Beyond generative models: superfast traversal,optimization, novelty,exploration and discovery (STONED) algorithm for molecules using SELFIES

Inverse design allows the generation of molecules with desirable physical quantities using property optimization. Deep generative models have recently been applied to tackle inverse design, as they possess the ability to optimize molecular properties directly through structure modification using gradients. While the ability to carry out direct property optimizations is promising, the use of generative deep learning models to solve practical problems requires large amounts of data and is very time-consuming. In this work, we propose STONED – a simple and efficient algorithm to perform interpolation and exploration in the chemical space, comparable to deep generative models. STONED bypasses the need for large amounts of data and training times by using string modifications in the SELFIES molecular representation. First, we achieve non-trivial performance on typical benchmarks for generative models without any training. Additionally, we demonstrate applications in high-throughput virtual screening for the design of drugs, photovoltaics, and the construction of chemical paths, allowing for both property and structure-based interpolation in the chemical space. Overall, we anticipate our results to be a stepping stone for developing more sophisticated inverse design models and benchmarking tools, ultimately helping generative models achieve wider adoption.

Interpolation and exploration within the chemical space for inverse design.     

Relating atomic energy,radius and electronegativity through compression

Trends in atomic properties are well-established tools for guiding the analysis and discovery of materials. Here, we show how compression can reveal a long sought-after connection between two central chemical concepts – van-der-Waals (vdW) radii and electronegativity – and how these relate to the driving forces behind chemical and physical transformations.

Compression is used to derive a long sought-after connection between two central chemical concepts – van-der-Waals (vdW) radii and electronegativity – and how these relate to the driving forces behind chemical and physical transformations.     

A redox-active ionic liquid manifesting charge-transfer interaction between a viologen and carbazole and its effect on the viscosity,ionic conductivity,and redox process of the viologen

Redox-active ionic liquids (RAILs) are gaining attention as a material that can create a wide range of functions. We herein propose a charge-transfer (CT) RAIL by mixing two RAILs, specifically a carbazole-based ionic liquid ([CzC₄ImC₁][TFSI]) as a donor and a viologen-based ionic liquid ([C₄VC₇][TFSI]₂) as an acceptor. We investigated the effect of CT interaction on the physicochemical properties of the CT ionic liquid (CT-IL) using the results of temperature-dependent measurements of UV-vis absorption, viscosity, and ionic conductivity as well as cyclic voltammograms. We employed the Walden analysis and the Grunberg–Nissan model to elucidate the effect of the CT interaction on the viscosity and ionic conductivity. The CT interaction reduces the viscosity by reducing the electrostatic attraction between the dicationic viologen and TFSI anion. It also reduces the ionic conductivity by the CT association of the dicationic viologen and carbazole. The electrochemically reversible responses of the viologens in [C₄VC₇][TFSI]₂ and CT-IL are consistent with the Nernstian and the interacting two-redox site models. Notably, the transport and electrochemical properties are modulated by CT interaction, leading to unique features that are not present in individual component ILs. The inclusion of CT interaction in RAILs thus provides a powerful means to expand the scope of functionalized ionic liquids.

A redox-active ionic liquid (RAIL) consisting of a carbazole and viologen shows charge transfer (CT) interaction. The physicochemical properties are modulated by the CT interaction by comparison with the individual RAILs.     

Atomistic fibrillar architectures of polar prion-inspired heptapeptides

This article provides the computational prediction of the atomistic architectures resulting from self-assembly of the polar heptapeptide sequences NYNYNYN, SYSYSYS and GYGYGYG. Using a combination of molecular dynamics and a newly developed tool for non-covalent interaction analysis, we uncover the properties of a new class of bionanomaterials, including hydrogen-bonded polar zippers, and the relationship between peptide composition, fibril geometry and weak interaction networks. Our results, corroborated by experimental observations, provide the basis for the rational design of prion-inspired nanomaterials.

This article provides the computational prediction of the atomistic architectures resulting from self-assembly of the polar heptapeptide sequences NYNYNYN, SYSYSYS and GYGYGYG.     

Diagnostic prospects and preclinical development of optical technologies using gold nanostructure contrast agents to boost endogenous tissue contrast

Numerous developments in optical biomedical imaging research utilizing gold nanostructures as contrast agents have advanced beyond basic research towards demonstrating potential as diagnostic tools; some of which are translating into clinical applications. Recent advances in optics, lasers and detection instrumentation along with the extensive, yet developing, knowledge-base in tailoring the optical properties of gold nanostructures has significantly improved the prospect of near-infrared (NIR) optical detection technologies. Of particular interest are optical coherence tomography (OCT), photoacoustic imaging (PAI), multispectral optoacoustic tomography (MSOT), Raman spectroscopy (RS) and surface enhanced spatially offset Raman spectroscopy (SESORS), due to their respective advancements. Here we discuss recent technological developments, as well as provide a prediction of their potential to impact on clinical diagnostics. A brief summary of each techniques'' capability to distinguish abnormal (disease sites) from normal tissues, using endogenous signals alone is presented. We then elaborate on the use of exogenous gold nanostructures as contrast agents providing enhanced performance in the above-mentioned techniques. Finally, we consider the potential of these approaches to further catalyse advances in pre-clinical and clinical optical diagnostic technologies.

