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1.
J Chem Inf Model ; 62(22): 5435-5445, 2022 Nov 28.
Artigo em Inglês | MEDLINE | ID: mdl-36315033

RESUMO

Accurately predicting new polymers' properties with machine learning models apriori to synthesis has potential to significantly accelerate new polymers' discovery and development. However, accurately and efficiently capturing polymers' complex, periodic structures in machine learning models remains a grand challenge for the polymer cheminformatics community. Specifically, there has yet to be an ideal solution for the problems of how to capture the periodicity of polymers, as well as how to optimally develop polymer descriptors without requiring human-based feature design. In this work, we tackle these problems by utilizing a periodic polymer graph representation that accounts for polymers' periodicity and coupling it with a message-passing neural network that leverages the power of graph deep learning to automatically learn chemically relevant polymer descriptors. Remarkably, this approach achieves state-of-the-art performance on 8 out of 10 distinct polymer property prediction tasks. These results highlight the advancement in predictive capability that is possible through learning descriptors that are specifically optimized for capturing the unique chemical structure of polymers.


Assuntos
Quimioinformática , Polímeros , Humanos , Polímeros/química , Redes Neurais de Computação , Aprendizado de Máquina
2.
ACS Omega ; 7(3): 2624-2637, 2022 Jan 25.
Artigo em Inglês | MEDLINE | ID: mdl-35097261

RESUMO

The materials science community has been increasingly interested in harnessing the power of deep learning to solve various domain challenges. However, despite their effectiveness in building highly predictive models, e.g., predicting material properties from microstructure imaging, due to their opaque nature fundamental challenges exist in extracting meaningful domain knowledge from the deep neural networks. In this work, we propose a technique for interpreting the behavior of deep learning models by injecting domain-specific attributes as tunable "knobs" in the material optimization analysis pipeline. By incorporating the material concepts in a generative modeling framework, we are able to explain what structure-to-property linkages these black-box models have learned, which provides scientists with a tool to leverage the full potential of deep learning for domain discoveries.

3.
J Chem Inf Model ; 61(5): 2147-2158, 2021 05 24.
Artigo em Inglês | MEDLINE | ID: mdl-33899482

RESUMO

To expedite new molecular compound development, a long-sought goal within the chemistry community has been to predict molecules' bulk properties of interest a priori to synthesis from a chemical structure alone. In this work, we demonstrate that machine learning methods can indeed be used to directly learn the relationship between chemical structures and bulk crystalline properties of molecules, even in the absence of any crystal structure information or quantum mechanical calculations. We focus specifically on a class of organic compounds categorized as energetic materials called high explosives (HE) and predicting their crystalline density. An ongoing challenge within the chemistry machine learning community is deciding how best to featurize molecules as inputs into machine learning models-whether expert handcrafted features or learned molecular representations via graph-based neural network models-yield better results and why. We evaluate both types of representations in combination with a number of machine learning models to predict the crystalline densities of HE-like molecules curated from the Cambridge Structural Database, and we report the performance and pros and cons of our methods. Our message passing neural network (MPNN) based models with learned molecular representations generally perform best, outperforming current state-of-the-art methods at predicting crystalline density and performing well even when testing on a data set not representative of the training data. However, these models are traditionally considered black boxes and less easily interpretable. To address this common challenge, we also provide a comparison analysis between our MPNN-based model and models with fixed feature representations that provides insights as to what features are learned by the MPNN to accurately predict density.


