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Frontal ring-opening metathesis polymerization (FROMP) involves a self-perpetuating exothermic reaction, which enables the rapid and energy-efficient manufacturing of thermoset polymers and composites. Current state-of-the-art reaction-diffusion FROMP models rely on a phenomenological description of the olefin metathesis kinetics, limiting their ability to model the governing thermo-chemical FROMP processes. Furthermore, the existing models are unable to predict the variations in FROMP kinetics with changes in the resin composition and as a result are of limited utility toward accelerated discovery of new resin formulations. In this work, we formulate a chemically meaningful model grounded in the established mechanism of ring-opening metathesis polymerization (ROMP). Our study aims to validate the hypothesis that the ROMP mechanism, applicable to monomer-initiator solutions below 100 °C, remains valid under the nonideal conditions encountered in FROMP, including ambient to >200 °C temperatures, sharp temperature gradients, and neat monomer environments. Through extensive simulations, we demonstrate that our mechanism-based model accurately predicts the FROMP behavior across various resin compositions, including polymerization front velocities and thermal characteristics (e.g., Tmax). Additionally, we introduce a semi-inverse workflow that predicts FROMP behavior from a single experimental data point. Notably, the physiochemical parameters utilized in our model can be obtained through DFT calculations and minimal experiments, highlighting the model's potential for rapid screening of new FROMP chemistries in pursuit of thermoset polymers with superior thermo-chemo-mechanical properties.
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Alternating copolymers are crucial for diverse applications. While dispersity (Æ, also known as molecular weight distribution, MWD) influences the properties of polymers, achieving low dispersities in alternating copolymers poses a notable challenge via free radical polymerizations (FRPs). In this work, we demonstrated an unexpected discovery that dispersities are affected by the participation of charge transfer complexes (CTCs) formed between monomer pairs during free radical alternating copolymerization, which have inspired the successful synthesis of various alternating copolymers with low dispersities (>30 examples, Æ = 1.13-1.39) under visible-light irradiation. The synthetic method is compatible with binary, ternary and quaternary alternating copolymerizations and is expandable for both fluorinated and non-fluorinated monomer pairs. DFT calculations combined with model experiments indicated that CTC-absent reaction exhibits higher propagation rates and affords fewer radical terminations, which could contribute to low dispersities. Based on the integration of Monte Carlo simulation and Bayesian optimization, we established the relationship map between FRP parameter space and dispersity, further suggested the correlation between low dispersities and higher propagation rates. Our research sheds light on dispersity control via FRPs and creates a novel platform to investigate polymer dispersity through machine learning.
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Solid polymer electrolytes have the potential to enable safer and more energy-dense batteries; however, a deeper understanding of their ion conduction mechanisms, and how they can be optimized by molecular design, is needed to realize this goal. Here, we investigate the impact of anion dissociation energy on ion conduction in solid polymer electrolytes via a novel class of ionenes prepared using acyclic diene metathesis (ADMET) polymerization of highly dissociative, liquid crystalline fluorinated aryl sulfonimide-tagged ("FAST") anion monomers. These ionenes with various cations (Li+, Na+, K+, and Cs+) form well-ordered lamellae that are thermally stable up to 180 °C and feature domain spacings that correlate with cation size, providing channels lined with dissociative FAST anions. Electrochemical impedance spectroscopy (EIS) and differential scanning calorimetry (DSC) experiments, along with nudged elastic band (NEB) calculations, suggest that cation motion in these materials operates via an ion-hopping mechanism. The activation energy for Li+ conduction is 59 kJ/mol, which is among the lowest for systems that are proposed to operate via an ion conduction mechanism that is decoupled from polymer segmental motion. Moreover, the addition of a cation-coordinating solvent to these materials led to a >1000-fold increase in ionic conductivity without detectable disruption of the lamellar structure, suggesting selective solvation of the lamellar ion channels. This work demonstrates that molecular design can facilitate controlled formation of dissociative anionic channels that translate to significant enhancements in ion conduction in solid polymer electrolytes.