Optical biomedical imaging research utilising gold nanostructures as contrast agents has advanced beyond basic science, demonstrating potential in various optical diagnostic tools; some of which are currently translating into clinical applications.     

Photoresponsive molecular tools for emerging applications of light in medicine

Light-based therapeutic and imaging modalities, which emerge in clinical applications, rely on molecular tools, such as photocleavable protecting groups and photoswitches that respond to photonic stimulus and translate it into a biological effect. However, optimisation of their key parameters (activation wavelength, band separation, fatigue resistance and half-life) is necessary to enable application in the medical field. In this perspective, we describe the applications scenarios that can be envisioned in clinical practice and then we use those scenarios to explain the necessary properties that the photoresponsive tools used to control biological function should possess, highlighted by examples from medical imaging, drug delivery and photopharmacology. We then present how the (photo)chemical parameters are currently being optimized and an outlook is given on pharmacological aspects (toxicity, solubility, and stability) of light-responsive molecules. With these interdisciplinary insights, we aim to inspire the future directions for the development of photocontrolled tools that will empower clinical applications of light.

This perspective article explores the current state of light-controlled molecular tools for medical therapy and imaging and offers an outlook on clinical application scenarios and optimisation strategies.     

Machine learning dielectric screening for the simulation of excited state properties of molecules and materials

Accurate and efficient calculations of absorption spectra of molecules and materials are essential for the understanding and rational design of broad classes of systems. Solving the Bethe–Salpeter equation (BSE) for electron–hole pairs usually yields accurate predictions of absorption spectra, but it is computationally expensive, especially if thermal averages of spectra computed for multiple configurations are required. We present a method based on machine learning to evaluate a key quantity entering the definition of absorption spectra: the dielectric screening. We show that our approach yields a model for the screening that is transferable between multiple configurations sampled during first principles molecular dynamics simulations; hence it leads to a substantial improvement in the efficiency of calculations of finite temperature spectra. We obtained computational gains of one to two orders of magnitude for systems with 50 to 500 atoms, including liquids, solids, nanostructures, and solid/liquid interfaces. Importantly, the models of dielectric screening derived here may be used not only in the solution of the BSE but also in developing functionals for time-dependent density functional theory (TDDFT) calculations of homogeneous and heterogeneous systems. Overall, our work provides a strategy to combine machine learning with electronic structure calculations to accelerate first principles simulations of excited-state properties.

Machine learning can circumvent explicit calculation of dielectric response in first principles methods and accelerate simulations of optical properties of complex materials at finite temperature.     

Assessing the performance of rotational spectroscopy in chiral analysis

The capabilities of rotational spectroscopy-based methods as tools to deliver accurate and precise chirality-sensitive information are still breaking ground, but their applicability in the challenging field of analytical chemistry is already clear. In this mini review, we explore the current abilities and challenges of two emergent techniques for chiral analysis based on rotational spectroscopy. For that, we will showcase the two methods (microwave 3-wave mixing and chiral tag rotational spectroscopy) while testing their performance to solve the absolute configuration and the enantiomeric excess of a blind sample containing a mixture of enantiomers of styrene oxide.

Two rotational spectroscopy methods are challenged to solve the absolute configuration and enantiomeric excess of a chiral mixture of unknown composition.     

Catalytic enantioselective construction of vicinal quaternary carbon stereocenters

This review summarizes the advances in the catalytic enantioselective construction of vicinal quaternary carbon stereocenters, introduces major synthetic strategies and discusses their advantages and limitations, highlights the application of known protocols in the total synthesis of natural products, and outlines the synthetic opportunities.

This review summarizes the advances in catalytic enantioselective construction of vicinal quaternary carbon stereocenters, introduces major synthetic strategies and discusses their advantages and limitations, and outlines the synthetic opportunities.     

On the underestimated influence of synthetic conditions in solid ionic conductors

The development of high-performance inorganic solid electrolytes is central to achieving high-energy- density solid-state batteries. Whereas these solid-state materials are often prepared via classic solid-state syntheses, recent efforts in the community have shown that mechanochemical reactions, solution syntheses, microwave syntheses, and various post-synthetic heat treatment routines can drastically affect the structure and microstructure, and with it, the transport properties of the materials. On the one hand, these are important considerations for the upscaling of a materials processing route for industrial applications and industrial production. On the other hand, it shows that the influence of the different syntheses on the materials'' properties is neither well understood fundamentally nor broadly internalized well. Here we aim to review the recent efforts on understanding the influence of the synthetic procedure on the synthesis – (micro)structure – transport correlations in superionic conductors. Our aim is to provide the field of solid-state research a direction for future efforts to better understand current materials properties based on synthetic routes, rather than having an overly simplistic idea of any given composition having an intrinsic conductivity. We hope this review will shed light on the underestimated influence of synthesis on the transport properties of solid electrolytes toward the design of syntheses of future solid electrolytes and help guide industrial efforts of known materials.

