Integrating Computational and Experimental Workflows for Accelerated Organic Materials Discovery

Web-BO: towards increased accessibility of Bayesian optimisation (BO) for chemistry

Historically, the chemical discovery process has predominantly been a matter of trial-and-improvement, where small modifications are made to a chemical system, guided by chemical knowledge, with the aim of optimising towards a target property or combination of properties. While a trial-and-improvement approach is frequently successful, especially when assisted by the help of serendipity, the approach is incredibly time- and resource-intensive. Complicating this further, the available chemical space that could, in theory, be explored is remarkably vast. As we are faced with near infinite possibilities and limited resources, we require improved search methods to effectively move towards desired optima, e.g. chemical systems exhibiting a target property, or several desired properties. Bayesian optimisation (BO) has recently gained significant traction in chemistry, where within the BO framework, prior knowledge is used to inform and guide the search process to optimise towards desired chemical targets, e.g. optimal reaction conditions to maximise yield, or optimal catalyst exhibiting improved catalytic activity. While powerful, implementing BO algorithms in practice is largely limited to interfacing via various APIs - requiring advanced coding experience and bespoke scripts for each optimisation task. Further, it is challenging to seamlessly link these with electronic lab notebooks via a graphical user interface (GUI). Ultimately, this limits the accessibility of BO algorithms. Here, we present Web-BO, a GUI to support BO for chemical optimisation tasks. We demonstrate its performance using an open source dataset and associated emulator, and link the platform with an existing electronic lab notebook, datalab. By providing a GUI-based BO service, we hope to improve the accessibility of data-driven optimisation tools in chemistry; https://suprashare.rcs.ic.ac.uk/web-bo/.

Self-Driving Laboratories for Chemistry and Materials Science

Self-driving laboratories (SDLs) promise an accelerated application of the scientific method. Through the automation of experimental workflows, along with autonomous experimental planning, SDLs hold the potential to greatly accelerate research in chemistry and materials discovery. This review provides an in-depth analysis of the state-of-the-art in SDL technology, its applications across various scientific disciplines, and the potential implications for research and industry. This review additionally provides an overview of the enabling technologies for SDLs, including their hardware, software, and integration with laboratory infrastructure. Most importantly, this review explores the diverse range of scientific domains where SDLs have made significant contributions, from drug discovery and materials science to genomics and chemistry. We provide a comprehensive review of existing real-world examples of SDLs, their different levels of automation, and the challenges and limitations associated with each domain.

Design and Synthesis of Red-Absorbing Fluoran Leuco Dyes Supported by Computational Screening

We report here on the design and synthesis of red-absorbing fluoran leuco dyes (LD). An essential part of the present dye development process is a computational screening of the candidate molecules, which allows for both time-efficient and accurate in silico characterization of the dyes. We focus our study here on the robust benzo[a]fluoran scaffold frequently used in leuco dyes. For the computational screening of LD candidates, an automated DFT-based simulation protocol has been developed and applied. The protocol consists of a combinatorial generation of the molecular structures of possible LD candidates, followed by simulations of their optimized molecular geometries, with their UV-Vis spectra as the main figure of merit. In the present application of the simulation protocol, more than 1600 structures of possible LD candidates have been evaluated. Finally, two structures, LD01 and LD02, have been chosen from the list of the best computed LD candidates to be synthesized and characterized. Our study demonstrates how the synergy between experiment and simulation can facilitate the design of novel leuco dyes.

Dynamic and solid-state behaviour of bromoisotrianglimine

Solid-state materials formed from discrete imine macrocycles have potential in industrial separations, but dynamic behaviour during both synthesis and crystallisation makes them challenging to exploit. Here, we explore opportunities for structural control by investigating the dynamic nature of a C-5 brominated isotrianglimine in solution and under crystallisation conditions. In solution, the equilibrium between the [3 + 3] and the less reported [2 + 2] macrocycle was investigated, and both macrocycles were fully characterised. Solvent templating during crystallisation was used to form new packing motifs for the [3 + 3] macrocycle and a previously unreported [4 + 4] macrocycle. Finally, chiral self-sorting was used to demonstrate how crystallisation conditions can not only influence packing arrangements but also shift the macrocycle equilibrium to yield new structures. This work thus exemplifies three strategies for exploiting dynamic behaviour to form isotrianglimine materials, and highlights the importance of understanding the dynamic behaviour of a system when designing and crystallising functional materials formed using dynamic covalent chemistry.

