Two-dimensional halide perovskite lateral epitaxial heterostructures.

Epitaxial heterostructures based on oxide perovskites and III-V, II-VI and transition metal dichalcogenide semiconductors form the foundation of modern electronics and optoelectronics1-7. Halide perovskites-an emerging family of tunable semiconductors with desirable properties-are attractive for applications such as solution-processed solar cells, light-emitting diodes, detectors and lasers8-15. Their inherently soft crystal lattice allows greater tolerance to lattice mismatch, making them promising for heterostructure formation and semiconductor integration16,17. Atomically sharp epitaxial interfaces are necessary to improve performance and for device miniaturization. However, epitaxial growth of atomically sharp heterostructures of halide perovskites has not yet been achieved, owing to their high intrinsic ion mobility, which leads to interdiffusion and large junction widths18-21, and owing to their poor chemical stability, which leads to decomposition of prior layers during the fabrication of subsequent layers. Therefore, understanding the origins of this instability and identifying effective approaches to suppress ion diffusion are of great importance22-26. Here we report an effective strategy to substantially inhibit in-plane ion diffusion in two-dimensional halide perovskites by incorporating rigid π-conjugated organic ligands. We demonstrate highly stable and tunable lateral epitaxial heterostructures, multiheterostructures and superlattices. Near-atomically sharp interfaces and epitaxial growth are revealed by low-dose aberration-corrected high-resolution transmission electron microscopy. Molecular dynamics simulations confirm the reduced heterostructure disorder and larger vacancy formation energies of the two-dimensional perovskites in the presence of conjugated ligands. These findings provide insights into the immobilization and stabilization of halide perovskite semiconductors and demonstrate a materials platform for complex and molecularly thin superlattices, devices and integrated circuits.

Deep reaction network exploration of glucose pyrolysis.

Resolving the reaction networks associated with biomass pyrolysis is central to understanding product selectivity and aiding catalyst design to produce more valuable products. However, even the pyrolysis network of relatively simple [Formula: see text]-D-glucose remains unresolved due to its significant complexity in terms of the depth of the network and the number of major products. Here, a transition-state-guided reaction exploration has been performed that provides complete pathways to most significant experimental pyrolysis products of [Formula: see text]-D-glucose. The resulting reaction network involves over 31,000 reactions and transition states computed at the semiempirical quantum chemistry level and approximately 7,000 kinetically relevant reactions and transition states characterized with density function theory, comprising the largest reaction network reported for biomass pyrolysis. The exploration was conducted using graph-based rules to explore the reactivities of intermediates and an adaption of the Dijkstra algorithm to identify kinetically relevant intermediates. This simple exploration policy surprisingly (re)identified pathways to most major experimental pyrolysis products, many intermediates proposed by previous computational studies, and also identified new low-barrier reaction mechanisms that resolve outstanding discrepancies between reaction pathways and yields in isotope labeling experiments. This network also provides explanatory pathways for the high yield of hydroxymethylfurfural and the reaction pathway that contributes most to the formation of hydroxyacetaldehyde during glucose pyrolysis. Due to the limited domain knowledge required to generate this network, this approach should also be transferable to other complex reaction network prediction problems in biomass pyrolysis.

Anomalous magnetoresistance in a nonconjugated radical polymer glass.

Macromolecules bearing open-shell entities offer unique transport properties for both electronic and spintronic devices. This work demonstrates that, unlike their conjugated polymer counterparts, the charge carriers in radical polymers (i.e., macromolecules with nonconjugated backbones and with stable open-shell sites present at their pendant groups) are singlet cations, which opens significant avenues for manipulating macromolecular design for advanced solid-state transport in these highly transparent conductors. Despite this key point, magnetoresistive effects are present in radical polymer thin films under applied magnetic fields due to the presence of impurity sites in low (i.e., <1%) concentrations. Additionally, thermal annealing of poly(4-glycidyloxy-2,2,6,6- tetramethylpiperidine-1-oxyl) (PTEO), a nonconjugated polymer with stable open-shell pendant groups, facilitated better electron exchange and pairwise spin interactions resulting in an unexpected magnetoresistance signal at relatively low field strengths (i.e., <2 T). The addition of 4-hydroxy-2,2,6,6-tetramethylpiperidin-N-oxy (TEMPO-OH), a paramagnetic species, increased the magnitude of the MR effect when the small molecule was added to the radical polymer matrix. These macroscopic experimental observables are explained using computational approaches that detail the fundamental molecular principles. This intrinsic localized charge transport behavior differs from the current state of the art regarding closed-shell conjugated macromolecules, and it opens an avenue towards next-generation transport in organic electronic materials.