Influence of synthesis and processing on the nature of ultimate product and the ionic transport properties of superionic conductors.     

Recent progress in the improvement of hydrothermal stability of zeolites

Zeolites have been successfully employed in many catalytic reactions of industrial relevance. The severe conditions required in some processes, where high temperatures are frequently combined with the presence of steam, highlight the need of considering the evolution of the catalyst structure during the reaction. This review attempts to summarize the recently developed strategies to improve the hydrothermal framework stability of zeolites.

This review attempts to summarize the recently developed strategies to improve the hydrothermal framework stability of zeolites.     

Tailor-made thermoplastic elastomers: customisable materials via modulation of molecular weight distributions

The ability to change polymer properties has in the past largely been a factor of modulating the molecular weight, molecular weight distribution breadth, crosslinking, or branching. The use of controlled MWD shape has recently emerged as a promising avenue towards modifying polymer properties. Taking advantage of molecular weight distribution shape, we report a simple and efficient approach for tuning material properties in polystyrene-block-polyisoprene-block-polystyrene (SIS) thermoplastic elastomers (TPEs). We find that the skew of the MWD function governs tensile properties and can be used as a handle to predictably vary polymer toughness while reducing energy dissipation.

Taking advantage of molecular weight distributions shape, we report a simple and efficient approach for predictably tuning material properties for thermoplastic elastomers.     

Pressure-driven,solvation-directed planar chirality switching of cyclophano-pillar[5]arenes (molecular universal joints)

Planar chiral cyclophanopillar[5]arenes with a fused oligo(oxyethylene) or polymethylene subring (MUJs), existing as an equilibrium mixture of subring-included (in) and -excluded (out) conformers, respond to hydrostatic pressure to exhibit dynamic chiroptical property changes, leading to an unprecedented pressure-driven chirality inversion and the largest ever-reported leap of anisotropy (g) factor for the MUJ with a dodecamethylene subring. The pressure susceptivity of MUJs, assessed by the change in g per unit pressure, is a critical function of the size and nature of the subring incorporated and the solvent employed. Mechanistic elucidations reveal that the in–out equilibrium, as the origin of the MUJ''s chiroptical property changes, is on a delicate balance of the competitive inclusion of subrings versus solvent molecules as well as the solvation of the excluded subring. The present results further encourage our use of pressure as a unique tool for dynamically manipulating various supramolecular devices/machines.

Pressure switches the in/out conformation of cyclophano-pillararenes with accompanying inversion of the chiroptical properties.     

Asymmetric systematic synthesis,structures, and (chir)optical properties of a series of dihetero[8]helicenes

A series of dihetero[8]helicenes have been systematically synthesized in enantiomerically enriched forms by utilizing the characteristic transformations of the organosulfur functionality. The synthetic route begins with assembling a ternaphthyl common synthetic intermediate from 2-naphthol and bissulfinylnaphthalene through an extended Pummerer reaction followed by facile multi-gram-scale resolution. The subsequent cyclization reactions into dioxa- and dithia[8]helicenes take place with excellent axial-to-helical chirality conversion. Dithia[8]helicene is further transformed into the nitrogen and the carbon analogs by replacing the two endocyclic sulfur atoms via S_NAr-based skeletal reconstruction. The efficient systematic synthesis has enabled comprehensive evaluation of physical properties, which has clarified the effect of the endocyclic atoms on their structures and (chir)optical properties as well as the unexpected conformational stability of the common helical framework.

A series of dihetero[8]helicenes have been synthesized in a stereoselective manner through an organosulfur-based synthetic strategy, which has enabled clarifying the effect of the endocyclic atoms on physical properties.     