Mesophase Behavior of Molecules Containing Three Benzene Rings Connected via Imines and Ester Linkages

Ten new compounds based on the methineazo-azomethine (CH=N-N=CH) and ester linking groups were prepared and investigated for their mesophase behavior and optical stability, and liquid crystals of 4-substituted phenyl methineazo-azomethine phenyl 4-alkoxybenzoates, I n a-e , were investigated. An alkoxy group with a length between 8 and 12 carbons is attached to the phenyl eater wing, while the other terminal ring is substituted in its 4-position with one of the polar NO2, F, Cl, CH3O, and N(CH3)2 groups. The molecular structures of the newly prepared compounds were verified by using 1H NMR, 13C NMR, and elemental analysis. Differential scanning calorimetry and polarized optical microscopy were applied to investigate their mesophase behavior. All members of the prepared homologous series showed excellent thermal mesomorphic stability over wide temperature ranges. The geometrical and thermal properties of the investigated compounds were verified via density functional theory (DFT). The theoretical results revealed that all of the compounds are almost planar. Finally, the experimentally established values of the mesophase data were correlated with the predicted quantum chemical characteristics evaluated by DFT.

Materials discovery with extreme properties via reinforcement learning-guided combinatorial chemistry

The goal of most materials discovery is to discover materials that are superior to those currently known. Fundamentally, this is close to extrapolation, which is a weak point for most machine learning models that learn the probability distribution of data. Herein, we develop reinforcement learning-guided combinatorial chemistry, which is a rule-based molecular designer driven by trained policy for selecting subsequent molecular fragments to get a target molecule. Since our model has the potential to generate all possible molecular structures that can be obtained from combinations of molecular fragments, unknown molecules with superior properties can be discovered. We theoretically and empirically demonstrate that our model is more suitable for discovering better compounds than probability distribution-learning models. In an experiment aimed at discovering molecules that hit seven extreme target properties, our model discovered 1315 of all target-hitting molecules and 7629 of five target-hitting molecules out of 100 000 trials, whereas the probability distribution-learning models failed. Moreover, it has been confirmed that every molecule generated under the binding rules of molecular fragments is 100% chemically valid. To illustrate the performance in actual problems, we also demonstrate that our models work well on two practical applications: discovering protein docking molecules and HIV inhibitors.

Argentophilic Interactions, Flexibility, and Dynamics of Pyrrole Cages Encapsulating Silver(I) Clusters

Recently, pyrrole cages have been synthesized that encapsulate ion pairs and silver(I) clusters to form intricate supramolecular capsules. We report here a computational analysis of these structures using density functional theory combined with a semiempirical tight-binding approach. We find that for neutral pyrrole cages, the Gibbs free energies of formation provide reliable predictions for the ratio of bound ions. For charged pyrrole cages, we find strong argentophilic interactions between Ag ions on the basis of the calculated bond indices and molecular orbitals. For the cage with the Ag4 cluster, we find two minimum-geometry conformations that differ by only 6.5 kcal/mol, with an energy barrier <1 kcal/mol, suggesting a very flexible structure as indicated by molecular dynamics. The predicted energies of formation of [Agn⊂1]n-3+ (n = 1-5) cryptands provide low energy barriers of formation of 5-20 kcal/mol for all cases, which is consistent with the experimental data. Furthermore, we also examined the structural variability of mixed-valence silver clusters to test whether additional geometrical conformations inside the organic cage are thermodynamically accessible. In this context, we show that the time-dependent density functional theory UV-vis spectra may potentially serve as a diagnostic probe to characterize mixed-valence and geometrical configurations of silver clusters encapsulated into cryptands.

