent DFT (TDDFT) calculations is presented. The computational time is assumed as the intrinsic property for the molecule. Based on this assumption, the forecasting system is established using the "reinforced concrete", which combines the cheminformatics, several machine-learning (ML) models, and the framework of many-world interpretation (MWI) in multiverse ansatz. Herein, the cheminformatics is used to recognize the topological structure of molecules, the ML models are used to build the relationships between topology and computational cost, and the MWI framework is used to hold various combinations of DFT functionals and basis sets in DFT/TDDFT calculations. Calculated results of molecules from the DrugBank dataset show that (1) it can give quantitative predictions of computational costs, typical mean relative errors can be less than 0.2 for DFT/TDDFT calculations with derivations of ±25% using the exactly pretrained ML models and (2) it can also be employed to various combinations of DFT functional and basis set cases without exactly pretrained ML models, while only slightly enlarge predicting errors.

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.

Metal-Organic Frameworks and Metal-Organic Cages - A Perspective

The fields of metal-organic cages (MOCs) and metal-organic frameworks (MOFs) are both highly topical and continue to develop at a rapid pace. Despite clear synergies between the two fields, overlap is rarely observed. This article discusses the peculiarities and similarities of MOCs and MOFs in terms of synthetic strategies and approaches to system characterisation. The stability of both classes of material is compared, particularly in relation to their applications in guest storage and catalysis. Lastly, suggestions are made for opportunities for each field to learn and develop in partnership with the other.

Computational discovery of molecular C60 encapsulants with an evolutionary algorithm

Computation is playing an increasing role in the discovery of materials, including supramolecular materials such as encapsulants. In this work, a function-led computational discovery using an evolutionary algorithm is used to find potential fullerene (C₆₀) encapsulants within the chemical space of porous organic cages. We find that the promising host cages for C₆₀ evolve over the simulations towards systems that share features such as the correct cavity size to host C₆₀, planar tri-topic aldehyde building blocks with a small number of rotational bonds, di-topic amine linkers with functionality on adjacent carbon atoms, high structural symmetry, and strong complex binding affinity towards C₆₀. The proposed cages are chemically feasible and similar to cages already present in the literature, helping to increase the likelihood of the future synthetic realisation of these predictions. The presented approach is generalisable and can be tailored to target a wide range of properties in molecular material systems.

Toward crystalline porosity estimators for porous molecules

Our data-mining of crystalline molecular materials reveals the correlations between the molecular and crystalline porosity.

Extension and evaluation of the D4 London-dispersion model for periodic systems

We present an extension of the DFT-D4 model [J. Chem. Phys., 2019, 150, 154122] for periodic systems. The main new ingredients are additional reference polarizabilities for highly-coordinated group 1-5 elements derived from pseudo-periodic electrostatically-embedded cluster calculations. To illustrate the performance of the updated method, several test cases are considered, for which we compare D4 to its predecessor D3(BJ), as well as to a comprehensive set of other dispersion-corrected methods. The largest improvements are observed for solid-state polarizabilities of 16 inorganic salts, where the D4 model achieves an unprecedented accuracy, surpassing its predecessor as well as other, computationally much more demanding approaches. For cell volumes and lattice energies of two sets of chemically diverse molecular crystals, the accuracy gain is less pronounced compared to the already excellently performing D3(BJ) method. For the challenging adsorption energies of small organic molecules on metallic as well as on ionic surfaces, DFT-D4 provides values in good agreement with experimental and/or high-level references. These results suggest the application of the proposed D4 model as a physically improved yet computationally efficient dispersion correction for standard DFT calculations as well as low-cost approaches like semi-empirical or even force-field models.

Structurally Diverse Covalent Triazine-Based Framework Materials for Photocatalytic Hydrogen Evolution from Water

A structurally diverse family of 39 covalent triazine-based framework materials (CTFs) are synthesized by Suzuki-Miyaura polycondensation and tested as hydrogen evolution photocatalysts using a high-throughput workflow. The two best-performing CTFs are based on benzonitrile and dibenzo[b,d]thiophene sulfone linkers, respectively, with catalytic activities that are among the highest for this material class. The activities of the different CTFs are rationalized in terms of four variables: the predicted electron affinity, the predicted ionization potential, the optical gap, and the dispersibility of the CTFs particles in solution, as measured by optical transmittance. The electron affinity and dispersibility in solution are found to be the best predictors of photocatalytic hydrogen evolution activity.

Accelerated Discovery of Organic Polymer Photocatalysts for Hydrogen Evolution from Water through the Integration of Experiment and Theory

Conjugated polymers are an emerging class of photocatalysts for hydrogen production where the large breadth of potential synthetic diversity presents both an opportunity and a challenge. Here, we integrate robotic experimentation with high-throughput computation to navigate the available structure-property space. A total of 6354 co-polymers was considered computationally, followed by the synthesis and photocatalytic characterization of a sub-library of more than 170 co-polymers. This led to the discovery of new polymers with sacrificial hydrogen evolution rates (HERs) of more than 6 mmol g^-1 h^-1. The variation in HER across the library does not correlate strongly with any single physical property, but a machine-learning model involving four separate properties can successfully describe up to 68% of the variation in the HER data between the different polymers. The four variables used in the model were the predicted electron affinity, the predicted ionization potential, the optical gap, and the dispersibility of the polymer particles in solution, as measured by optical transmittance.