BonDNet: a graph neural network for the prediction of bond dissociation energies for charged molecules

A broad collection of technologies, including e.g. drug metabolism, biofuel combustion, photochemical decontamination of water, and interfacial passivation in energy production/storage systems rely on chemical processes that involve bond-breaking molecular reactions. In this context, a fundamental thermodynamic property of interest is the bond dissociation energy (BDE) which measures the strength of a chemical bond. Fast and accurate prediction of BDEs for arbitrary molecules would lay the groundwork for data-driven projections of complex reaction cascades and hence a deeper understanding of these critical chemical processes and, ultimately, how to reverse design them. In this paper, we propose a chemically inspired graph neural network machine learning model, BonDNet, for the rapid and accurate prediction of BDEs. BonDNet maps the difference between the molecular representations of the reactants and products to the reaction BDE. Because of the use of this difference representation and the introduction of global features, including molecular charge, it is the first machine learning model capable of predicting both homolytic and heterolytic BDEs for molecules of any charge. To test the model, we have constructed a dataset of both homolytic and heterolytic BDEs for neutral and charged (−1 and +1) molecules. BonDNet achieves a mean absolute error (MAE) of 0.022 eV for unseen test data, significantly below chemical accuracy (0.043 eV). Besides the ability to handle complex bond dissociation reactions that no previous model could consider, BonDNet distinguishes itself even in only predicting homolytic BDEs for neutral molecules; it achieves an MAE of 0.020 eV on the PubChem BDE dataset, a 20% improvement over the previous best performing model. We gain additional insight into the model''s predictions by analyzing the patterns in the features representing the molecules and the bond dissociation reactions, which are qualitatively consistent with chemical rules and intuition. BonDNet is just one application of our general approach to representing and learning chemical reactivity, and it could be easily extended to the prediction of other reaction properties in the future.

Prediction of bond dissociation energies for charged molecules with a graph neural network enabled by global molecular features and reaction difference features between products and reactants.     

Mechanochemical reactivity inhibited,prohibited and reversed by liquid additives: examples from crystal-form screens

We demonstrate that liquid additives can exert inhibitive or prohibitive effects on the mechanochemical formation of multi-component molecular crystals, and report that certain additives unexpectedly prompt the dismantling of such solids into physical mixtures of their constituents. Computational methods were employed in an attempt to identify possible reasons for these previously unrecognised effects of liquid additives on mechanochemical transformations.

Liquid additives can exert catalytic, inhibitive or prohibitive effects on the mechanochemical formation of multi-component molecular crystals.     

Rapidly self-deoxygenating controlled radical polymerization in water via in situ disproportionation of Cu(i)

Rapidly self-deoxygenating Cu-RDRP in aqueous media is investigated. The disproportionation of Cu(i)/Me₆Tren in water towards Cu(ii) and highly reactive Cu(0) leads to O₂-free reaction environments within the first seconds of the reaction, even when the reaction takes place in the open-air. By leveraging this significantly fast O₂-reducing activity of the disproportionation reaction, a range of well-defined water-soluble polymers with narrow dispersity are attained in a few minutes or less. This methodology provides the ability to prepare block copolymers via sequential monomer addition with little evidence for chain termination over the lifetime of the polymerization and allows for the synthesis of star-shaped polymers with the use of multi-functional initiators. The mechanism of self-deoxygenation is elucidated with the use of various characterization tools, and the species that participate in the rapid oxygen consumption is identified and discussed in detail.

The rapidly self-deoxygenating Cu-RDRP in aqueous media is investigated.     

Shaping non-noble metal nanocrystals via colloidal chemistry

Non-noble metal nanocrystals with well-defined shapes have been attracting increasingly more attention in the last decade as potential alternatives to noble metals, by virtue of their earth abundance combined with intriguing physical and chemical properties relevant for both fundamental studies and technological applications. Nevertheless, their synthesis is still primitive when compared to noble metals. In this contribution, we focus on third row transition metals Mn, Fe, Co, Ni and Cu that are recently gaining interest because of their catalytic properties. Along with providing an overview on the state-of-the-art, we discuss current synthetic strategies and challenges. Finally, we propose future directions to advance the synthetic development of shape-controlled non-noble metal nanocrystals in the upcoming years.

This minireview describes the state-of-the-art of shape-controlled nanocrystals of third raw transition metals and discusses future directions to advance their synthetic development, which is important for many applications.     

Physically inspired deep learning of molecular excitations and photoemission spectra

Modern functional materials consist of large molecular building blocks with significant chemical complexity which limits spectroscopic property prediction with accurate first-principles methods. Consequently, a targeted design of materials with tailored optoelectronic properties by high-throughput screening is bound to fail without efficient methods to predict molecular excited-state properties across chemical space. In this work, we present a deep neural network that predicts charged quasiparticle excitations for large and complex organic molecules with a rich elemental diversity and a size well out of reach of accurate many body perturbation theory calculations. The model exploits the fundamental underlying physics of molecular resonances as eigenvalues of a latent Hamiltonian matrix and is thus able to accurately describe multiple resonances simultaneously. The performance of this model is demonstrated for a range of organic molecules across chemical composition space and configuration space. We further showcase the model capabilities by predicting photoemission spectra at the level of the GW approximation for previously unseen conjugated molecules.

A physically-inspired machine learning model for orbital energies is developed that can be augmented with delta learning to obtain photoemission spectra, ionization potentials, and electron affinities with experimental accuracy.