Graph to Activation Energy Models Easily Reach Irreducible Errors but Show Limited Transferability.

Activation energy characterization of competing reactions is a costly but crucial step for understanding the kinetic relevance of distinct reaction pathways, product yields, and myriad other properties of reacting systems. The standard methodology for activation energy characterization has historically been a transition state search using the highest level of theory that can be afforded. However, recently, several groups have popularized the idea of predicting activation energies directly based on nothing more than the reactant and product graphs, a sufficiently complex neural network, and a broad enough data set. Here, we have revisited this task using the recently developed Reaction Graph Depth 1 (RGD1) transition state data set and several newly developed graph attention architectures. All of these new architectures achieve similar state-of-the-art results of ∼4 kcal/mol mean absolute error on withheld testing sets of reactions but poor performance on external testing sets composed of reactions with differing mechanisms, reaction molecularity, or reactant size distribution. Limited transferability is also shown to be shared by other contemporary graph to activation energy architectures through a series of case studies. We conclude that an array of standard graph architectures can already achieve results comparable to the irreducible error of available reaction data sets but that out-of-distribution performance remains poor.

Enhanced Electron Transport in Nonconjugated Radical Oligomers Occurs by Tunneling.

Incorporating temperature- and air-stable organic radical species into molecular designs is a potentially advantageous means of controlling the properties of electronic materials. However, we still lack a complete understanding of the structure-property relationships of organic radical species at the molecular level. In this work, the charge transport properties of (2,2,6,6-tetramethylpiperidin-1-yl)oxyl (TEMPO) radical-containing nonconjugated molecules are studied using single-molecule charge transport experiments and molecular modeling. Importantly, the TEMPO pendant groups promote temperature-independent molecular charge transport in the tunneling region relative to the quenched and closed-shell phenyl pendant groups. Results from molecular modeling show that the TEMPO radicals interact with the gold metal electrodes near the interface to facilitate a high-conductance conformation. Overall, the large enhancement of charge transport by incorporation of open-shell species into a single nonconjugated molecular component opens exciting avenues for implementing molecular engineering in the development of next-generation electronic devices based on novel nonconjugated radical materials.

Reductive Doping Inhibits the Formation of Isomerization-Derived Structural Defects in N-doped Poly(benzodifurandione) (n-PBDF).

Recently, solution-processable n-doped poly(benzodifurandione) (n-PBDF) has been made through in-situ oxidative polymerization and reductive doping, which exhibited exceptionally high electrical conductivities and optical transparency. The discovery of n-PBDF is considered a breakthrough in the field of organic semiconductors. In the initial report, the possibility of structural defect formation in n-PBDF was proposed, based on the observation of structural isomerization from (E)-2H,2'H-[3,3'-bibenzofuranylidene]-2,2'-dione (isoxindigo) to chromeno[4,3-c]chromene-5,11-dione (dibenzonaphthyrone) in the dimer model reactions. In this study, we present clear evidence that structural isomerization is inhibited during polymerization. We reveal that the dimer (BFD1) and the trimer (BFD2) can be reductively doped by several mechanisms, including hydride transfer, forming charge transfer complexes (CTC) or undergoing an integer charge transfer (ICT) with reactants available during polymerization. Once the hydride transfer adducts, the CTC, or the ICT product forms, structural isomerization can be effectively prevented even at elevated temperatures. Our findings provide a mechanistic understanding of why isomerization-derived structural defects are absent in n-PBDF backbone. It lays a solid foundation for the future development of n-PBDF as a benchmark polymer for organic electronics and beyond.

Diffusion of volatile organics and water in the epicuticular waxes of petunia petal epidermal cells.

