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Effect of Increasing the Descriptor Set on Machine Learning Prediction of Small Molecule-Based Organic Solar Cells

https://doi.org/10.1021/acs.chemmater.0c02325
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inferred← citeshttps://doi.org/10.2139/ssrn.4098717openalex-extracted
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inferred← citeshttps://doi.org/10.1002/ange.202213953openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/anie.202213953openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s00894-025-06514-5openalex-snapshot
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Machine learning for high performance organic solar cells: current scenario and future prospects
inferred← citeshttps://doi.org/10.1039/d1dt02722kopenalex-snapshot
De novo synthesis of hybrid d–f block metal complex salts for electronic charge transport applications
inferred← citeshttps://doi.org/10.1039/d1nj05498hopenalex-snapshot
Data-driven approach towards identifying dyesensitizer molecules for higher power conversion efficiency in solar cells
inferred← citeshttps://doi.org/10.1039/d1ta04742fopenalex-snapshot
A time and resource efficient machine learning assisted design of non-fullerene small molecule acceptors for P3HT-based organic solar cells and green solvent selection
inferred← citeshttps://doi.org/10.1039/d1tc03256aopenalex-snapshot
High-throughput virtual screening for organic electronics: a comparative study of alternative strategies
inferred← citeshttps://doi.org/10.1039/d2dd00004kopenalex-snapshot
Limitations of machine learning models when predicting compounds with completely new chemistries: possible improvements applied to the discovery of new non-fullerene acceptors
inferred← citeshttps://doi.org/10.1039/d2tc03276gopenalex-snapshot
Opportunities and challenges for machine learning to select combination of donor and acceptor materials for efficient organic solar cells
inferred← citeshttps://doi.org/10.1039/d3cp00917copenalex-snapshot
Deep transfer learning for predicting frontier orbital energies of organic materials using small data and its application to porphyrin photocatalysts
inferred← citeshttps://doi.org/10.1039/d3ta04603fopenalex-snapshot
Machine-learning-guided prediction of photovoltaic performance of non-fullerene organic solar cells using novel molecular and structural descriptors
inferred← citeshttps://doi.org/10.1039/d3ya00057eopenalex-snapshot
Machine learning in energy chemistry: introduction, challenges and perspectives
inferred← citeshttps://doi.org/10.1039/d4ta01942copenalex-snapshot
Beyond molecular structure: critically assessing machine learning for designing organic photovoltaic materials and devices
inferred← citeshttps://doi.org/10.1039/d4ta02059fopenalex-snapshot
Two-dimensional carbon/boron nitrides: modification, machine learning and beyond
inferred← citeshttps://doi.org/10.1039/d4ta03944kopenalex-snapshot
Accelerating the generation and discovery of high-performance donor materials for organic solar cells by deep learning
inferred← citeshttps://doi.org/10.1039/d5dd00496aopenalex-snapshot
Structure-guided machine learning for efficiency prediction of organic photovoltaics using experimentally informed molecular descriptors
inferred← citeshttps://doi.org/10.1039/d5dd00552copenalex-snapshot
Artificial intelligence in the discovery and design of molecular semiconductors: a systematic review
inferred← citeshttps://doi.org/10.1039/d5me00182jopenalex-snapshot
From fragments to function: data-driven design of high-performance non-fullerene acceptors for organic photovoltaics
inferred← citeshttps://doi.org/10.1039/d5ta01529dopenalex-snapshot
BiBERTa: a self-supervised framework for accelerating the discovery of stable organic photovoltaic materials
inferred← citeshttps://doi.org/10.1039/d5tc00111kopenalex-snapshot
Deep learning accelerated high-throughput screening of organic solar cells
inferred← citeshttps://doi.org/10.1039/d5tc00931fopenalex-snapshot
High throughput molecular design of electron donors and non-fullerene acceptors using machine learning combined with substructure importance
inferred← citeshttps://doi.org/10.1039/d5tc02014jopenalex-snapshot
Molecular design of organic photovoltaic donors and non-fullerene acceptors: a combined machine learning and genetic algorithm approach
inferred← citeshttps://doi.org/10.1039/d6ra03997aopenalex-snapshot
Decoding non-fullerene acceptors for organic solar cells: a theoretical structure–property–performance perspective
inferred← citeshttps://doi.org/10.1063/5.0048164openalex-snapshot
G ryffin : An algorithm for Bayesian optimization of categorical variables informed by expert knowledge
inferred← citeshttps://doi.org/10.1063/5.0087299openalex-snapshot
Computational evolution of high-performing unfused non-fullerene acceptors for organic solar cells
inferred← citeshttps://doi.org/10.1063/5.0169284openalex-snapshot
Machine learning study on organic solar cells and virtual screening of designed non-fullerene acceptors
inferred← citeshttps://doi.org/10.1063/5.0214754openalex-snapshot
Transfer learning for molecular property predictions from small datasets
inferred← citeshttps://doi.org/10.1063/5.0242118openalex-snapshot
Kernel regression methods for prediction of materials properties: Recent developments
inferred← citeshttps://doi.org/10.1088/1361-6668/ac95d6openalex-snapshot
Artificial intelligence-based models for reconstructing the critical current and index-value surfaces of HTS tapes
inferred← citeshttps://doi.org/10.1088/2516-1075/ac884dopenalex-snapshot
Interface energetics make devices
inferred← citeshttps://doi.org/10.1109/jphotov.2025.3592683openalex-snapshot
Uncovering Structure–Performance Relationships in Organic Photovoltaics: Interpretable Machine Learning Model for Predicting the Power Conversion Efficiency
inferred← citeshttps://doi.org/10.1111/jace.19343openalex-snapshot
Review and perspective on polyhedron model for estimating thermodynamic properties of oxides
inferred← citeshttps://doi.org/10.1146/annurev-chembioeng-092220-024340openalex-snapshot
Machine Learning–Assisted Design of Material Properties
inferred← citeshttps://doi.org/10.20517/jmi.2022.07openalex-snapshot
Recent progress in the data-driven discovery of novel photovoltaic materials
inferred← citeshttps://doi.org/10.21203/rs.3.rs-10968603/v1openalex-snapshot
A Structure-Based Machine Learning Framework for Polymer Solar Cells: Prediction, Interpretation, and Virtual Screening
inferred← citeshttps://doi.org/10.26434/chemrxiv-2023-zj3tvopenalex-snapshot
Comparative Analyses of Data Driven Machine Learning Models for TADF Emitters
inferred← citeshttps://doi.org/10.3390/pr13020393openalex-snapshot
A Review of Machine Learning in Organic Solar Cells
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.