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RESEARCH PAPER · The Journal of Chemical Physics · 2021

OCELOT: An infrastructure for data-driven research to discover and design crystalline organic semiconductors

Qianxiang Ai, Vinayak Bhat, Sean M. Ryno, Karol Jarolimek, Parker Sornberger, Andrew K. Smith, Michael M. Haley, John E. Anthony, Chad M. Risko

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OCELOT: An infrastructure for data-driven research to discover and design crystalline organic semiconductorsOriginalDelving into guest-free and He-filled sI and sII clathrate hydrates: a first-principles computational study8Predicting compositional changes of organic–inorganic hybrid materials with Augmented CycleGAN4The Solution is the Solution: Data-Driven Elucidation of Solution-to-Device Feature Transfer for π-Conjugated Polymer Semiconductors28Challenges in Information-Mining the Materials Literature: A Case Study and Perspective21Computational materials discovery5Machine Learning for the Design of Novel OLED Materials10The role of crystal packing on the optical response of trialkyltetrelethynyl acenes5Electronic, redox, and optical property prediction of organic π-conjugated molecules through a hierarchy of machine learning approaches44Machine learning of atomic force microscopy images of organic solar cells20Shear-aligned large-area organic semiconductor crystals through extended π–π interaction12Towards a comprehensive data infrastructure for redox-active organic molecules targeting non-aqueous redox flow batteries20Supramolecular Tuning of Exciton Transport in Pi-Peptide Assemblies15Twisted Crystalline Organic Semiconductor Photodetectors21Identification via Virtual Screening of Emissive Molecules with a Small Exciton–Vibration Coupling for High Color Purity and Potential Large Exciton Delocalization9Computational Approaches for Organic Semiconductors: From Chemical and Physical Understanding to Predicting New Materials102Factors Impacting Dihedral Angle Rotation and Classification in π-Conjugated Systems7Transfer learning for predicting reorganization energy11The COMPAS Project: A Computational Database ofPolycyclic Aromatic Systems. Phase 2:cata-condensed Hetero-Polycyclic Aromatic Systems0Advances in data‐assisted high‐throughput computations for material design112Data‐Driven Discovery of Organic Electronic Materials Enabled by Hybrid Top‐Down/Bottom‐Up Design30Conjugated Polymer Process Ontology and Experimental Data Repository for Organic Field-Effect Transistors9Benchmark Data Set of Crystalline Organic Semiconductors11Computational Insights into the Thermophysical, Conformational and Electronic Properties of Diketopyrrolopyrrole and Isoindigo Based Semiconducting Polymers1COMPAS-3: a dataset of peri -condensed polybenzenoid hydrocarbons21COMPAS-2: a dataset of cata-condensed hetero-polycyclic aromatic systems17Automated analysis of surface facets: the example of cesium telluride6Machine learned interatomic potentials for ternary carbides trained on the AFLOW database18Rapid Estimation of the Intermolecular Electronic Couplings and Charge-Carrier Mobilities of Crystalline Molecular Organic Semiconductors through a Machine Learning Pipeline16What has scripting ever done for us? The CSD Python application programming interface (API)33Property Prediction of Functional Organic Molecular Crystals with Graph Neural Networks1Beyond chemical structures: lessons and guiding principles for the next generation of molecular databases8X2-GNN: A Physical Message Passing Neural Network with Natural Generalization Ability to Large and Complex Molecules9Accelerating the discovery of high-mobility molecular semiconductors: a machine learning approach8Development of Organic Semiconductor Materials for Organic Solar Cells via the Integration of Computational Quantum Chemistry and AI-Powered Machine Learning17Advances in the Design and Discovery of Organic Semiconductors Aided by Machine Learning18Evaluating Molecular Similarity Measures: Do Similarity Measures Reflect Electronic Structure Properties?5COMPAS-4: A Data Set of (BN) 1 Substituted Cata -Condensed Polybenzenoid Hydrocarbons─Data Analysis and Feature Engineering11A database of steric and electronic properties of heteroaryl substituents12Virtual characterization via knowledge-enhanced representation learning: from organic conjugated molecules to devices1A Comparative Analysis of Crowdsourced and Kernel Density Approaches for Improved Accidents Blackspots Prediction Accuracy0OPECM: A comprehensive dataset for organic polymer energy conversion materials enabling data-driven interpretative analysis0A multireference excited state database for correlation-aware screening of organic semiconductors in excitonic energy-materials discovery0Machine Learning Potential-Enabled Platform for the In Silico Design of Functional Organic Molecular Crystals0MolGen-Transformer: A molecule language model for the generation and latent space exploration of organic molecules1High-throughput virtual screening for organic electronics: From energy levels to advanced properties and physical insight0