Plant cuticles are a mixture of crystalline and amorphous waxes that restrict the exchange of molecules between the plant and the atmosphere. The multicomponent nature of cuticular waxes complicates the study of the relationship between the physical and transport properties. Here, a model cuticle based on the epicuticular waxes of Petunia hybrida flower petals was formulated to test the effect of wax composition on diffusion of water and volatile organic compounds (VOCs). The model cuticle was composed of an n-tetracosane (C24 H50 ), 1-docosanol (C22 H45 OH), and 3-methylbutyl dodecanoate (C17 H34 O2 ), reflecting the relative chain length, functional groups, molecular arrangements, and crystallinity of the natural waxes. Molecular dynamics simulations were performed to obtain diffusion coefficients for compounds moving through waxes of varying composition. Simulated VOC diffusivities of the model system were found to highly correlate with in vitro measurements in isolated petunia cuticles. VOC diffusivity increased up to 30-fold in completely amorphous waxes, indicating a significant effect of crystallinity on cuticular permeability. The crystallinity of the waxes was highly dependent on the elongation of the lattice length and decrease in gap width between crystalline unit cells. Diffusion of water and higher molecular weight VOCs were significantly affected by alterations in crystalline spacing and lengths, whereas the low molecular weight VOCs were less affected. Comparison of measured diffusion coefficients from atomistic simulations and emissions from petunia flowers indicates that the role of the plant cuticle in the VOC emission network is attributed to the differential control on mass transfer of individual VOCs by controlling the composition, amount, and dynamics of scent emission.

Thermally Accessible Prebiotic Pathways for Forming Ribonucleic Acid and Protein Precursors from Aqueous Hydrogen Cyanide.

The search for prebiotic chemical pathways to biologically relevant molecules is a long-standing puzzle that has generated a menagerie of competing hypotheses with limited experimental prospects for falsification. However, the advent of computational network exploration methodologies has created the opportunity to compare the kinetic plausibility of various channels and even propose new pathways. Here, the space of organic molecules that can be formed within four polar or pericyclic reactions from water and hydrogen cyanide (HCN), two established prebiotic candidates for generating biological precursors, was comprehensively explored with a state-of-the-art exploration algorithm. A surprisingly diverse reactivity landscape was revealed within just a few steps of these simple molecules. Reaction pathways to several biologically relevant molecules were discovered involving lower activation energies and fewer reaction steps compared with recently proposed alternatives. Accounting for water-catalyzed reactions qualitatively affects the interpretation of the network kinetics. The case-study also highlights omissions of simpler and lower barrier reaction pathways to certain products by other algorithms that qualitatively affect the interpretation of HCN reactivity.

Emerging Two-Dimensional Organic Semiconductor-Incorporated Perovskites─A Fascinating Family of Hybrid Electronic Materials.

Halide perovskites have attracted a great amount of attention owing to their unique materials chemistry, excellent electronic properties, and low-cost manufacturing. Two-dimensional (2D) halide perovskites, originating from three-dimensional (3D) perovskite structures, are structurally more diverse and therefore create functional possibilities beyond 3D perovskites. The much less restrictive size constraints on the organic component of these hybrid materials particularly provide an exciting platform for designing unprecedented materials and functionalities at the molecular level. In this Perspective, we discuss the concept and recent development of a sub-class of 2D perovskites, namely, organic semiconductor-incorporated perovskites (OSiPs). OSiPs combine the electronic functionality of organic semiconductors with the soft and dynamic halide perovskite lattice, offering opportunities for tailoring the energy landscape, lattice and carrier dynamics, and electron/ion transport properties for various fundamental studies, as well as device applications. Specifically, we summarize recent advances in the design, synthesis, and structural analysis of OSiPs with various organic conjugated moieties as well as the application of OSiPs in photovoltaics, light-emitting devices, and transistors. Lastly, challenges and further opportunities for OSiPs in molecular design, integration of novel functionality, film quality, and stability issues are addressed.

Evidence That Less Can Be More for Transferable Force Fields.

