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RESEARCH PAPER · Nature Communications · 2020

Designing and understanding light-harvesting devices with machine learning

Florian Häse, Loı̈c M. Roch, Pascal Friederich, Alán Aspuru‐Guzik

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Designing and understanding light-harvesting devices with machine learningOriginalComputation sparks chemical discovery3A Regional Frequency Analysis of Tide Gauges to Assess Pacific Coast Flood Risk24Role of Napping for Learning Across the Lifespan12Machine Learning for Electronically Excited States of Molecules398Extreme sea level variability dominates coastal flood risk changes at decadal time scales15Activation for Newly Learned Words in Left Medial-Temporal Lobe During Toddlers’ Sleep is Associated with Word Learning0Beyond generative models: superfast traversal, optimization, novelty, exploration and discovery (STONED) algorithm for molecules using SELFIES165Machine learning dielectric screening for the simulation of excited state properties of molecules and materials28Efficient enumeration of bosonic configurations with applications to the calculation of non-radiative rates9Experiment‐Oriented Machine Learning of Polymer:Non‐Fullerene Organic Solar Cells94Sleep and human cognitive development274Unsupervised Learning Methods for Molecular Simulation Data402Using molecular dynamics simulations to prioritize and understand AI-generated cell penetrating peptides39Scalable Estimator of the Diversity for De Novo Molecular Generation Resulting in a More Robust QM Dataset (OD9) and a More Efficient Molecular Optimization.0Multi‐Fidelity High‐Throughput Optimization of Electrical Conductivity in P3HT‐CNT Composites58Development of the N400 for Word Learning in the First 2 Years of Life: A Systematic Review41Structural Analysis of Nanoscale Network Materials Using Graph Theory75Machine Learning Predictions of Drug Release from Polymeric Long Acting Injectables5Scalable estimator of the diversity for de novo molecular generation resulting in a more robust QM dataset (OD9) and a more efficient molecular optimization12Nap timing makes a difference: Sleeping sooner rather than later after learning improves infants’ locomotor problem solving13Picroscope: low-cost system for simultaneous longitudinal biological imaging35Application of computational approach in plastic pyrolysis kinetic modelling: a review41Charge Transfer Landscape Manifesting Structure-Rate Relationship in the Condensed Phase via Machine Learning1Concordance between subjective and objective measures of infant sleep varies by age and maternal mood: Implications for studies of sleep and cognitive development21Charge-Transfer Landscape Manifesting the Structure–Rate Relationship in the Condensed Phase Via Machine Learning23Heuristic and Meta Dendral Systems: A Review1Machine Learning-Assisted Development of Organic Solar Cell Materials: Issues, Analyses, and Outlooks98Data-driven modeling of S0 → S1 excitation energy in the BODIPY chemical space: High-throughput computation, quantum machine learning, and inverse design.19High-throughput oxygen chemical potential engineering of perovskite oxides for chemical looping applications110Identifying structure–absorption relationships and predicting absorption strength of non-fullerene acceptors for organic photovoltaics59Dimensional heterojunction design: The rising star of 2D bismuth-based nanostructured photocatalysts for solar-to-chemical conversion57The atomistic modeling of light-harvesting complexes from the physical models to the computational protocol62Accelerating the discovery of high-performance donor/acceptor pairs in photovoltaic materials via machine learning and density functional theory23Performance Prediction and Experimental Optimization Assisted by Machine Learning for Organic Photovoltaics30Predicting the future of excitation energy transfer in light-harvesting complex with artificial intelligence-based quantum dynamics51Computational evolution of high-performing unfused non-fullerene acceptors for organic solar cells19Organic Photovoltaic Efficiency Predictor: Data-Driven Models for Non-Fullerene Acceptor Organic Solar Cells37Evaluation guidelines for machine learning tools in the chemical sciences180Predicting Materials Parameters in Colloidal Quantum Dot Photovoltaic Devices Using Machine Learning Models Trained On Experimental Data02D conjugated polymers: exploiting topological properties for the rational design of metal-free photocatalysts31Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery552Improved Predictions of Organic Photovoltaic Performance through Machine Learning Models Empowered by Artificially Generated Failure Data27The principles, design and applications of fused-ring electron acceptors454Automatic Evolution of Machine-Learning-Based Quantum Dynamics with Uncertainty Analysis29Recent advances in machine learning for electronic excited state molecular dynamics simulations2Self-assembled supramolecular artificial light-harvesting nanosystems: construction, modulation, and applications102Alleviating Drought Stress Effects on Photosynthetic Productivity of Larix Decidua Through Physiological Process by Management Measures0Artificial Intelligence in Formulation and Product Design of BCS Class I and II Drugs4Machine learning in energy chemistry: introduction, challenges and perspectives31Process Insights into Perovskite Thin‐Film Photovoltaics from Machine Learning with In Situ Luminescence Data27Analysis and evaluation of machine learning applications in materials design and discovery33An Expedited Route to Optical and Electronic Properties at Finite Temperature via Unsupervised Learning23A review on the application of machine learning in production of woody biomass from natural and planted forests2TimeSOAP: Tracking high-dimensional fluctuations in complex molecular systems via time variations of SOAP spectra26An evolutionary-driven AI model discovering redox-stable organic electrode materials for alkali-ion batteries20A review on the applications of machine learning and deep learning in agriculture section for the production of crop biomass raw materials11Revolutionizing