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Light-activated drugs are a promising way to localize biological activity and minimize side effects. However, their development is complicated by the numerous photophysical and biological properties that must be simultaneously optimized. To accelerate the design of photoactive drugs, we describe a procedure that combines ligand-protein docking with chemical property prediction based on machine learning (ML). We apply this procedure to 58 proteins and 9000 photo-drug candidates based on azobenzene cis-trans isomerism. We find that most proteins display a preference for trans isomers over cis and that the binding affinities of nominally active/inactive pairs are in fact highly correlated. These findings have significant value for photopharmacology research, and reinforce the need for virtual screening to identify compounds with rare desirable properties. Further, we combine our procedure with quantum chemical validation to identify promising candidates for the photoactive inhibition of PARP1, an enzyme that is over-expressed in cancer cells. The top compounds are predicted to have long-lived active forms, differential bioactivity, and absorption in the near-infrared therapeutic window.
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Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos , Humanos , Ligantes , Simulação por Computador , Isomerismo , Aprendizado de MáquinaRESUMO
Learning pair interactions from experimental or simulation data is of great interest for molecular simulations. We propose a general stochastic method for learning pair interactions from data using differentiable simulations (DiffSim). DiffSim defines a loss function based on structural observables, such as the radial distribution function, through molecular dynamics (MD) simulations. The interaction potentials are then learned directly by stochastic gradient descent, using backpropagation to calculate the gradient of the structural loss metric with respect to the interaction potential through the MD simulation. This gradient-based method is flexible and can be configured to simulate and optimize multiple systems simultaneously. For example, it is possible to simultaneously learn potentials for different temperatures or for different compositions. We demonstrate the approach by recovering simple pair potentials, such as Lennard-Jones systems, from radial distribution functions. We find that DiffSim can be used to probe a wider functional space of pair potentials compared with traditional methods like iterative Boltzmann inversion. We show that our methods can be used to simultaneously fit potentials for simulations at different compositions and temperatures to improve the transferability of the learned potentials.
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Expanded helicenes are an emerging class of helical nanocarbons composed of alternating linear and angularly fused rings, which give rise to an internal cavity and a large diameter. The latter is expected to impart exceptional chiroptical properties, but low enantiomerization free energy barriers (ΔGe) have largely precluded experimental interrogation of this prediction. Here, we report the syntheses of expanded helicenes containing 15, 19, and 23 rings on the inner helical circuit, using two iterations of an Ir-catalyzed, site-selective [2 + 2 + 2] reaction. This series of compounds displays a linear relationship between the number of rings and ΔGe. The expanded [23]-helicene, which is 7 rings longer than any known single carbohelicene and among the longest known all-carbon ladder oligomers, exhibits a ΔGe that is high enough (29.2 ± 0.1 kcal/mol at 100 °C in o-DCB) to halt enantiomerization at ambient temperature. This enabled the isolation of enantiopure samples displaying circular dichroism dissymmetry factors of ±0.056 at 428 nm, which are ≥1.7× larger than values for previously reported classical and expanded helicenes. Computational investigations suggest that this improved performance is the result of both the increased diameter and length of the [23]-helicene, providing guiding design principles for high dissymmetry molecular materials.
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Carbono , Compostos Policíclicos , Dicroísmo CircularRESUMO
Given a chemical reaction going from reactant (R) to the product (P) on a potential energy surface (PES) and a collective variable (CV) discriminating between R and P, we define the free-energy profile (FEP) as the logarithm of the marginal Boltzmann distribution of the CV. This FEP is not a true free energy. Nevertheless, it is common to treat the FEP as the "free-energy" analog of the minimum potential energy path and to take the activation free energy, ΔFRP , as the difference between the maximum at the transition state and the minimum at R. We show that this approximation can result in large errors. The FEP depends on the CV and is, therefore, not unique. For the same reaction, different discriminating CVs can yield different ΔFRP . We derive an exact expression for the activation free energy that avoids this ambiguity. We find ΔFRP to be a combination of the probability of the system being in the reactant state, the probability density on the dividing surface, and the thermal de Broglie wavelength associated with the transition. We apply our formalism to simple analytic models and realistic chemical systems and show that the FEP-based approximation applies only at low temperatures for CVs with a small effective mass. Most chemical reactions occur on complex, high-dimensional PES that cannot be treated analytically and pose the added challenge of choosing a good CV. We study the influence of that choice and find that, while the reaction free energy is largely unaffected, ΔFRP is quite sensitive.