Mapping binary copolymer property space with neural networks

The extremely large number of unique polymer compositions that can be achieved through copolymerisation makes it an attractive strategy for tuning their optoelectronic properties. However, this same attribute also makes it challenging to explore the resulting property space and understand the range of properties that can be realised. In an effort to enable the rapid exploration of this space in the case of binary copolymers, we train a neural network using a tiered data generation strategy to accurately predict the optical and electronic properties of 350 000 binary copolymers that are, in principle, synthesizable from their dihalogen monomers via Yamamoto, or Suzuki-Miyaura and Stille coupling after one-step functionalisation. By extracting general features of this property space that would otherwise be obscured in smaller datasets, we identify simple models that effectively relate the properties of these copolymers to the homopolymers of their constituent monomers, and challenge common ideas behind copolymer design. We find that binary copolymerisation does not appear to allow access to regions of the optoelectronic property space that are not already sampled by the homopolymers, although it conceptually allows for more fine-grained property control. Using the large volume of data available, we test the hypothesis that copolymerisation of 'donor' and 'acceptor' monomers can result in copolymers with a lower optical gap than their related homopolymers. Overall, despite the prevalence of this concept in the literature, we observe that this phenomenon is relatively rare, and propose conditions that greatly enhance the likelihood of its experimental realisation. Finally, through a 'topographical' analysis of the co-polymer property space, we show how this large volume of data can be used to identify dominant monomers in specific regions of property space that may be amenable to a variety of applications, such as organic photovoltaics, light emitting diodes, and thermoelectrics.

Computational high-throughput screening of polymeric photocatalysts: exploring the effect of composition, sequence isomerism and conformational degrees of freedom

We discuss a low-cost computational workflow for the high throughput screening of polymeric photocatalysts.

Computationally-inspired discovery of an unsymmetrical porous organic cage

A completely unsymmetrical porous organic cage was synthesised from a C_2v symmetrical building block that was identified by a computational screen. The cage was formed through a 12-fold imine condensation of a tritopic C_2v symmetric trialdehyde with a ditopic C₂ symmetric diamine in a [4 + 6] reaction. The cage was rigid and microporous, as predicted by the simulations, with an apparent Brunauer-Emmett-Teller surface area of 578 m² g^-1. The reduced symmetry of the tritopic building block relative to its topicity meant there were 36 possible structural isomers of the cage. Experimental characterisation suggests a single isomer with 12 unique imine environments, but techniques such as NMR could not conclusively identify the isomer. Computational structural and electronic analysis of the possible isomers was used to identify the most likely candidates, and hence to construct a 3-dimensional model of the amorphous solid. The rational design of unsymmetrical cages using building blocks with reduced symmetry offers new possibilities in controlling the degree of crystallinity, porosity, and solubility, of self-assembled materials.

An evolutionary algorithm for the discovery of porous organic cages

The chemical and structural space of possible molecular materials is enormous, as they can, in principle, be built from any combination of organic building blocks. Here we have developed an evolutionary algorithm (EA) that can assist in the efficient exploration of chemical space for molecular materials, helping to guide synthesis to materials with promising applications. We demonstrate the utility of our EA to porous organic cages, predicting both promising targets and identifying the chemical features that emerge as important for a cage to be shape persistent or to adopt a particular cavity size. We identify that shape persistent cages require a low percentage of rotatable bonds in their precursors (<20%) and that the higher topicity building block in particular should use double bonds for rigidity. We can use the EA to explore what size ranges for precursors are required for achieving a given pore size in a cage and show that 16 Å pores, which are absent in the literature, should be synthetically achievable. Our EA implementation is adaptable and easily extendable, not only to target specific properties of porous organic cages, such as optimal encapsulants or molecular separation materials, but also to any easily calculable property of other molecular materials.

High-Throughput Screening Approach for the Optoelectronic Properties of Conjugated Polymers

We propose a general high-throughput virtual screening approach for the optical and electronic properties of conjugated polymers. This approach makes use of the recently developed xTB family of low-computational-cost density functional tight-binding methods from Grimme and co-workers, calibrated here to (Time-Dependent) Density Functional Theory ((TD)DFT) data computed for a representative diverse set of (co)polymers. Parameters drawn from the resulting calibration using a linear model can then be applied to the xTB derived results for new polymers, thus generating near DFT-quality data with orders of magnitude reduction in computational cost. As a result, after an initial computational investment for calibration, this approach can be used to quickly and accurately screen on the order of thousands of polymers for target applications. We also demonstrate that the (opto)electronic properties of the conjugated polymers show only a very minor variation when considering different conformers and that the results of high-throughput screening are therefore expected to be relatively insensitive with respect to the conformer search methodology applied.