Low‐Cost Solar Cells with Machine Learning: A Systematic Review of Optimization Techniques35Efficient modeling of double absorber layered structure in perovskite solar cells using machine learning techniques18Tunable Band Engineering Management on Perovskite MAPbBr3/COFs Nano‐Heterostructures for Efficient S–S Coupling Reactions11Machine learning for sustainable reutilization of waste materials as energy sources – a comprehensive review9The emergence of quantum energy science11Exploration of Solution-Processed Bi/Sb Solar Cells by Automated Robotic Experiments Equipped with Microwave Conductivity18AI-driven robotic chemist for autonomous synthesis of organic molecules108Machine Learning-Based Screening for Potential Singlet Fission Chromophores: The Challenge of Imbalanced Data Sets11A Comprehensive Survey of Machine Learning Methodologies with Emphasis in Water Resources Management142Light Harvesting Modulation of Melanin-Like Polymers via Thiol-Michael “Click” Chemistry25Overview: Photovoltaic Solar Cells, Science, Materials, Artificial Intelligence, Nanotechnology and State of the Art12Nature-inspired and computer-aided approaches to enable improved photovoltaic materials, more efficient processing, and novel devices0Workflow for practical quantum chemical calculations with a quantum phase estimation algorithm: electronic ground and π–π* excited states of benzene and its derivatives10Optimizing biodiesel production from waste with computational chemistry, machine learning and policy insights: a review124Chemical physics behind Tetraphenylporphyrin-PC70BM interaction in solution: Charge transfer, non-covalent binding and photoactive properties7Learning from machine learning: the case of band-gap directness in semiconductors9Spectroscopic and quantum chemical insights on supramolecular interaction between N-methylfulleropyrrolidine and designed porphyrins in solution6Exploring the chemical physics in tetraphenylporphyrin-N-methylfulleropyrrolidine supramolecular complex by spectroscopic investigations and quantum chemical calculations6In Silico Chemical Experiments in the Age of AI: From Quantum Chemistry to Machine Learning and Back78Machine learning approaches for assessing rechargeable battery state-of-charge in unmanned aircraft vehicle-eVTOL10Exploring KGeCl3 material for perovskite solar cell absorber layer through different machine learning models31Bioinspired Photonic Systems Directed by Designer DNA Nanostructures11A review on the algae-derived biochar catalysts:Advanced oxidation processes and machine learning tools22Predicting PbS Colloidal Quantum Dot Solar Cell Parameters Using Neural Networks Trained on Experimental Data6Toward Artificial Photosynthetic Assemblies Inspired by Photosynthetic Bacteria7Lanthanide-Metal-Doped Light-Harvesting Quantum Dots for Exceptional X-ray Imaging Scintillators17Advanced Computational Techniques for Optimizing Manganese-Based Perovskite Solar Cells: From SCAPS-1D Simulations to Machine Learning Predictions34Predicting electronic screening for fast Koopmans spectral functional calculations4Design of simple-structured conjugated polymers for organic solar cells by machine learning-assisted structural modification and experimental validation2Machine Learning for Custom-Designed Light Emitters in Biomedicine15Artificial intelligence interventions in 2D MXenes-based photocatalytic applications38Designing and optimization of a highly efficient and new lead-free Cs2RbGaI6 based double perovskite solar cell through SCAPS-1D and machine learning29Bioinspired Deformable Antireflective Materials by Block Copolymer Self-Assembly7Pyrene-Based Triphenylamine Light-Harvesting Materials with Related Structure–Property Relationships10State-of-charge estimation and prediction by machine learning models using experimental dataset of lithium-ion batteries based on ionic liquid modified LiFSI electrolyte2Advanced intelligent recognition algorithm for tree canopy carbon sequestration capacity based on 3D visualization: application in Larch plantations under drought stress2AI-driven solutions in wastewater treatment and agricultural reuse systems: A comprehensive review23Systematic review and topic classification of soft computing and machine learning in water resources management6AI‐Driven Advances in Sustainable Materials for Green Energy: From Innovation to Lifecycle Management31Advances on biochar applications for organic wastewater Treatment: Material design, removal mechanisms, innovative machine learning and challenges16Accelerating the discovery of high-efficiency donor–acceptor pairs in organic photovoltaics via SolarPCE-Net guided screening2A rotational wind energy harvester based on the centrifugal mechanism for self-powered sensing4Machine Learning for Accelerating Energy Materials Discovery: Bridging Quantum Accuracy with Computational Efficiency44The Overtones of the Carotenoid Vibrational Modes Significantly Improve the Quality of Optical Response Modeling0Dissipation Pathways in a Photosynthetic Complex3Application of artificial intelligence tools in development of biochar-based photocatalysts for removal of emerging environmental contaminants2Quantum Biology Using Ultrafast Integrative and Molecular Engineering Tools: A Perspective0General framework for quantifying dissipation pathways in open quantum systems. III. Off-diagonal subsystem–bath couplings3Design concept and solar-to-energy applications of CuPbSbS3 from photovoltaics to photocatalysis8Design Concept of Metal Sulfide Photocatalyst for Efficient Photocatalytic Hydrogen Evolution31Accelerating quasiparticle physics: a review on the synergy of machine learning and density functional theory for excitons, polaritons, and plasmons0Active learning and explainable machine intelligence for lead-free chalcogenide perovskite photovoltaics: Gaussian process Bayesian optimization and SHAP interpretability applied to CaZrSe 3 /CdZnS solar cells0Photocatalytic hydrogen evolution for diabetic wound healing: mechanisms, materials, and therapeutic prospects0Cu-Enabled Photoinduced Charge Transfer in Green-Processed Cu-BPDC Metal–Organic Framework Films on FTO0

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