Lamellar Ionenes with Highly Dissociative, Anionic Channels Provide Lower Barriers for Cation Transport.

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.

Mapping the Space of Photoswitchable Ligands and Photodruggable Proteins with Computational Modeling.

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.

Learning pair potentials using differentiable simulations.

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.

Expanded [23]-Helicene with Exceptional Chiroptical Properties via an Iterative Ring-Fusion Strategy.

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 (ΔG‡e) 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 ΔG‡e. 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 ΔG‡e 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.

From free-energy profiles to activation free energies.

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.

Tunable CHA/AEI Zeolite Intergrowths with A Priori Biselective Organic Structure-Directing Agents: Controlling Enrichment and Implications for Selective Catalytic Reduction of NOx.

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.

Benchmarking binding energy calculations for organic structure-directing agents in pure-silica zeolites.

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.

Graph similarity drives zeolite diffusionless transformations and intergrowth.

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.

Temperature-transferable coarse-graining of ionic liquids with dual graph convolutional neural networks.

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.

Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach.

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%.

UV-Vis spectrophotometry of quinone flow battery electrolyte for in situ monitoring and improved electrochemical modeling of potential and quinhydrone formation.

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) ∼ 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.

Photocell Optimization Using Dark State Protection.

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.

Comparative study of singlet oxygen production by photosensitiser dyes encapsulated in silicone: towards rational design of anti-microbial surfaces.

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.

Alkylating potential of styrene oxide: reactions and factors involved in the alkylation process.

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.

ZeoSyn: A Comprehensive Zeolite Synthesis Dataset Enabling Machine-Learning Rationalization of Hydrothermal Parameters.

Zeolites, nanoporous aluminosilicates with well-defined porous structures, are versatile materials with applications in catalysis, gas separation, and ion exchange. Hydrothermal synthesis is widely used for zeolite production, offering control over composition, crystallinity, and pore size. However, the intricate interplay of synthesis parameters necessitates a comprehensive understanding of synthesis-structure relationships to optimize the synthesis process. Hitherto, public zeolite synthesis databases only contain a subset of parameters and are small in scale, comprising up to a few thousand synthesis routes. We present ZeoSyn, a dataset of 23,961 zeolite hydrothermal synthesis routes, encompassing 233 zeolite topologies and 921 organic structure-directing agents (OSDAs). Each synthesis route comprises comprehensive synthesis parameters: 1) gel composition, 2) reaction conditions, 3) OSDAs, and 4) zeolite products. Using ZeoSyn, we develop a machine learning classifier to predict the resultant zeolite given a synthesis route with >70% accuracy. We employ SHapley Additive exPlanations (SHAP) to uncover key synthesis parameters for >200 zeolite frameworks. We introduce an aggregation approach to extend SHAP to all building units. We demonstrate applications of this approach to phase-selective and intergrowth synthesis. This comprehensive analysis illuminates the synthesis parameters pivotal in driving zeolite crystallization, offering the potential to guide the synthesis of desired zeolites. The dataset is available at https://github.com/eltonpan/zeosyn_dataset.

Atomic order of rare earth ions in a complex oxide: a path to magnetotaxial anisotropy.

Complex oxides offer rich magnetic and electronic behavior intimately tied to the composition and arrangement of cations within the structure. Rare earth iron garnet films exhibit an anisotropy along the growth direction which has long been theorized to originate from the ordering of different cations on the same crystallographic site. Here, we directly demonstrate the three-dimensional ordering of rare earth ions in pulsed laser deposited (EuxTm1-x)3Fe5O12 garnet thin films using both atomically-resolved elemental mapping to visualize cation ordering and X-ray diffraction to detect the resulting order superlattice reflection. We quantify the resulting ordering-induced 'magnetotaxial' anisotropy as a function of Eu:Tm ratio using transport measurements, showing an overwhelmingly dominant contribution from magnetotaxial anisotropy that reaches 30 kJ m-3 for garnets with x = 0.5. Control of cation ordering on inequivalent sites provides a strategy to control matter on the atomic level and to engineer the magnetic properties of complex oxides.

Design of Cytotoxic T Cell Epitopes by Machine Learning of Human Degrons.

