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RESEARCH PAPER · The Journal of Physical Chemistry A · 2018

Machine-Learning Energy Gaps of Porphyrins with Molecular Graph Representations

Zheng Li, Noushin Omidvar, Wei Shan Chin, Esther Robb, Amanda J. Morris, Luke E. K. Achenie, Hongliang Xin

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Machine-Learning Energy Gaps of Porphyrins with Molecular Graph RepresentationsOriginalStrategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry159Systematic Study of the Effect of Auxiliary Acceptors in D–A′−π–A Sensitizers Used on Dye-Sensitized Solar Cells21Machine learning for renewable energy materials312Making Machine Learning Work in Chemistry: Methodological Innovation, Software Development, and Application Studies3Advances of machine learning in molecular modeling and simulation128Machine-Learning Based Stacked Ensemble Model for Accurate Analysis of Molecular Dynamics Simulations34Development of an Accurate Coarse-Grained Model of Poly(acrylic acid) in Explicit Solvents28Enumeration of de novo inorganic complexes for chemical discovery and machine learning42Toward a Design of Active Oxygen Evolution Catalysts: Insights from Automated Density Functional Theory Calculations and Machine Learning178Machine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal–Oxo Intermediate Formation90Machine Learning for Predicting Electron Transfer Coupling65Machine Learning (ML)‐Assisted Design and Fabrication for Solar Cells99Predicting aggregation energy for single atom bimetallic catalysts on clean and O* adsorbed surfaces through machine learning models48A review of mathematical representations of biomolecular data98Efficient photocatalysts of a tetraphenylporphyrin/P25 hybrid for visible-light photoreduction of CO 24Machine learning approach for accurate backmapping of coarse-grained models to all-atom models55Machine learning the computational cost of quantum chemistry41An Adaptive Machine Learning Strategy for Accelerating Discovery of Perovskite Electrocatalysts135Amorphous Catalysis: Machine Learning Driven High-Throughput Screening of Superior Active Site for Hydrogen Evolution Reaction65Exploiting Machine Learning to Efficiently Predict Multidimensional Optical Spectra in Complex Environments47Predicting oxidation and spin states by high-dimensional neural networks: Applications to lithium manganese oxide spinels41Review of Molecular Modeling and Photoelectronic Applications of Porphyrin-based Materials0Artificial neural networks for predicting charge transfer coupling50Calculation of frontier orbitals, electronic coupling and paramagnetic interaction of conjugated porphyrin arrays based on DFT theory0Data-driven materials discovery for solar photovoltaics0Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery55Catalyze Materials Science with Machine Learning67The donor–acceptor dyad based on high substituted fullero[70]pyrrolidine-coordinated manganese (III) phthalocyanine for photoinduced electron transfer4Key factors governing the device performance of CIGS solar cells: Insights from machine learning32Machine learning for multiscale modeling in computational molecular design25Machine Learning Predictor Models in the Electronic Properties of Alkanes based on Degree-Topology Indices29Machine Learning-Assisted Development of Organic Solar Cell Materials: Issues, Analyses, and Outlooks98Design of dyes for energy transformation: From the interaction with biological systems to application in solar cells2Key Factors Governing the External Quantum Efficiency of Thermally Activated Delayed Fluorescence Organic Light-Emitting Devices: Evidence from Machine Learning38Data‐Driven Materials Innovation and Applications187New Strategies for Direct Methane-to-Methanol Conversion from Active Learning Exploration of 16 Million Catalysts74Meso-carbazole substituted porphyrin complexes: Synthesis and spectral properties according to experiment, DFT calculations and the prediction by machine learning methods20Exploring the potential of in silico machine learning tools for the prediction of acute Daphnia magna nanotoxicity32Machine learning the frontier orbital energies of SubPc based triads4Exploring Deep Learning for Metalloporphyrins: Databases, Molecular Representations, and Model Architectures7Machine Learning Frontier Orbital Energies of Nanodiamonds8Counting Polynomials in Chemistry: Past, Present, and Perspectives3Identifying Molecular Structure–Energy Level Quantitative Relationship of Thermally Activated Delayed Fluorescence Materials Using Machine Learning11Supervised Machine Learning-Graph Theory Approach For Analyzing the Electronic Properties of Alkanes35‐3: Predicting External Quantum Efficiency of Red Phosphorescent Organic Light‐Emitting Devices by Machine Learning1A Simple and Promising Prediction Model to Analyze the Optical Properties of Organic Photovoltaic Materials3Machine learning-enabled discovery of multi-resonance TADF molecules: Unraveling PLQY predictions from molecular structures21Transfer learning across different chemical domains: virtual screening of organic materials with deep learning models pretrained on small molecule and chemical reaction data18Insights into nanoparticle toxicity against aquatic organisms using multivariate regression, read-across, and ML algorithms: Predictive models for Daphnia magna and Danio rerio8MAG-SOLex Molecular Representation: A Methodology for Handling Complex Molecules in Algorithms1Logistic regression exploration of host–dopant compatibility in MR-TADF-based OLEDs0A Modular Platform for Porphyrin Molecular Design and UV–Vis–NIR Spectral Prediction0Enhancing Triplet Properties of Phthalocyanines with Heavy Atoms: Iodine Substitution or Palladium Complexation?0

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