Graph-based parameter assignment has been the basis for developing transferable force fields for molecular dynamics simulations for decades. Nevertheless, transferable force fields vary in how specifically terms are defined with respect to the molecular graph and the procedures for generating parametrization data. More-specific force-field terms increase the complexity of the force field, theoretically increasing accuracy but also increasing training data requirements. In contrast, less-specific force fields can be reused across larger regions of chemical space, theoretically reducing accuracy but also reducing the number of parameters and training data requirements. Here, the tradeoffs between force-field specificity and accuracy are quantified by parametrizing three new sets of force fields with varying levels of graph specificity, using a shared procedure for generating training data. These force fields are benchmarked for their ability to reproduce the structural features and liquid properties of 87 organic molecules at 146 distinct state points. The overall accuracy for properties that were directly trained on rapidly saturates as the graph specificity of the force-field increases. From this, we conclude there is at best a marginal benefit of using less transferable and more complex force fields with common sources of quantum-chemically derived training data. When looking at properties unseen during training, there is some evidence that the more-complex force fields even perform slightly worse. These results are rationalized by the fortuitous regularization of force fields based on less-specific and more-transferable atom types. Both the saturation in the accuracy of training properties and the marginally worse performance on off-target properties fundamentally contradict the expectation that bespoke force fields are generally more accurate, given their larger number of parameters, and suggests that increasing force-field complexity should be carefully justified against performance gains and balanced against available training data.

Δ-Learning applied to coarse-grained homogeneous liquids.

Coarse-grained molecular dynamics (CGMD) simulations address lengthscales and timescales that are critical to many chemical and material applications. Nevertheless, contemporary CGMD modeling is relatively bespoke and there are no black-box CGMD methodologies available that could play a comparable role in discovery applications that density functional theory plays for electronic structure. This gap might be filled by machine learning (ML)-based CGMD potentials that simplify model development, but these methods are still in their early stages and have yet to demonstrate a significant advantage over existing physics-based CGMD methods. Here, we explore the potential of Δ-learning models to leverage the advantages of these two approaches. This is implemented by using ML-based potentials to learn the difference between the target CGMD variable and the predictions of physics-based potentials. The Δ-models are benchmarked against the baseline models in reproducing on-target and off-target atomistic properties as a function of CG resolution, mapping operator, and system topology. The Δ-models outperform the reference ML-only CGMD models in nearly all scenarios. In several cases, the ML-only models manage to minimize training errors while still producing qualitatively incorrect dynamics, which is corrected by the Δ-models. Given their negligible added cost, Δ-models provide essentially free gains over their ML-only counterparts. Nevertheless, an unexpected finding is that neither the Δ-learning models nor the ML-only models significantly outperform the elementary pairwise models in reproducing atomistic properties. This fundamental failure is attributed to the relatively large irreducible force errors associated with coarse-graining that produces little benefit from using more complex potentials.

Design Rules for Two-Dimensional Organic Semiconductor-Incorporated Perovskites (OSiP) Gleaned from Thousands of Simulated Structures.

Two-dimensional (2D) halide perovskites are an attractive class of hybrid perovskites that have additional optoelectronic tunability due to their accommodation of relatively large organic ligands. Nevertheless, contemporary ligand design depends on either expensive trial-and-error testing of whether a ligand can be integrated within the lattice or conservative heuristics that unduly limit the scope of ligand chemistries. Here, the structural determinants of stable ligand incorporation within Ruddlesden-Popper (RP) phase perovskites are established by molecular dynamics (MD) simulations of over ten-thousand RP-phase perovskites and training of machine learning classifiers capable of predicting structural stability based solely on generalizable ligand features. The simulation results show near-perfect predictions of positive and negative literature examples, predict trade-offs between several ligand features and stability, and ultimately predict an inexhaustibly large 2D-compatible ligand design-space.

Electronic and Spintronic Open-Shell Macromolecules, Quo Vadis?

Open-shell macromolecules (i.e., polymers containing radical sites either along their backbones or at the pendant sites of repeat units) have attracted significant attention owing to their intriguing chemical and physical (e.g., redox, optoelectronic, and magnetic) properties, and they have been proposed and/or implemented in a wide range of potential applications (e.g., energy storage devices, electronic systems, and spintronic modules). These successes span multiple disciplines that range from advanced macromolecular chemistry through nanoscale structural characterization and on to next-generation solid-state physics and the associated devices. In turn, this has allowed different scientific communities to expand the palette of radical-containing polymers relatively quickly. However, critical gaps remain on many fronts, especially regarding the elucidation of key structure-property-function relationships that govern the underlying electrochemical, optoelectronic, and spin phenomena in these materials systems. Here, we highlight vital developments in the history of open-shell macromolecules to explain the current state of the art in the field. Moreover, we provide a critical review of the successes and bring forward open opportunities that, if solved, could propel this class of materials in a meaningful manner. Finally, we provide an outlook to address where it seems most likely that open-shell macromolecules will go in the coming years. Our considered view is that the future of radical-containing polymers is extremely bright and the addition of talented researchers with diverse skills to the field will allow these materials and their end-use devices to have a positive impact on the global science and technology enterprise in a relatively rapid manner.