Antigen processing is critical for therapeutic vaccines to generate epitopes for priming cytotoxic T cell responses against cancer and pathogens, but insufficient processing often limits the quantity of epitopes released. We address this challenge using machine learning to ascribe a proteasomal degradation score to epitope sequences. Epitopes with varying scores were translocated into cells using nontoxic anthrax proteins. Epitopes with a low score show pronounced immunogenicity due to antigen processing, but epitopes with a high score show limited immunogenicity. This work sheds light on the sequence-activity relationships between proteasomal degradation and epitope immunogenicity. We anticipate that future efforts to incorporate proteasomal degradation signals into vaccine designs will lead to enhanced cytotoxic T cell priming by these vaccines in clinical settings.

One-Pot Synthesis of CHA/ERI-Type Zeolite Intergrowth from a Single Multiselective Organic Structure-Directing Agent.

We report the one-pot synthesis of a chabazite (CHA)/erionite (ERI)-type zeolite intergrowth structure characterized by adjustable extents of intergrowth enrichment and Si/Al molar ratios. This method utilizes readily synthesizable 6-azaspiro[5.6]dodecan-6-ium as the exclusive organic structure-directing agent (OSDA) within a potassium-dominant environment. High-throughput simulations were used to accurately determine the templating energy and molecular shape, facilitating the selection of an optimally biselective OSDA from among thousands of prospective candidates. The coexistence of the crystal phases, forming a distinct structure comprising disk-like CHA regions bridged by ERI-rich pillars, was corroborated via rigorous powder X-ray diffraction and integrated differential-phase contrast scanning transmission electron microscopy (iDPC S/TEM) analyses. iDPC S/TEM imaging further revealed the presence of single offretite layers dispersed within the ERI phase. The ratio of crystal phases between CHA and ERI in this type of intergrowth could be varied systematically by changing both the OSDA/Si and K/Si ratios. Two intergrown zeolite samples with different Si/Al molar ratios were tested for the selective catalytic reduction (SCR) of NOx with NH3, showing competitive catalytic performance and hydrothermal stability compared to that of the industry-standard commercial NH3-SCR catalyst, Cu-SSZ-13, prevalent in automotive applications. Collectively, this work underscores the potential of our approach for the synthesis and optimization of adjustable intergrown zeolite structures, offering competitive alternatives for key industrial processes.

Mechanisms of lactone hydrolysis in acidic conditions.

The acid-catalyzed hydrolysis of linear esters and lactones was studied using a hybrid supermolecule-polarizable continuum model (PCM) approach including up to six water molecules. The compounds studied included two linear esters, four ß-lactones, two γ-lactones, and one δ-lactone: ethyl acetate, methyl formate, ß-propiolactone, ß-butyrolactone, ß-isovalerolactone, diketene (4-methyleneoxetan-2-one), γ-butyrolactone, 2(5H)-furanone, and δ-valerolactone. The theoretical results are in good quantitative agreement with the experimental measurements reported in the literature and also in excellent qualitative agreement with long-held views regarding the nature of the hydrolysis mechanisms at molecular level. The present results help to understand the balance between the unimolecular (A(AC)1) and bimolecular (A(AC)2) reaction pathways. In contrast to the experimental setting, where one of the two branches is often occluded by the requirement of rather extreme experimental conditions, we have been able to estimate both contributions for all the compounds studied and found that a transition from A(AC)2 to A(AC)1 hydrolysis takes place as acidity increases. A parallel work addresses the neutral and base-catalyzed hydrolysis of lactones.

Mechanisms of lactone hydrolysis in neutral and alkaline conditions.

The neutral and base-catalyzed hydrolysis of nine carboxylic acid esters was studied using a hybrid supermolecule-PCM approach including six explicit water molecules. The molecules studied included two linear esters, four ß-lactones, two γ-lactones, and one δ-lactone: ethyl acetate and methyl formate, ß-propiolactone, ß-butyrolactone, ß-isovalerolactone, diketene (4-methyleneoxetan-2-one), γ-butyrolactone, 2(5H)-furanone, and δ-valerolactone. DFT and ab initio methods were used to analyze the features of the various possible hydrolysis mechanisms. For all compounds, reasonable to very good qualitative and quantitative agreement with experimental work was found, and evidence is provided to support long-standing hypotheses regarding the role of solvent molecule as a base catalyst. In addition, novel evidence is presented for the existence of an elimination-addition mechanism in the basic hydrolysis of diketene. A parallel work addresses the acid-catalyzed hydrolysis of lactones.