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RESEARCH PAPER · The Journal of Physical Chemistry A · 2019

Prediction of Intramolecular Reorganization Energy Using Machine Learning

Şule Atahan-Evrenk, F. Betul Atalay

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Prediction of Intramolecular Reorganization Energy Using Machine LearningOriginalA high throughput molecular screening for organic electronics via machine learning: present status and perspective72Unraveling Correlations between Molecular Properties and Device Parameters of Organic Solar Cells Using Machine Learning107Quantum Chemistry in the Age of Machine Learning417Computational Screening of Organic Semiconductors: Exploring Side-Group Functionalisation and Assembly to Optimise Charge Transport in Chiral Molecules3Computational Screening of Organic Semiconductors: Exploring Side-Group Functionalisation and Assembly to Optimise Charge Transport in Chiral Molecules2Machine-Learned Charge Transfer Integrals for Multiscale Simulations in Organic Thin Films42A time and resource efficient machine learning assisted design of non-fullerene small molecule acceptors for P3HT-based organic solar cells and green solvent selection205Rational design of organic semiconductors with low internal reorganization energies for hole and electron transport: position effect of aza-substitution in phenalenyl derivatives10Transfer learning with graph neural networks for optoelectronic properties of conjugated oligomers51Molecular design and performance improvement in organic solar cells guided by high‐throughput screening and machine learning22Active discovery of organic semiconductors135Molecular excited states through a machine learning lens200An Informatics Approach for Designing Conducting Polymers29Machine learning to accelerate screening for Marcus reorganization energies27Computational Screening of Chiral Organic Semiconductors: Exploring Side-Group Functionalization and Assembly to Optimize Charge Transport20Charge Transfer Landscape Manifesting Structure-Rate Relationship in the Condensed Phase via Machine Learning1Charge-Transfer Landscape Manifesting the Structure–Rate Relationship in the Condensed Phase Via Machine Learning23Open-Circuit Voltage Loss and Dielectric Constants as New Descriptors in Machine Learning Study on Organic Photovoltaics11Reorganization energies of flexible organic molecules as a challenging target for machine learning enhanced virtual screening29Efficient designing of half-moon-shaped chalcogen heterocycles as non-fullerene acceptors for organic solar cells44Application of Machine Learning in Developing Quantitative Structure–Property Relationship for Electronic Properties of Polyaromatic Compounds10Machine learning for semiconductors40Electronic, redox, and optical property prediction of organic π-conjugated molecules through a hierarchy of machine learning approaches44The Effect of Alkyl Substitutes on the Characteristics of Charge Transfer in Stacks of D–π–A–π–D Molecules2Classification of crystal structures of thiophene-containing organic semiconductors13Frame Design Based on Machine Learning Sports Result Prediction0Machine learning of atomic force microscopy images of organic solar cells20Machine learning based charge mobility prediction for organic semiconductors26Judicious training pattern for superior molecular reorganization energy prediction model11Reorganization Energy Predictions with Graph Neural Networks Informed by Low-Cost Conformers14Computational Approaches for Organic Semiconductors: From Chemical and Physical Understanding to Predicting New Materials102Quantum Chemistry–Machine Learning Approach for Predicting Properties of Lewis Acid–Lewis Base Adducts6An Intelligent, User‐Inclusive Pipeline for Organic Semiconductor Design9Transfer learning for predicting reorganization energy11Dynamic aggregation of carbon dots self‐stabilizes symmetry breaking for exceptional hydrogen production with near‐infrared light47How good can a simple artificial neural network predict the medium reorganization energy and the free energy gap from a steady-state fluorescence spectrum?3Atom-Based Machine Learning Model for Quantitative Property–Structure Relationship of Electronic Properties of Fusenes and Substituted Fusenes6Machine learning approaches for screening of materials in flexible electronic devices0Artificial Intelligence for Conjugated Polymers39Tunable Properties of New Cyano‐Substituent Heptahelicenes for Optoelectronic Devices: A Combined Experimental and DFT Computational Study4Data-Quality-Navigated Machine Learning Strategy with Chemical Intuition to Improve Generalization13Accelerating the discovery of high-mobility molecular semiconductors: a machine learning approach8Introduction to Predicting Properties of Organic Materials1Kernel regression methods for prediction of materials properties: Recent developments22A rapid UV/Vis assisted designing of benzodithiophene based polymers by machine learning to predict their light absorption for photovoltaics6Advances in the Design and Discovery of Organic Semiconductors Aided by Machine Learning18Predicting electronic properties of π-conjugated organic polymers using machine learning for optoelectronic applications0Machine‐Learning‐Based Design of Organic Chromophores with Improved Photovoltaic Efficiency2A priority control list for LCMs in freshwater food chain by deep learning0A 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 review3Learning the limits: how data, diversity, and representation control machine-learning predictions of reorganisation energy2Data-Driven Design of Organic Semiconductors Exhibiting Low Reorganization Energy via Hierarchical Variational Autoencoders, Gaussian Mixture Regression, and Bayesian Optimization0Informatics-Guided Computational Design of Polyaromatic Hydrocarbons Suitable for Use as Hole Transport Materials1

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