Circularly Recyclable Polymers Featuring Topochemically Weakened Carbon-Carbon Bonds.

Closed-loop circular utilization of plastics is of manifold significance, yet energy-intensive and poorly selective scission of the ubiquitous carbon-carbon (C-C) bonds in contemporary commercial polymers pose tremendous challenges to envisioned recycling and upcycling scenarios. Here, we demonstrate a topochemical approach for creating elongated C-C bonds with a bond length of 1.57∼1.63 Å between repeating units in the solid state with decreased bond dissociation energies. Elongated bonds were introduced between the repeating units of 12 distinct polymers from three classes. In all cases, the materials exhibit rapid depolymerization via breakage of the elongated bond within a desirable temperature range (140∼260 °C) while otherwise remaining remarkably stable under harsh conditions. Furthermore, the topochemically prepared polymers are processable and 3D-printable while maintaining a high depolymerization yield and tunable mechanical properties. These results suggest that the crystalline polymers synthesized from simple photochemistry and without expensive catalysts are promising for practical applications with complete materials' circularity.

Actively Searching: Inverse Design of Novel Molecules with Simultaneously Optimized Properties.

Combining quantum chemistry characterizations with generative machine learning models has the potential to accelerate molecular discovery. In this paradigm, quantum chemistry acts as a relatively cost-effective oracle for evaluating the properties of particular molecules, while generative models provide a means of sampling chemical space based on learned structure-function relationships. For practical applications, multiple potentially orthogonal properties must be optimized in tandem during a discovery workflow. This carries additional difficulties associated with the specificity of the targets and the ability for the model to reconcile all properties simultaneously. Here, we demonstrate an active learning approach to improve the performance of multi-target generative chemical models. We first demonstrate the effectiveness of a set of baseline models trained on single property prediction tasks in generating novel compounds (i.e., not present in the training data) with various property targets, including both interpolative and extrapolative generation scenarios. For property ranges where accurate targeting proves difficult, the novel compounds suggested by the model are characterized using quantum chemistry and the new molecules closest to expressing the desired properties are fed back into the generative model for additional training. This gradually improves the generative models' understanding of targeted areas of chemical space and shifts the distribution of the generated compounds toward the targeted values. We then demonstrate the effectiveness of this active learning approach in generating compounds with multiple chemical constraints, including vertical ionization potential, electron affinity, and dipole moment targets, and validate the results at the ωB97X-D3/def2-TZVP level. This method requires no modifications to extant generative approaches, but rather utilizes their inherent generative and predictive aspects for self-refinement, and can be applied to situations where any number of properties with varying degrees of correlation must be optimized simultaneously.

Algorithmic Explorations of Unimolecular and Bimolecular Reaction Spaces.

Algorithmic reaction exploration based on transition state searches has already made inroads into many niche applications, but its potential as a general-purpose tool is still largely unrealized. Computational cost and the absence of benchmark problems involving larger molecules remain obstacles to further progress. Here an ultra-low cost exploration algorithm is implemented and used to explore the reactivity of unimolecular and bimolecular reactants, comprising a total of 581 reactions involving 51 distinct reactants. The algorithm discovers all established reaction pathways, where such comparisons are possible, while also revealing a much richer reactivity landscape, including lower barrier reaction pathways and a strong dependence of reaction conformation in the apparent barriers of the reported reactions. The diversity of these benchmarks illustrate that reaction exploration algorithms are approaching general-purpose capability.

Side-Chain Control of Topochemical Polymer Single Crystals with Tunable Elastic Modulus.

