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RESEARCH PAPER · Journal of Materials Chemistry C · 2021

High-throughput virtual screening for organic electronics: a comparative study of alternative strategies

Ömer H. Omar, Marcos del Cueto, Tahereh Nematiaram, Alessandro Troisi

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High-throughput virtual screening for organic electronics: a comparative study of alternative strategiesOriginalReorganization energies of flexible organic molecules as a challenging target for machine learning enhanced virtual screening29The Problem of Scientific Regret in AI-Driven Materials Selection0Organic materials repurposing, a data set for theoretical predictions of new applications for existing compounds41Feasibility of p-Doped Molecular Crystals as Transparent Conductive Electrodes via Virtual Screening9Recent progress in generative adversarial networks applied to inversely designing inorganic materials: A brief review50Roles and opportunities for machine learning in organic molecular crystal structure prediction and its applications18Strategies for Enhancing the Dielectric Constant of Organic Materials39Deep transfer learning for predicting frontier orbital energies of organic materials using small data and its application to porphyrin photocatalysts13Design and screening of B–N functionalized non-fullerene acceptors for organic solar cells via multiscale computation6Generative organic electronic molecular design informed by quantum chemistry21Towards a comprehensive data infrastructure for redox-active organic molecules targeting non-aqueous redox flow batteries20Accurate Ionization Potentials, Electron Affinities, and Band Gaps from the ωLH22t Range-Separated Local Hybrid Functional: No Tuning Required26Identification of New Inverted Singlet-Triplet Cores by High--Throughput Virtual Screening0Identification of New Inverted Singlet-Triplet Cores by High--Throughput Virtual Screening0Generative Organic Electronic Molecular Design via Reinforcement Learning Integration with Quantum Chemistry: Tuning Singlet and Triplet Energy Energy Levels0Generative Organic Electronic Molecular Design via Reinforcement Learning Integration with Quantum Chemistry: Tuning Singlet and Triplet Energy Levels1Reverse Designing the Wavelength-Specific Thermally Activation Delayed Fluorescent Molecules Using a Genetic Algorithm Coupled with Cheap QM Methods8Identification of Unknown Inverted Singlet–Triplet Cores by High-Throughput Virtual Screening65From Concept to Synthesis: Developing Heat-Resistant High Explosives through Automated High-Throughput Virtual Screening14Virtual Screening and Binding Analysis of Potential CD58 Inhibitors in Colorectal Cancer (CRC)4Guided diffusion for inverse molecular design84Accelerated Organic Crystal Structure Prediction with Genetic Algorithms and Machine Learning24Ground‐State Orbital Descriptors for Accelerated Development of Organic Room‐Temperature Phosphorescent Materials46Ground‐State Orbital Descriptors for Accelerated Development of Organic Room‐Temperature Phosphorescent Materials9Uncertainty-Aware Surrogate Modeling of High-Throughput DFT Data for Rapid Materials Screening0Perspectives on Development of Optoelectronic Materials in Artificial Intelligence Age8Precise Regulation on the Bond Dissociation Energy of Exocyclic C–N Bonds in Various N-Heterocycle Electron Donors via Machine Learning17From Chemical Drawing to Electronic Properties of Semiconducting Polymers in Bulk: A Tool for Chemical Discovery8Nonadiabatic Simulation of Exciton Dynamics in Organic Semiconductors Using Neural Network-Based Frenkel Hamiltonian and Gradients9Rapid Estimation of fwhm for OLED Emission Spectra Using Bond-Length and Bond-Order Alterations5A semi-automated quantum-mechanical workflow for the generation of molecular monolayers and aggregates4Rapid Prototyping for Accelerated Establishment of Film Processing‐Performance Relationships in Silicon Phthalocyanine OFETs1A high throughput virtual screening approach for identifying new thermally activated delayed fluorescence-based emitters3Accelerating the discovery of high-mobility molecular semiconductors: a machine learning approach8Introduction to Predicting Properties of Organic Materials1Inverting singlet–triplet gaps by design7Predicting the excited-state properties of crystalline organic semiconductors using GW+BSE and machine learning10Screening the Bioactivity of the P450 Enzyme by Spiking Neural Networks1Development of Organic Semiconductor Materials for Organic Solar Cells via the Integration of Computational Quantum Chemistry and AI-Powered Machine Learning17Theoretical and experimental photovoltaic behaviors of an s-tetrazine based D-A copolymer3Finite element analysis, machine learning, and digital twins for soft robots: state-of-arts and perspectives41A Review of Machine Learning in Organic Solar Cells20A general protocol for phosphorescent platinum(II) complexes: generation, high throughput virtual screening and highly accurate predictions4Electron-phonon coupling and mobility modeling in organic semiconductors: Method and application to two tetracene polymorphs3Role of artificial intelligence in the design and discovery of next-generation battery electrolytes29Advances in the Design and Discovery of Organic Semiconductors Aided by Machine Learning18Simple Linear Regression Models for Prediction of Ionization Energies, Electron Affinities, and Fundamental Gaps of Atoms and Molecules2El Agente: An autonomous agent for quantum chemistry54Machine learning-driven design and optimization of electronic packaging: applications and future developments12Predictive Approaches for 3D ‐Printing: Methods and Approaches for Polymeric Materials5The Enduring Relevance of Semiempirical Quantum Mechanics4Computation and machine learning for materials: Past, present, and future perspectives9Fluorescence-based sensors for environmental monitoring of small hazardous molecules: Recent trends and future prospects23Data-Driven Recommendation of Optimal Tuning Scheme for Range-Separated Hybrid Functionals in Solution-Phase UV/Vis Absorption Energy Prediction6Electrons and Phonons in Pentacene: Coupling Patterns Reveal the Microscopic Origin of the Phonon Limited Mobility1A multireference excited state database for correlation-aware screening of organic semiconductors in excitonic energy-materials discovery0Artificial intelligence in the discovery and design of molecular semiconductors: a systematic review3Combining fingerprint-based deep neural networks with machine learning potentials for the study of eumelanin components0Learning the limits: how data, diversity, and representation control machine-learning predictions of reorganisation energy2Uncertainty guided inverse design of polymers0Accurate Prediction of Excited-State Energies from Molecular Orbital Energies Based on Graph Neural Network with Transfer Learning2Predicting S 1 TDDFT Energies from ZINDO Calculations Using Message-Passing ΔML with Electronically Informed Descriptors5From Quantum Mechanics to Coarse-Grained Models: Bridging the Gap toward Polymer Rational Design1High-throughput virtual screening for organic electronics: From energy levels to advanced properties and physical insight0Physics-informed transfer learning via frontier orbital pretraining for prediction of polymer electronic properties0Closed-Loop Machine Learning in Materials Discovery: From Predict-Validate-Update Paradigms to Autonomous Ecosystems0From Molecular Design to Device Performance: DFT and TD ‐ DFT Insights into Small‐Molecule Organic Solar Cells0Artificial Intelligence for Advanced Functional Materials: Progress and Emerging Frontiers0

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