Assuntos
Aprendizado de Máquina , Redes Neurais de Computação
4.
J Chem Inf Model ; 60(12): 6147-6154, 2020 12 28.
Artigo em Inglês | MEDLINE | ID: mdl-33245232

RESUMO

Packing motifs-patterns in how molecules orient relative to one another in a crystal structure-are an important concept in many subdisciplines of materials science because of correlations observed between specific packing motifs and properties of interest. That said, packing motif data sets have remained small and noisy due to intensive manual labeling processes and insufficient labeling schemes. The most prominent labeling algorithms calculate relative interplanar angles of nearest neighbor molecules to determine the packing motif of a molecular crystal, but this simple approach can fail when neighbors are naively sampled isotropically around the crystal structure. To remedy this issue, we propose an optimization algorithm, which rotates the molecular crystal structure to find representative molecules that inform the packing motif. We package this algorithm into an automated framework-Autopack-which both optimally rotates the crystal structure and labels the packing motif based on the appropriate neighboring molecules. In this work, we detail the Autopack framework and its performance, which shows improvements compared to previous state-of-the-art labeling methods, providing the first quantitative point of comparison for packing motif labeling algorithms. Furthermore, using Autopack (available at https://ipo.llnl.gov/technologies/software/autopack), we perform the first large-scale study of potential relationships between chemicals' compositions and packing motifs, which shows that these relationships are more complex than previously hypothesized from studies that used only tens of polycyclic aromatic hydrocarbon molecules. Autopack's capabilities help pose next steps for crystal engineering research focusing not only on a molecule's adoption of a specific packing motif but also on new structure-property relationships.


Assuntos
Algoritmos , Análise por Conglomerados , Estrutura Molecular
5.
J Chem Inf Model ; 60(6): 2876-2887, 2020 06 22.
Artigo em Inglês | MEDLINE | ID: mdl-32286818

RESUMO

Nanomaterials of varying compositions and morphologies are of interest for many applications from catalysis to optics, but the synthesis of nanomaterials and their scale-up are most often time-consuming and Edisonian processes. Information gleaned from the scientific literature can help inform and accelerate nanomaterials development, but again, searching the literature and digesting the information are time-consuming manual processes for researchers. To help address these challenges, we developed scientific article-processing tools that extract and structure information from the text and figures of nanomaterials articles, thereby enabling the creation of a personalized knowledgebase for nanomaterials synthesis that can be mined to help inform further nanomaterials development. Starting with a corpus of ∼35k nanomaterials-related articles, we developed models to classify articles according to the nanomaterial composition and morphology, extract synthesis protocols from within the articles' text, and extract, normalize, and categorize chemical terms within synthesis protocols. We demonstrate the efficiency of the proposed pipeline on an expert-labeled set of nanomaterials synthesis articles, achieving 100% accuracy on composition prediction, 95% accuracy on morphology prediction, 0.99 AUC on protocol identification, and up to a 0.87 F1-score on chemical entity recognition. In addition to processing articles' text, microscopy images of nanomaterials within the articles are also automatically identified and analyzed to determine the nanomaterials' morphologies and size distributions. To enable users to easily explore the database, we developed a complementary browser-based visualization tool that provides flexibility in comparing across subsets of articles of interest. We use these tools and information to identify trends in nanomaterials synthesis, such as the correlation of certain reagents with various nanomaterial morphologies, which is useful in guiding hypotheses and reducing the potential parameter space during experimental design.


Assuntos
Nanoestruturas , Catálise , Bases de Dados Factuais , Aprendizado de Máquina , Software
6.
Soft Matter ; 15(24): 4898-4904, 2019 Jun 19.
Artigo em Inglês | MEDLINE | ID: mdl-31166358

RESUMO

We demonstrate a scalable method to create metallic nanowire arrays and meshes over square-centimeter-areas with tunable sub-100 nm dimensions and geometries using the shear alignment of block copolymers. We use the block copolymer poly(styrene)-b-poly(2-vinyl pyridine) (PS-P2VP) since the P2VP block complexes with metal salts like Na2PtCl4, thereby enabling us to directly pattern nanoscale platinum features. We investigate what shear alignment processing parameters are necessary to attain high quality and well-ordered nanowire arrays and quantify how the block copolymer's molecular weight affects the resulting Pt nanowires' dimensions and defect densities. Through systematic studies of processing parameters and scanning transmission electron microscopy (STEM) tomography, we determine that the equivalent of 2-3 monolayers of PS-P2VP are required to produce a single layer of well-aligned nanowires. The resulting nanowires' widths and heights can be tuned between 11-27 nm and 9-50 nm, respectively, and have periodicites varying between 37 and 63 nm, depending on the choice of block copolymer molecular weight. We observe that the height-to-width ratio of the nanowires also increases with molecular weight, reaching a value of almost 2 with the largest dimensions fabricated. Furthermore, we demonstrate that an additional layer of Pt nanowires can be orthogonally aligned on top of and without disturbing an underlying layer, thereby enabling the fabrication of Pt nanowire meshes with tunable sub-100 nm dimensions and geometries over a cm2-area.