Substituted tetrazoles with N-oxide moiety: critical assessment of thermochemical properties

Modeling of the structure of molecules and simulation of crystal structure followed by the calculation of the enthalpies of formation for 21 salts of three high-energy tetrazole 1N-oxides: 5-nitro-1-hydroxy-1H-tetrazole 1a-1g, 5-trinitromethyl-1-hydroxy-1H-tetrazole 2a-2g and 6-amino-3-(1-hydroxy-1H-tetrazol-5-yl)-1,2,4,5-tetrazine 1,5-dioxide 3a-3g was performed. The methods of quantum chemistry and the method of atom-atom potentials were used. Structural search for optimal crystal packings was carried out in 11 most common space symmetry groups. The enthalpies of formation were obtained and analyzed using two different approaches: VBT and MICCM methods, which allowed to evaluate the quality of these calculation methods. In addition, the results obtained indicate high values of thermochemical characteristics for some of the considered compounds, which have a positive effect on their explosive properties and unveil their future application potential.

Systematic exploration of accessible topologies of cage molecules via minimalistic models

Cages are macrocyclic structures with an intrinsic internal cavity that support applications in separations, sensing and catalysis. These materials can be synthesised via self-assembly of organic or metal-organic building blocks. Their bottom-up synthesis and the diversity in building block chemistry allows for fine-tuning of their shape and properties towards a target property. However, it is not straightforward to predict the outcome of self-assembly, and, thus, the structures that are practically accessible during synthesis. Indeed, such a prediction becomes more difficult as problems related to the flexibility of the building blocks or increased combinatorics lead to a higher level of complexity and increased computational costs. Molecular models, and their coarse-graining into simplified representations, may be very useful to this end. Here, we develop a minimalistic toy model of cage-like molecules to explore the stable space of different cage topologies based on a few fundamental geometric building block parameters. Our results capture, despite the simplifications of the model, known geometrical design rules in synthetic cage molecules and uncover the role of building block coordination number and flexibility on the stability of cage topologies. This leads to a large-scale and systematic exploration of design principles, generating data that we expect could be analysed through expandable approaches towards the rational design of self-assembled porous architectures.

Machine-Learning-Enabled Ligand Screening for Cs/Sr Crystallizing Separation

The reprocessing of spent nuclear fuel is critical for the sustainability of the nuclear energy industry. However, several key separation processes present challenges in this regard, calling for continuous research into next-generation separation materials. Herein, we propose a high-throughput screening framework to improve efficiency in identifying potential ligands that selectively coordinate metal cations of interest in liquid wastes that considers multiple key chemical characteristics, including aqueous solubility, pKa, and coordination bond length. Machine-learning models were designed for the fast and accurate prediction of these characteristics by using graph convolution and transfer-learning techniques. Suitable ligands for Cs/Sr crystallizing separation were identified through the "computational funnel", and several top-ranking, nontoxic, low-cost ligands were selected for experimental verification.

Designing hexaphyrins for high-potential NLO switches: the synergy of core-modifications and meso-substitutions

Due to the enormous size of the chemical compound space, usually only small regions are traversed with traditional direct molecular design approaches making the discovery for novel functionalized molecules for nonlinear optical applications challenging. By applying inverse molecular design algorithms, we aim to efficiently explore larger regions of the compound space in search of promising hexaphyrin-based molecular switches as measured by their first-hyperpolarizability (β_HRS) contrast. We focus on the 28R → 30R switch with a functionalization pattern allowing for centrosymmetric OFF states yielding zero β_HRS response. This switch is particularly challenging as full meso-substitution with a single type of functional group or core-modifications result in almost no contrast enhancement. We carried out four inverse design procedures during which two sets of core-modifications and three sets of meso-substitutions sites were systematically optimized. All 4 optimal switches are characterized by a mix of meso-substitutions and core-modifications, of which the best performing switch yields a 10-fold improvement over the parent macrocycle. Throughout the inverse design procedures, we collected and analyzed a database biased towards high NLO contrasts that contains 277 different patterns for hexaphyrin-based switches. We derived three design rules to obtain highly functional 28R → 30R NLO switches: (I) a combination of 2 strong EWG and 1 EDG group is the ideal recipe for increasing the NLO contrast, though their position also plays an important role. (II) The type of core-modification is less important when only the diagonal positions are core-modified. Switches with 4 core-modifications show a clear preference for oxygen. (III) Keeping centrosymmetry in the OFF state remains highly beneficial given the investigated functionalization pattern. Finally, we have demonstrated that combining meso-substitutions with core-modifications can synergistically improve the NLO contrast.