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A novel ab initio methodology based on high-throughput simulations has permitted designing unique biselective organic structure-directing agents (OSDAs) that allow the efficient synthesis of CHA/AEI zeolite intergrowth materials with controlled phase compositions. Distinctive local crystallographic ordering of the CHA/AEI intergrowths was revealed at the nanoscale level using integrated differential phase contrast scanning transmission electron microscopy (iDPC STEM). These novel CHA/AEI materials have been tested for the selective catalytic reduction (SCR) of NOx, presenting an outstanding catalytic performance and hydrothermal stability, even surpassing the performance of the well-established commercial CHA-type catalyst. This methodology opens the possibility for synthetizing new zeolite intergrowths with more complex structures and unique catalytic properties.
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Molecular modeling plays an important role in the discovery of organic structure-directing agents (OSDAs) for zeolites. By quantifying the intensity of host-guest interactions, it is possible to select cost-effective molecules that maximize binding toward a given zeolite framework. Over the last few decades, a variety of methods and levels of theory have been used to calculate these binding energies. Nevertheless, there is no consensus on the best calculation strategy for high-throughput virtual screening undertakings. In this work, we compare binding affinities from density functional theory (DFT) and Dreiding force field calculations for 272 zeolite-OSDA pairs obtained from static and time-averaged simulations. Enabled by automation software, we show that Dreiding binding energies from the frozen pose method correlate best with DFT energies. They are also less sensitive to the choice of initial lattice parameters and optimization algorithms, as well as less computationally expensive than their time-averaged counterparts. Furthermore, we demonstrate that a broader exploration of the conformation space from molecular dynamics simulations does not provide significant improvements in binding energy trends over the frozen pose method despite being orders of magnitude more expensive. The code and benchmark data are open-sourced and provide robust and computationally efficient guidelines to calculating binding energies in zeolite-OSDA pairs.
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Predicting and directing polymorphic transformations is a critical challenge in zeolite synthesis1-3. Interzeolite transformations enable selective crystallization4-7, but are often too complex to be designed by comparing crystal structures. Here, computational and theoretical tools are combined to both exhaustively data mine polymorphic transformations reported in the literature and analyse and explain interzeolite relations. It was found that crystallographic building units are weak predictors of topology interconversion and insufficient to explain intergrowth. By introducing a supercell-invariant metric that compares crystal structures using graph theory, we show that diffusionless (topotactic and reconstructive) transformations occur only between graph-similar pairs. Furthermore, all the known instances of intergrowth occur between either structurally similar or graph similar frameworks. We identify promising pairs to realize diffusionless transformations and intergrowth, with hundreds of low-distance pairs identified among known zeolites, and thousands of hypothetical frameworks connected to known zeolite counterparts. The theory may enable the understanding and control of zeolite polymorphism.
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Computer simulations can provide mechanistic insight into ionic liquids (ILs) and predict the properties of experimentally unrealized ion combinations. However, ILs suffer from a particularly large disparity in the time scales of atomistic and ensemble motion. Coarse-grained models are therefore used in place of costly all-atom simulations, accessing longer time scales and larger systems. Nevertheless, constructing the many-body potential of mean force that defines the structure and dynamics of a coarse-grained system can be complicated and computationally intensive. Machine learning shows great promise for the linked challenges of dimensionality reduction and learning the potential of mean force. To improve the coarse-graining of ILs, we present a neural network model trained on all-atom classical molecular dynamics simulations. The potential of mean force is expressed as two jointly trained neural network interatomic potentials that learn the coupled short-range and many-body long range molecular interactions. These interatomic potentials treat temperature as an explicit input variable to capture its influence on the potential of mean force. The model reproduces structural quantities with high fidelity, outperforms the temperature-independent baseline at capturing dynamics, generalizes to unseen temperatures, and incurs low simulation cost.
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Virtual screening is becoming a ground-breaking tool for molecular discovery due to the exponential growth of available computer time and constant improvement of simulation and machine learning techniques. We report an integrated organic functional material design process that incorporates theoretical insight, quantum chemistry, cheminformatics, machine learning, industrial expertise, organic synthesis, molecular characterization, device fabrication and optoelectronic testing. After exploring a search space of 1.6 million molecules and screening over 400,000 of them using time-dependent density functional theory, we identified thousands of promising novel organic light-emitting diode molecules across the visible spectrum. Our team collaboratively selected the best candidates from this set. The experimentally determined external quantum efficiencies for these synthesized candidates were as large as 22%.