7.
Chem Mater ; 30(10): 3330-3337, 2018 May 22.
Artigo em Inglês | MEDLINE | ID: mdl-31178626

RESUMO

The macroscopic properties of molecular materials can be drastically influenced by their solid-state packing arrangements, of which there can be many (e.g., polymorphism). Strategies to controllably and predictively access select polymorphs are thus highly desired, but computationally predicting the conditions necessary to access a given polymorph is challenging with the current state of the art. Using derivatives of contorted hexabenzocoronene, cHBC, we employed data mining, rather than first-principles approaches, to find relationships between the crystallizing molecule, postdeposition solvent-vapor annealing conditions that induce polymorphic transformation, and the resulting polymorphs. This analysis yields a correlative function that can be used to successfully predict the appearance of either one of two polymorphs in thin films of cHBC derivatives. Within the postdeposition processing phase space of cHBC derivatives, we have demonstrated an approach to generate guidelines to select crystallization conditions to bias polymorph access. We believe this approach can be applied more broadly to accelerate the predictions of processing conditions to access desired molecular polymorphs, making progress toward one of the grand challenges identified by the Materials Genome Initiative.

8.
ACS Appl Mater Interfaces ; 9(40): 35360-35367, 2017 Oct 11.
Artigo em Inglês | MEDLINE | ID: mdl-28960951

RESUMO

Recent advancements with the directed assembly of block copolymers have enabled the fabrication over cm2 areas of highly ordered metal nanowire meshes, or nanolattices, which are of significant interest as transparent electrodes. Compared to randomly dispersed metal nanowire networks that have long been considered the most promising next-generation transparent electrode material, such ordered nanolattices represent a new design paradigm that is yet to be optimized. Here, through optical and electrical simulations, we explore the potential design parameters for such nanolattices as transparent conductive electrodes, elucidating relationships between the nanowire dimensions, defects, and the nanolattices' conductivity and transmissivity. We find that having an ordered nanowire network significantly decreases the length of nanowires required to attain both high transmissivity and high conductivity, and we quantify the network's tolerance to defects in relation to other design constraints. Furthermore, we explore how both optical and electrical anisotropy can be introduced to such nanolattices, opening an even broader materials design space and possible set of applications.

9.
J Vis Exp ; (105): e53235, 2015 Nov 05.
Artigo em Inglês | MEDLINE | ID: mdl-26574930

RESUMO

Efforts to assemble graphene into three-dimensional monolithic structures have been hampered by the high cost and poor processability of graphene. Additionally, most reported graphene assemblies are held together through physical interactions (e.g., van der Waals forces) rather than chemical bonds, which limit their mechanical strength and conductivity. This video method details recently developed strategies to fabricate mass-producible, graphene-based bulk materials derived from either polymer foams or single layer graphene oxide. These materials consist primarily of individual graphene sheets connected through covalently bound carbon linkers. They maintain the favorable properties of graphene such as high surface area and high electrical and thermal conductivity, combined with tunable pore morphology and exceptional mechanical strength and elasticity. This flexible synthetic method can be extended to the fabrication of polymer/carbon nanotube (CNT) and polymer/graphene oxide (GO) composite materials. Furthermore, additional post-synthetic functionalization with anthraquinone is described, which enables a dramatic increase in charge storage performance in supercapacitor applications.