Unveiling Hidden Zeolitic Imidazolate Frameworks Guided by Intuition-Based Geometrical Factors

Herein, synthesizable candidate topologies to form zeolitic imidazolate frameworks (ZIFs) are efficiently identified from over 2 000 000 hypothetical structures in zeolite databases, using structural descriptors extracted from known ZIFs. A combination of intuition-based structural descriptors, such as ring patterns, node numbers, and TOT bridging angles (T = tetrahedral metal nodes in zeolites and ZIFs), is used as data filters to eliminate topologies infeasible for ZIF formation. Carefully chosen structural descriptors facilitate the prediction of plausible ZIF topologies. To investigate potential applications as porous ZIFs, this work performs hydrogen adsorption screening and suggested notable target ZIFs. The collection of new plausible ZIFs, derived from the combined descriptors, will be a structural blueprint for synthetic chemists.

Construction of Covalent Organic Frameworks via Multicomponent Reactions

Multicomponent reactions (MCRs) combine at least three reactants to afford the desired product in a highly atom-economic way and are therefore viewed as efficient one-pot combinatorial synthesis tools allowing one to significantly boost molecular complexity and diversity. Nowadays, MCRs are no longer confined to organic synthesis and have found applications in materials chemistry. In particular, MCRs can be used to prepare covalent organic frameworks (COFs), which are crystalline porous materials assembled from organic monomers and exhibit a broad range of properties and applications. This synthetic approach retains the advantages of small-molecule MCRs, not only strengthening the skeletal robustness of COFs, but also providing additional driving forces for their crystallization, and has been used to prepare a series of robust COFs with diverse applications. The present perspective article provides the general background for MCRs, discusses the types of MCRs employed for COF synthesis to date, and addresses the related critical challenges and future perspectives to inspire the MCR-based design of new robust COFs and promote further progress in this emerging field.

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.

Coarse-grained modelling to predict the packing of porous organic cages

How molecules pack has vital ramifications for their applications as functional molecular materials. Small changes in a molecule's functionality can lead to large, non-intuitive, changes in their global solid-state packing, resulting in difficulty in targeted design. Predicting the crystal structure of organic molecules from only their molecular structure is a well-known problem plaguing crystal engineering. Although relevant to the properties of many organic molecules, the packing behaviour of modular porous materials, such as porous organic cages (POCs), greatly impacts the properties of the material. We present a novel way of predicting the solid-state phase behaviour of POCs by using a simplistic model containing the dominant degrees of freedom driving crystalline phase formation. We employ coarse-grained simulations to systematically study how chemical functionality of pseudo-octahedral cages can be used to manipulate the solid-state phase formation of POCs. Our results support those of experimentally reported structures, showing that for cages which pack via their windows forming a porous network, only one phase is formed, whereas when cages pack via their windows and arenes, the phase behaviour is more complex. While presenting a lower computational cost route for predicting molecular crystal packing, coarse-grained models also allow for the development of design rules which we start to formulate through our results.

Into the Unknown: How Computation Can Help Explore Uncharted Material Space

Novel functional materials are urgently needed to help combat the major global challenges facing humanity, such as climate change and resource scarcity. Yet, the traditional experimental materials discovery process is slow and the material space at our disposal is too vast to effectively explore using intuition-guided experimentation alone. Most experimental materials discovery programs necessarily focus on exploring the local space of known materials, so we are not fully exploiting the enormous potential material space, where more novel materials with unique properties may exist. Computation, facilitated by improvements in open-source software and databases, as well as computer hardware has the potential to significantly accelerate the rational development of materials, but all too often is only used to postrationalize experimental observations. Thus, the true predictive power of computation, where theory leads experimentation, is not fully utilized. Here, we discuss the challenges to successful implementation of computation-driven materials discovery workflows, and then focus on the progress of the field, with a particular emphasis on the challenges to reaching novel materials.

