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RESEARCH PAPER · Advanced Materials · 2020

Machine Learning Predictions of Block Copolymer Self‐Assembly

Kun‐Hua Tu, H. H. HUANG, Sangho Lee, Wonmoo Lee, Zehao Sun, Alfredo Alexander‐Katz, Caroline Anne Ross

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Machine Learning Predictions of Block Copolymer Self‐AssemblyOriginalMachine learning analysis and prediction models of alkaline anion exchange membranes for fuel cells70Autonomous Construction of Phase Diagrams of Block Copolymers by Theory-Assisted Active Machine Learning57Machine learning-assisted systematical polymerization planning: case studies on reversible-deactivation radical polymerization35Efficient tracking and estimation of solvent chamber development during warm solvent injection in heterogeneous reservoirs via machine learning9Global Inverse Design across Multiple Photonic Structure Classes Using Generative Deep Learning76Block copolymer thin films94A simple route to prepare supramolecular block copolymers using telechelic polystyrene/polydimethylsiloxane pairs10Metallic Nanomeshes Fabricated by Multimechanism Directed Self-Assembly23Bayesian Machine Learning for Efficient Minimization of Defects in ALD Passivation Layers30A Machine Learning Method for Material Property Prediction: Example Polymer Compatibility1Machine Learning Strategies for the Structure-Property Relationship of Copolymers51Machine Learning Guided Synthesis of Flash Graphene110Integration of Machine Learning and Coarse-Grained Molecular Simulations for Polymer Materials: Physical Understandings and Molecular Design59Machine Learning at the Interface of Polymer Science and Biology: How Far Can We Go?21Artificial intelligence to bring nanomedicine to life189Studying Complex Evolution of Hyperelastic Materials under External Field Stimuli using Artificial Neural Networks with Spatiotemporal Features in a Small‐Scale Dataset35Evaluation guidelines for machine learning tools in the chemical sciences180Machine-learning exploration of polymer compatibility39Using Machine Learning to Predict and Understand Complex Self‐Assembly Behaviors of a Multicomponent Nanocomposite39Employing Artificial Neural Networks to Identify Reaction Coordinates and Pathways for Self-Assembly10Fatigue life prediction of bending polymer films using random forest68Understanding and Modeling Polymers: The Challenge of Multiple Scales110Inferring the Physics of Structural Evolution of Multicomponent Polymers via Machine-Learning-Accelerated Method2Artificial intelligence in accelerating the development of air electrode materials for proton conducting solid oxide cells0Visualization of Deep Convolutional Neural Networks to Investigate Porous Nanocomposites for Electromagnetic Interference Shielding16Amalgamation of Artificial Intelligence with Nanoscience for Biomedical Applications36Confined micro-environment of micelles regulates their interaction with anions1Template Design for Complex Block Copolymer Patterns Using a Machine Learning Method8Reversible Morphology Locking via Metal Infiltration in a Block Copolymer10Seeded RAFT Polymerization‐Induced Self‐assembly: Recent Advances and Future Opportunities13Functional composites by programming entropy-driven nanosheet growth40Topological structure-induced self-assembly of highly asymmetric PLLA-b-QPDMAEMA block copolymers in solution: Linear versus bottlebrush3Machine‐Learning Assisted Screening Proton Conducting Co/Fe based Oxide for the Air Electrode of Protonic Solid Oxide Cell44Synergistic D‐Amino Acids Based Antimicrobial Cocktails Formulated via High‐Throughput Screening and Machine Learning22Advances and challenges in high-performance cathodes for protonic solid oxide fuel cells and machine learning-guided perspectives57Identification of D- and L-phenylalanine enantiomeric mixtures by employing deep neural network models3The triumvirate of effective and rapid synthesis, analysis, and artificial intelligence to explore the structure-property relationship of copolymers6Evolution of artificial intelligence as a modern technology in advanced cancer therapy30Design and Fabrication of Nano‐Particles with Customized Properties using Self‐Assembly of Block‐Copolymers9Damaging effect of fine grinding treatment on the microstructure of polyurea elastomer modifier used in asphalt binder73Transcend the boundaries: Machine learning for designing polymeric membrane materials for gas separation11Review of bioderived and biodegradable polymers/block-copolymers and their biomedical and electronic applications17Predicting homopolymer and copolymer solubility through machine learning10Machine Learning Applications in Polymer Informatics—An Overview1Morphology prediction for polymer blend thin films using machine learning9Recent advances in perovskite air electrode materials for protonic solid oxide electrochemical cells52The evolution of ABC star polymers: from trial-and-error to rational design5Machine Learning Approaches in Polymer Science: Progress and Fundamental for a New Paradigm86Self-assembly of architected macromolecules: Bridging a gap between experiments and simulations7Machine‐Learning‐Enhanced Trial‐and‐Error for Efficient Optimization of Rubber Composites29Machine Learning‐Enhanced Nanoparticle Design for Precision Cancer Drug Delivery60Enabling data-driven design of block copolymer self-assembly7Role of Artificial Intelligence in Nanomedicine and Organ-specific Therapy: An Updated Review4AI-Empowered Electrochemical Sensors for Biomedical Applications: Technological Advances and Future Challenges51Bayesian Optimization of Colloidal Monolayer Formation in Langmuir–Blodgett System2Self-assembling nanocomposites for smart drug delivery: towards personalized and stimuli-responsive therapeutics16Hybrid Quantum-Classical Inverse Design of Metasurfaces for Tailored High Q-factor Response1Cross-Scale Machine Learning for Polymer Materials: Linking Molecular Structure, Mesoscale Organization, Processing History, and Macroscopic Properties0On‐Stage and Behind‐the‐Scenes: Solvent‐Directed Structural Transformation and Optical Properties of Metal Clusters0

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  2. Autonomous Construction of Phase Diagrams of Block Copolymers by Theory-Assisted Active Machine Learning

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  4. Efficient tracking and estimation of solvent chamber development during warm solvent injection in heterogeneous reservoirs via machine learning

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  8. Metallic Nanomeshes Fabricated by Multimechanism Directed Self-Assembly

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  19. Using Machine Learning to Predict and Understand Complex Self‐Assembly Behaviors of a Multicomponent Nanocomposite

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  20. Employing Artificial Neural Networks to Identify Reaction Coordinates and Pathways for Self-Assembly

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  21. Fatigue life prediction of bending polymer films using random forest

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