Assuntos
Grafite/química , Elasticidade , Nanotubos de Carbono/química , Óxidos/química , Polímeros/química
10.
J Appl Crystallogr ; 47(Pt 6): 2090-2099, 2014 Dec 01.
Artigo em Inglês | MEDLINE | ID: mdl-25484845

RESUMO

The DPC toolkit is a simple-to-use computational tool that helps users identify the unit-cell lattice parameters of a crystal structure that are consistent with a set of two-dimensional grazing-incidence wide-angle X-ray scattering data. The input data requirements are minimal and easy to assemble from data sets collected with any position-sensitive detector, and the user is required to make as few initial assumptions about the crystal structure as possible. By selecting manual or automatic modes of operation, the user can either visually match the positions of the experimental and calculated reflections by individually tuning the unit-cell parameters or have the program perform this process for them. Examples that demonstrate the utility of this program include determining the lattice parameters of a polymorph of a fluorinated contorted hexabenzocoronene in a blind test and refining the lattice parameters of the thin-film phase of 5,11-bis(triethylsilylethynyl)anthradithiophene with the unit-cell dimensions of its bulk crystal structure being the initial inputs.

11.
J Am Chem Soc ; 136(44): 15749-56, 2014 Nov 05.
Artigo em Inglês | MEDLINE | ID: mdl-25317987

RESUMO

Though both the crystal structure and molecular orientation of organic semiconductors are known to impact charge transport in thin-film devices, separately accessing different polymorphs and varying the out-of-plane molecular orientation is challenging, typically requiring stringent control over film deposition conditions, film thickness, and substrate chemistry. Here we demonstrate independent tuning of the crystalline polymorph and molecular orientation in thin films of contorted hexabenzocoronene, c-HBC, during post-deposition processing without the need to adjust deposition conditions. Three polymorphs are observed, two of which have not been previously reported. Using our ability to independently tune the crystal structure and out-of-plane molecular orientation in thin films of c-HBC, we have decoupled and evaluated the effects that molecular packing and orientation have on device performance in thin-film transistors (TFTs). In the case of TFTs comprising c-HBC, polymorphism and molecular orientation are equally important; independently changing either one affects the field-effect mobility by an order of magnitude.


Assuntos
Compostos Orgânicos/química , Semicondutores , Difração de Raios X
12.
ACS Appl Mater Interfaces ; 6(16): 14533-42, 2014 Aug 27.
Artigo em Inglês | MEDLINE | ID: mdl-25089728

RESUMO

We designed and synthesized a series of isoindigo-based derivatives to investigate how chemical structure modification at both the 6,6'- and 5,5'-positions of the core with electron-rich and electron-poor moieties affect photophysical and redox properties as well as their solid-state organization. Our studies reveal that 6,6'-substitution on the isoindigo core results in a stronger intramolecular charge transfer band due to strong electronic coupling between the 6,6'-substituent and the core, whereas 5,5'-substitution induces a weaker CT band that is more sensitive to the electronic nature of the substituents. In the solid state, 6,6'-derivatives generally form J-aggregates, whereas 5,5'-derivatives form H-aggregates. With only two branched ethylhexyl side chains, the 6,6'-derivatives form organized lamellar structures in the solid state. The incorporation of electron-rich benzothiophene, BT, substituents further enhances ordering, likely because of strong intermolecular donor-acceptor interactions between the BT substituent and the electron-poor isoindigo core on neighboring compounds. Collectively, the enhanced photophysical properties and solid-state organization of the 6,6'-benzothiophene substituted isoindigo derivative compared to the other isoindigo derivatives examined in this study resulted in solar cells with higher power conversion efficiencies when blended with a fullerene derivative.


