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  • Royal Society of Chemistry (RSC)  (4)
  • Bhat, Vinayak  (4)
  • 1
    Online Resource
    Online Resource
    Data storage architectures to accelerate chemical discovery: data accessibility for individual laboratories and the community
    Royal Society of Chemistry (RSC) ; 2022
    In:  Chemical Science Vol. 13, No. 46 ( 2022), p. 13646-13656
    Details
    In: Chemical Science, Royal Society of Chemistry (RSC), Vol. 13, No. 46 ( 2022), p. 13646-13656
    Abstract: As buzzwords like “big data,” “machine learning,” and “high-throughput” expand through chemistry, chemists need to consider more than ever their data storage, data management, and data accessibility, whether in their own laboratories or with the broader community. While it is commonplace for chemists to use spreadsheets for data storage and analysis, a move towards database architectures ensures that the data can be more readily findable, accessible, interoperable, and reusable (FAIR). However, making this move has several challenges for those with limited-to-no knowledge of computer programming and databases. This Perspective presents basics of data management using databases with a focus on chemical data. We overview database fundamentals by exploring benefits of database use, introducing terminology, and establishing database design principles. We then detail the extract, transform, and load process for database construction, which includes an overview of data parsing and database architectures, spanning Standard Query Language (SQL) and No-SQL structures. We close by cataloging overarching challenges in database design. This Perspective is accompanied by an interactive demonstration available at https://github.com/D3TaLES/databases_demo. We do all of this within the context of chemical data with the aim of equipping chemists with the knowledge and skills to store, manage, and share their data while abiding by FAIR principles.
    Type of Medium: Online Resource
    ISSN: 2041-6520 , 2041-6539
    URL: Article
    DOI: 10.1039/D2SC05142G
    Language: English
    Publisher: Royal Society of Chemistry (RSC)
    Publication Date: 2022
    detail.hit.zdb_id: 2559110-1
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  • 2
    Online Resource
    Online Resource
    Shear-aligned large-area organic semiconductor crystals through extended π–π interaction
    Royal Society of Chemistry (RSC) ; 2023
    In:  Journal of Materials Chemistry C Vol. 11, No. 26 ( 2023), p. 8992-9001
    Details
    In: Journal of Materials Chemistry C, Royal Society of Chemistry (RSC), Vol. 11, No. 26 ( 2023), p. 8992-9001
    Abstract: Small molecule-based organic semiconductors are of broad interest in organic field-effect transistors (OFETs) due to their potential for high crystallinity and electrical performance. The 2D molecule, TIPS-peri-pentacenopentacene (TIPS-PPP), which is the vertical extension of the 1D TIPS-pentacene (TIPS-PEN) molecule, offers a lower bandgap, higher aromaticity, and an enhanced π–π interaction with neighboring molecules in the solid state when compared to TIPS-PEN. However, an in-depth understanding of the relationship between the molecule structure, solid-state molecular packing, and the electronic properties has not been reported due to poor control over the TIPS-PPP crystallite size. In this work, we successfully engineered highly oriented large-area TIPS-PPP crystals through the solution shear coating technique. Compared with narrow ribbon-like TIPS-PEN crystals, TIPS-PPP crystals can grow centimeters long and over 500 μm wide. TIPS-PPP molecules are less susceptible to forming metastable polymorphs than TIPS-PEN molecules upon fast evaporation. The crystal structure of TIPS-PPP is also thermally stable at 250 °C. Notably, the anisotropic charge carrier mobility of TIPS-PPP crystals is resolved through fabricating bottom-gate top-contact devices, with a hole mobility of 3.1 cm 2 V −1 s −1 along the preferred packing direction. Further device optimization using top-gate bottom-contact devices improved the mobility up to 6.5 cm 2 V −1 s −1 , which is among the highest for pentacene-derivative-based organic semiconductors.