Purely Covalent Molecular Cages and Containers for Guest Encapsulation

Cage compounds offer unique binding pockets similar to enzyme-binding sites, which can be customized in terms of size, shape, and functional groups to point toward the cavity and many other parameters. Different synthetic strategies have been developed to create a toolkit of methods that allow preparing tailor-made organic cages for a number of distinct applications, such as gas separation, molecular recognition, molecular encapsulation, hosts for catalysis, etc. These examples show the versatility and high selectivity that can be achieved using cages, which is impossible by employing other molecular systems. This review explores the progress made in the field of fully organic molecular cages and containers by focusing on the properties of the cavity and their application to encapsulate guests.

Unlocking the computational design of metal-organic cages

Metal-organic cages are macrocyclic structures that can possess an intrinsic void that can hold molecules for encapsulation, adsorption, sensing, and catalysis applications. As metal-organic cages may be comprised from nearly any combination of organic and metal-containing components, cages can form with diverse shapes and sizes, allowing for tuning toward targeted properties. Therefore, their near-infinite design space is almost impossible to explore through experimentation alone and computational design can play a crucial role in exploring new systems. Although high-throughput computational design and screening workflows have long been known as powerful tools in drug and materials discovery, their application in exploring metal-organic cages is more recent. We show examples of structure prediction and host-guest/catalytic property evaluation of metal-organic cages. These examples are facilitated by advances in methods that handle metal-containing systems with improved accuracy and are the beginning of the development of automated cage design workflows. We finally outline a scope for how high-throughput computational methods can assist and drive experimental decisions as the field pushes toward functional and complex metal-organic cages. In particular, we highlight the importance of considering realistic, flexible systems.

Material Evolution with Nanotechnology, Nanoarchitectonics, and Materials Informatics: What will be the Next Paradigm Shift in Nanoporous Materials?

Materials science and chemistry have played a central and significant role in advancing society. With the shift toward sustainable living, it is anticipated that the development of functional materials will continue to be vital for sustaining life on our planet. In the recent decades, rapid progress has been made in materials science and chemistry owing to the advances in experimental, analytical, and computational methods, thereby producing several novel and useful materials. However, most problems in material development are highly complex. Here, the best strategy for the development of functional materials via the implementation of three key concepts is discussed: nanotechnology as a game changer, nanoarchitectonics as an integrator, and materials informatics as a super-accelerator. Discussions from conceptual viewpoints and example recent developments, chiefly focused on nanoporous materials, are presented. It is anticipated that coupling these three strategies together will open advanced routes for the swift design and exploratory search of functional materials truly useful for solving real-world problems. These novel strategies will result in the evolution of nanoporous functional materials.

Artificial intelligence: A powerful paradigm for scientific research

Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences.

Computational and data driven molecular material design assisted by low scaling quantum mechanics calculations and machine learning

Electronic structure methods based on quantum mechanics (QM) are widely employed in the computational predictions of the molecular properties and optoelectronic properties of molecular materials. The computational costs of these QM methods, ranging from density functional theory (DFT) or time-dependent DFT (TDDFT) to wave-function theory (WFT), usually increase sharply with the system size, causing the curse of dimensionality and hindering the QM calculations for large sized systems such as long polymer oligomers and complex molecular aggregates. In such cases, in recent years low scaling QM methods and machine learning (ML) techniques have been adopted to reduce the computational costs and thus assist computational and data driven molecular material design. In this review, we illustrated low scaling ground-state and excited-state QM approaches and their applications to long oligomers, self-assembled supramolecular complexes, stimuli-responsive materials, mechanically interlocked molecules, and excited state processes in molecular aggregates. Variable electrostatic parameters were also introduced in the modified force fields with the polarization model. On the basis of QM computational or experimental datasets, several ML algorithms, including explainable models, deep learning, and on-line learning methods, have been employed to predict the molecular energies, forces, electronic structure properties, and optical or electrical properties of materials. It can be conceived that low scaling algorithms with periodic boundary conditions are expected to be further applicable to functional materials, perhaps in combination with machine learning to fast predict the lattice energy, crystal structures, and spectroscopic properties of periodic functional materials.

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.