Assuntos
Elétrons , Indóis/química , Fotoquímica
13.
Chem Mater ; 26(22): 6570-6577, 2014 Nov 25.
Artigo em Inglês | MEDLINE | ID: mdl-25678745

RESUMO

We have synthesized three new isoindigo-based small molecules by extending the conjugated length through the incorporation of octyl-thiophene units between the isoindigo core and benzothiophene terminal units. Both UV-vis and Grazing incidence X-ray diffraction experiments show that such extension of the π-conjugated backbone can induce H-aggregation, and enhance crystallinity and molecular ordering of these isoindigo-based small molecules in the solid state. Compared to two other isoindigo-based derivatives in the series, the derivative with two octyl-thiophene units, BT-T2-ID, is the most crystalline and ordered, and its molecular packing motif appears to be substantially different. Devices utilizing these new extended isoindigo-based small molecules as the electron donor exhibit higher performance than those utilizing nonextended BT-ID as the electron donor. Particularly, devices containing BT-T2-ID in an as-cast blend with PC61BM show power conversion efficiencies up to 3.4%, which is comparable to the best devices containing isoindigo-based molecular semiconductors and is a record among devices containing isoindigo-based small molecules that were processed in the absence of any additives.

14.
J Am Chem Soc ; 135(6): 2207-12, 2013 Feb 13.
Artigo em Inglês | MEDLINE | ID: mdl-23363295

RESUMO

This work explores the formation of well-defined molecular p-n junctions in solution-processed self-assembled heterojunction solar cells using dodecyloxy-substituted contorted hexabenzocoronene (12-c-HBC) as a donor material and phenyl-C(70)-butyric acid methyl ester (PC(70)BM) as an acceptor. We find that the contorted 12-c-HBC molecules effectively assemble in solution to form a nested structure with the ball-shaped PC(70)BM. The result is a self-assembled molecular-scale p-n junction. When this well-defined p-n junction is embedded in active films, we can make efficient self-assembled solar cells with minimal amounts of donor material relative to the acceptor. The power conversion efficiency is drastically enhanced by the mode of donor and acceptor assembly within the film.

15.
ACS Nano ; 7(1): 294-300, 2013 Jan 22.
Artigo em Inglês | MEDLINE | ID: mdl-23228001

RESUMO

The structuring in organic electrically active thin films critically influences the performance of devices comprising them. Controlling film structure, however, remains challenging and generally requires stringent deposition conditions or modification of the substrate. To this end, we have developed post-deposition processing methods that are decoupled from the initial deposition conditions to induce different out-of-plane molecular orientations in contorted hexabenzocoronene (HBC) thin films. As-deposited HBC thin films lack any long-range order; subjecting them to post-deposition processing, such as hexanes-vapor annealing, thermal annealing, and physical contact with elastomeric poly(dimethyl siloxane), induces crystallization with increasing extents of preferential edge-on orientation, corresponding to greater degrees of in-plane π-stacking. Accordingly, transistors comprising HBC thin films that have been processed under these conditions exhibit field-effect mobilities that increase by as much as 2 orders of magnitude with increasing extents of molecular orientation. The ability to decouple HBC deposition from its subsequent structuring through post-deposition processing affords us the unique opportunity to tune competing molecule-molecule and molecule-solvent interactions, which ultimately leads to control over the structure and electrical function of HBC films.


Assuntos
Cristalização/métodos , Galvanoplastia/métodos , Membranas Artificiais , Nanoestruturas/química , Nanoestruturas/ultraestrutura , Compostos Policíclicos/química , Transistores Eletrônicos , Desenho de Equipamento , Análise de Falha de Equipamento , Teste de Materiais , Conformação Molecular , Tamanho da Partícula
16.
Org Lett ; 12(21): 4840-3, 2010 Nov 05.
Artigo em Inglês | MEDLINE | ID: mdl-20923201

RESUMO

Fluorinated, contorted hexabenzocoronenes (HBCs) have been synthesized in a facile manner via Suzuki-Miyaura coupling of fluorinated phenyl boronic acids followed by photocyclization and Scholl cyclization. In addition to the molecular conformation observed in previous HBC derivatives, close-contact fluorine-fluorine intramolecular interactions result in a metastable conformation not previously observed. Heating the metastable HBCs above 100 °C irreversibly converts them to the stable conformation, suggesting that the metastable conformation arises from a kinetically arrested state during cyclization.

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