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Quinone-based aqueous flow batteries provide a potential opportunity for large-scale, low-cost energy storage due to their composition from earth abundant elements, high aqueous solubility, reversible redox kinetics and their chemical tunability such as reduction potential. In an operating flow battery utilizing 9,10-anthraquinone-2,7-disulfonic acid, the aggregation of an oxidized quinone and a reduced hydroquinone to form a quinhydrone dimer causes significant variations from ideal solution behavior and of optical absorption from the Beer-Lambert law. We utilize in situ UV-Vis spectrophotometry to establish (a), quinone, hydroquinone and quinhydrone molar attenuation profiles and (b), an equilibrium constant for formation of the quinhydrone dimer (KQHQ) â¼ 80 M-1. We use the molar optical attenuation profiles to identify the total molecular concentration and state of charge at arbitrary mixtures of quinone and hydroquinone. We report density functional theory calculations to support the quinhydrone UV-Vis measurements and to provide insight into the dimerization conformations. We instrument a quinone-bromine flow battery with a Pd-H reference electrode in order to demonstrate how complexation in both the negative (quinone) and positive (bromine) electrolytes directly impacts measured half-cell and full-cell voltages. This work shows how accounting for electrolyte complexation improves the accuracy of electrochemical modeling of flow battery electrolytes.
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Conventional photocells suffer a fundamental efficiency threshold imposed by the principle of detailed balance, reflecting the fact that good absorbers must necessarily also be fast emitters. This limitation can be overcome by "parking" the energy of an absorbed photon in a dark state which neither absorbs nor emits light. Here we argue that suitable dark states occur naturally as a consequence of the dipole-dipole interaction between two proximal optical dipoles for a wide range of realistic molecular dimers. We develop an intuitive model of a photocell comprising two light-absorbing molecules coupled to an idealized reaction center, showing asymmetric dimers are capable of providing a significant enhancement of light-to-current conversion under ambient conditions. We conclude by describing a road map for identifying suitable molecular dimers for demonstrating this effect by screening a very large set of possible candidate molecules.
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Surfaces with built-in antimicrobial activity have the potential to reduce hospital-acquired infections. One promising strategy is to create functionalised surfaces which, following illumination with visible light, are able to generate singlet oxygen under aerobic conditions. In contrast to antibiotics, the mechanism of bacterial kill by species derived from reactions with singlet oxygen is completely unselective, therefore offering little room for evolutionary adaptation. Here we consider five commercially available organic photosensitiser dyes encapsulated in silicone polymer that show varied antimicrobial activity. We correlate density functional theory calculations with UV-Vis spectroscopy, electron paramagnetic resonance spectroscopy and singlet oxygen production measurements in order to define and test the elements required for efficacious antimicrobial activity. Our approach forms the basis for the rational in silico design and spectroscopic screening of simple and efficient self-sterilising surfaces made from cheap, low toxicity photosensitiser dyes encapsulated in silicone.
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Anti-Infecciosos/química , Fármacos Fotossensibilizantes/química , Silicones/química , Oxigênio Singlete/química , Corantes , Espectroscopia de Ressonância de Spin EletrônicaRESUMO
The chemical reactivity of styrene-7,8-oxide (SO), an alkylating agent with high affinity for the guanineN7 position and a probable carcinogen for humans, with 4-(p-nitrobenzyl)pyridine (NBP), a trap for alkylating agents with nucleophilic characteristics similar to those of DNA bases, was investigated kinetically in water/dioxane media. UVvis spectrophotometry and ultrafast liquid chromatography were used to monitor the reactions involved. It was found that in the alkylation process four reactions occur simultaneously: (a) the formation of a ß-NBPSO adduct through an SN2 mechanism; (b) the acid-catalyzed formation of the stable α-NBPSO adduct through an SN2' mechanism; (c) the base-catalyzed hydrolysis of the ß-adduct, and (d) the acid-catalyzed hydrolysis of SO. At 37.5 °C and pH = 7.0 (in 7:3 water/dioxane medium), the values of the respective reaction rate constants were as follows: kalkß = (2.1 ± 0.3) × 104 M1 s1, kalkα = (1.0 ± 0.1) × 104 M1 s1, khydAD = (3.06 ± 0.09) × 106 s1, and khyd = (4.2 ± 0.9) × 106 s1. These values show that, in order to determine the alkylating potential of SO, none of the four reactions involved can be neglected. Temperature and pH were found to exert a strong influence on the values of some parameters that may be useful to investigate possible chemicobiological correlations (e.g., in the pH 5.817.69 range, the fraction of total adducts formed increased from 24% to 90% of the initial SO, whereas the adduct lifetime of the unstable ß-adduct, which gives an idea of the permanence of the adduct over time, decreased from 32358 to 13313 min). A consequence of these results is that the conclusions drawn in studies addressing alkylation reactions at temperatures and/or pH far from those of biological conditions should be considered with some reserve.