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  2. Predicting compositional changes of organic–inorganic hybrid materials with Augmented CycleGAN

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  3. The Solution is the Solution: Data-Driven Elucidation of Solution-to-Device Feature Transfer for π-Conjugated Polymer Semiconductors

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  6. Machine Learning for the Design of Novel OLED Materials

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  7. The role of crystal packing on the optical response of trialkyltetrelethynyl acenes

    The Journal of Chemical Physics · 2022-07-18

    Ling-yi Huang, Qianxiang Ai, Chad M. Risko

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  8. Electronic, redox, and optical property prediction of organic π-conjugated molecules through a hierarchy of machine learning approaches

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  9. Machine learning of atomic force microscopy images of organic solar cells

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  10. Shear-aligned large-area organic semiconductor crystals through extended π–π interaction

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  11. Towards a comprehensive data infrastructure for redox-active organic molecules targeting non-aqueous redox flow batteries

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  12. Supramolecular Tuning of Exciton Transport in Pi-Peptide Assemblies

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  13. Twisted Crystalline Organic Semiconductor Photodetectors

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  14. Identification via Virtual Screening of Emissive Molecules with a Small Exciton–Vibration Coupling for High Color Purity and Potential Large Exciton Delocalization

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  15. Computational Approaches for Organic Semiconductors: From Chemical and Physical Understanding to Predicting New Materials

    Chemical Reviews · 2023-05-04

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  16. Factors Impacting Dihedral Angle Rotation and Classification in π-Conjugated Systems

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    Rebekah Duke, Vinayak Bhat, Andrew K. Smith, Stephen M. Goodlett, Sergei Tretiak, Chad M. Risko

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  17. Transfer learning for predicting reorganization energy

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  18. The COMPAS Project: A Computational Database ofPolycyclic Aromatic Systems. Phase 2:cata-condensed Hetero-Polycyclic Aromatic Systems

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  19. Advances in data‐assisted high‐throughput computations for material design

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  20. Data‐Driven Discovery of Organic Electronic Materials Enabled by Hybrid Top‐Down/Bottom‐Up Design

    Advanced Materials · 2023-10-10

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  21. Conjugated Polymer Process Ontology and Experimental Data Repository for Organic Field-Effect Transistors

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  22. Benchmark Data Set of Crystalline Organic Semiconductors

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  23. Computational Insights into the Thermophysical, Conformational and Electronic Properties of Diketopyrrolopyrrole and Isoindigo Based Semiconducting Polymers

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  24. COMPAS-3: a dataset of peri -condensed polybenzenoid hydrocarbons

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  25. COMPAS-2: a dataset of cata-condensed hetero-polycyclic aromatic systems

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  26. Automated analysis of surface facets: the example of cesium telluride

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  27. Machine learned interatomic potentials for ternary carbides trained on the AFLOW database

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  30. Property Prediction of Functional Organic Molecular Crystals with Graph Neural Networks

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  31. Beyond chemical structures: lessons and guiding principles for the next generation of molecular databases

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  33. Accelerating the discovery of high-mobility molecular semiconductors: a machine learning approach

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