    Type of Medium: Online Resource
    ISSN: 2050-7526 , 2050-7534
    URL: Article
    DOI: 10.1039/D3TC01311A
    Language: English
    Publisher: Royal Society of Chemistry (RSC)
    Publication Date: 2023
    detail.hit.zdb_id: 2702245-6
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  • 3
    Online Resource
    Online Resource
    Towards a comprehensive data infrastructure for redox-active organic molecules targeting non-aqueous redox flow batteries
    Royal Society of Chemistry (RSC) ; 2023
    In:  Digital Discovery Vol. 2, No. 4 ( 2023), p. 1152-1162
    Details
    In: Digital Discovery, Royal Society of Chemistry (RSC), Vol. 2, No. 4 ( 2023), p. 1152-1162
    Abstract: The shift of energy production towards renewable, yet at times inconsistent, resources like solar and wind have increased the need for better energy storage solutions. An emerging energy storage technology that is highly scalable and cost-effective is the redox flow battery comprised of redox-active organic materials. Designing optimum materials for redox flow batteries involves balancing key properties such as the redox potential, stability, and solubility of the redox-active molecules. Here, we present the data-enabled discovery and design to transform liquid-based energy storage (D 3 TaLES) database, a curated data collection of more than 43 000 redox-active organic molecules that are of potential interest as the redox-active species for redox flow batteries with the aim to offer readily accessible and uniform data for big data metanalyses. D 3 TaLES raw data and derived properties are organized into a molecule-centric schema, and the database ontology contributes to the establishment of community reporting standards for electrochemical data. Data are readily accessed and analyzed through an easy-to-use web interface. The data infrastructure is coupled with data upload and processing tools that extract, transform, and load relevant data from raw computation or experimental data files, all of which are available to the public via a D 3 TaLES API. These processing tools along with an embedded high-throughput computational workflow enable community contributions and versatile data sharing and analyses, not only in redox-flow battery research but also in any field that applies redox-active organic molecules.
    Type of Medium: Online Resource
    ISSN: 2635-098X
    URL: Article
    DOI: 10.1039/D3DD00081H
    Language: English
    Publisher: Royal Society of Chemistry (RSC)
    Publication Date: 2023
    detail.hit.zdb_id: 3142965-8
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  • 4
    Online Resource
    Online Resource
    Electronic, redox, and optical property prediction of organic π-conjugated molecules through a hierarchy of machine learning approaches
    Royal Society of Chemistry (RSC) ; 2023
    In:  Chemical Science Vol. 14, No. 1 ( 2023), p. 203-213
    Details
    In: Chemical Science, Royal Society of Chemistry (RSC), Vol. 14, No. 1 ( 2023), p. 203-213
    Abstract: Accelerating the development of π-conjugated molecules for applications such as energy generation and storage, catalysis, sensing, pharmaceuticals, and (semi)conducting technologies requires rapid and accurate evaluation of the electronic, redox, or optical properties. While high-throughput computational screening has proven to be a tremendous aid in this regard, machine learning (ML) and other data-driven methods can further enable orders of magnitude reduction in time while at the same time providing dramatic increases in the chemical space that is explored. However, the lack of benchmark datasets containing the electronic, redox, and optical properties that characterize the diverse, known chemical space of organic π-conjugated molecules limits ML model development. Here, we present a curated dataset containing 25k molecules with density functional theory (DFT) and time-dependent DFT (TDDFT) evaluated properties that include frontier molecular orbitals, ionization energies, relaxation energies, and low-lying optical excitation energies. Using the dataset, we train a hierarchy of ML models, ranging from classical models such as ridge regression to sophisticated graph neural networks, with molecular SMILES representation as input. We observe that graph neural networks augmented with contextual information allow for significantly better predictions across a wide array of properties. Our best-performing models also provide an uncertainty quantification for the predictions. To democratize access to the data and trained models, an interactive web platform has been developed and deployed.
    Type of Medium: Online Resource
    ISSN: 2041-6520 , 2041-6539
    URL: Article
    DOI: 10.1039/D2SC04676H
    Language: English
    Publisher: Royal Society of Chemistry (RSC)
    Publication Date: 2023
    detail.hit.zdb_id: 2559110-1
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