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Alquilantes/química , Compostos de Epóxi/química , Piridinas/química , Alquilação , Catálise , Dioxanos/química , Concentração de Íons de Hidrogênio , Cinética , Temperatura , Água/químicaRESUMO
In molecular dynamics simulations, rare events, such as protein folding, are typically studied using enhanced sampling techniques, most of which are based on the definition of a collective variable (CV) along which acceleration occurs. Obtaining an expressive CV is crucial, but often hindered by the lack of information about the particular event, e.g., the transition from unfolded to folded conformation. We propose a simulation-free data augmentation strategy using physics-inspired metrics to generate geodesic interpolations resembling protein folding transitions, thereby improving sampling efficiency without true transition state samples. This new data can be used to improve the accuracy of classifier-based methods. Alternatively, a regression-based learning scheme for CV models can be adopted by leveraging the interpolation progress parameter.
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The reactivity of silicates in aqueous solution is relevant to various chemistries ranging from silicate minerals in geology, to the C-S-H phase in cement, nanoporous zeolite catalysts, or highly porous precipitated silica. While simulations of chemical reactions can provide insight at the molecular level, balancing accuracy and scale in reactive simulations in the condensed phase is a challenge. Here, we demonstrate how a machine-learning reactive interatomic potential trained on PaiNN architecture can accurately capture silicate-water reactivity. The model was trained on a dataset comprising 400,000 energies and forces of molecular clusters at the ωB97X-D3/def2-TZVP level. To ensure the robustness of the model, we introduce a general active learning strategy based on the attribution of the model uncertainty, that automatically isolates uncertain regions of bulk simulations to be calculated as small-sized clusters. The potential reproduces static and dynamic properties of liquid water and solid crystalline silicates, despite having been trained exclusively on cluster data. Furthermore, we utilize enhanced sampling simulations to recover the self-ionization reactivity of water accurately, and the acidity of silicate oligomers, and lastly study the silicate dimerization reaction in a water solution at neutral conditions and find that the reaction occurs through a flanking mechanism.
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Structure-based methods in drug discovery have become an integral part of the modern drug discovery process. The power of virtual screening lies in its ability to rapidly and cost-effectively explore enormous chemical spaces to select promising ligands for further experimental investigation. Relative free energy perturbation (RFEP) and similar methods are the gold standard for binding affinity prediction in drug discovery hit-to-lead and lead optimization phases, but have high computational cost and the requirement of a structural analog with a known activity. Without a reference molecule requirement, absolute FEP (AFEP) has, in theory, better accuracy for hit ID, but in practice, the slow throughput is not compatible with VS, where fast docking and unreliable scoring functions are still the standard. Here, we present an integrated workflow to virtually screen large and diverse chemical libraries efficiently, combining active learning with a physics-based scoring function based on a fast absolute free energy perturbation method. We validated the performance of the approach in the ranking of structurally related ligands, virtual screening hit rate enrichment, and active learning chemical space exploration; disclosing the largest reported collection of free energy simulations to date.
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Frontal ring-opening metathesis polymerization (FROMP) presents an energy-efficient approach to produce high-performance polymers, typically utilizing norbornene derivatives from Diels-Alder reactions. This study broadens the monomer repertoire for FROMP, incorporating the cycloaddition product of biosourced furan compounds and benzyne, namely 1,4-dihydro-1,4-epoxynaphthalene (HEN) derivatives. A computational screening of Diels-Alder products is conducted, selecting products with resistance to retro-Diels-Alder but also sufficient ring strain to facilitate FROMP. The experiments reveal that varying substituents both modulate the FROMP kinetics and enable the creation of thermoplastic materials characterized by different thermomechanical properties. Moreover, HEN-based crosslinkers are designed to enhance the resulting thermomechanical properties at high temperatures (>200 °C). The versatility of such materials is demonstrated through direct ink writing (DIW) to rapidly produce 3D structures without the need for printed supports. This research significantly extends the range of monomers suitable for FROMP, furthering efficient production of high-performance polymeric materials.