Topochemical polymerizations hold the promise of producing high molecular weight and stereoregular single crystalline polymers by first aligning monomers before polymerization. However, monomer modifications often alter the crystal packing and result in non-reactive polymorphs. Here, we report a systematic study on the side chain functionalization of the bis(indandione) derivative system that can be polymerized under visible light. Precisely engineered side chains help organize the monomer crystals in a one-dimensional fashion to maintain the topochemical reactivity. By optimizing the side chain length and end group of monomers, the elastic modulus of the resulting polymer single crystals can also be greatly enhanced. Lastly, using ultrasonication, insoluble polymer single crystals can be processed into free-standing and robust polymer thin films. This work provides new insights on the molecular design of topochemical reactions and paves the way for future applications of this fascinating family of materials.

Molecular Design Features for Charge Transport in Nonconjugated Radical Polymers.

Conducting polymers based on open-shell radical moieties exhibit potentially advantageous processing, stability, and optical attributes compared with conventional doped conjugated polymers. Despite their ascendance, reported radical conductors have been based almost exclusively on (2,2,6,6-tetramethylpiperidin-1-yl)oxyl (TEMPO), which raises fundamental questions regarding the ultimate limits of charge transport in these materials and whether some of the deficiencies exhibited by contemporary materials are due to the choice of radical chemistry. To address these questions, we have performed a density functional theory (DFT) study of the charge transfer characteristics of a broad range of open-shell chemistries relevant to radical conductors, including p-type, n-type, and ambipolar open-shell chemistries. We observe that far from being representative, TEMPO exhibits anomalously high reorganization energies due to strong charge localization. This, in turn, limits charge transfer in TEMPO compared with more delocalized open-shell species. By comprehensively mapping the dependence of charge transfer on radical-radical orientation, we have also identified a large mismatch between the conformations that are favored by intermolecular interactions and the conformations that maximize charge transfer in all of the open-shell chemistries investigated. These results suggest that significant opportunities exist to exploit directing interactions to promote charge transport in radical polymers.

Transferable Ring Corrections for Predicting Enthalpy of Formation of Cyclic Compounds.

Computational predictions of the thermodynamic properties of molecules and materials play a central role in contemporary reaction prediction and kinetic modeling. Due to the lack of experimental data and computational cost of high-level quantum chemistry methods, approximate methods based on additivity schemes and more recently machine learning are currently the only approaches capable of supplying the chemical coverage and throughput necessary for such applications. For both approaches, ring-containing molecules pose a challenge to transferability due to the nonlocal interactions associated with conjugation and strain that significantly impact thermodynamic properties. Here, we report the development of a self-consistent approach for parameterizing transferable ring corrections based on high-level quantum chemistry. The method is benchmarked against both the Pedley-Naylor-Kline experimental dataset for C-, H-, O-, N-, S-, and halogen-containing cyclic molecules and a dataset of Gaussian-4 quantum chemistry calculations. The prescribed approach is demonstrated to be superior to existing ring corrections while maintaining extensibility to arbitrary chemistries. We have also compared this ring-correction scheme against a novel machine learning approach and demonstrate that the latter is capable of exceeding the performance of physics-based ring corrections.

Topology Automated Force-Field Interactions (TAFFI): A Framework for Developing Transferable Force Fields.

Force-field development has undergone a revolution in the past decade with the proliferation of quantum chemistry based parametrizations and the introduction of machine learning approximations of the atomistic potential energy surface. Nevertheless, transferable force fields with broad coverage of organic chemical space remain necessary for applications in materials and chemical discovery where throughput, consistency, and computational cost are paramount. Here, we introduce a force-field development framework called Topology Automated Force-Field Interactions (TAFFI) for developing transferable force fields of varying complexity against an extensible database of quantum chemistry calculations. TAFFI formalizes the concept of atom typing and makes it the basis for generating systematic training data that maintains a one-to-one correspondence with force-field terms. This feature makes TAFFI arbitrarily extensible to new chemistries while maintaining internal consistency and transferability. As a demonstration of TAFFI, we have developed a fixed-charge force-field, TAFFI-gen, from scratch that includes coverage for common organic functional groups that is comparable to established transferable force fields. The performance of TAFFI-gen was benchmarked against OPLS and GAFF for reproducing several experimental properties of 87 organic liquids. The consistent performance of these force fields, despite their distinct origins, validates the TAFFI framework while also providing evidence of the representability limitations of fixed-charge force fields.