[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Lee, 2019 - Google Patents

Insights from machine learning techniques for predicting the efficiency of fullerene derivatives‐based ternary organic solar cells at ternary blend design

Lee, 2019

Document ID
14919573585870914671
Author
Lee M
Publication year
Publication venue
Advanced Energy Materials

External Links

Snippet

Ternary organic solar cells (OSCs) have progressed significantly in recent years due to the sufficient photon harvesting of the blend photoactive layer including three absorption‐ complementary materials. With the rapid development of highly efficient ternary OSCs in …
Continue reading at advanced.onlinelibrary.wiley.com (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6267Classification techniques
    • G06K9/6268Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6267Classification techniques
    • G06K9/6279Classification techniques relating to the number of classes
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • G06N5/02Knowledge representation
    • G06N5/022Knowledge engineering, knowledge acquisition
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor

Similar Documents

Publication Publication Date Title
Lee Insights from machine learning techniques for predicting the efficiency of fullerene derivatives‐based ternary organic solar cells at ternary blend design
Wu et al. A PID-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis
Omitaomu et al. Artificial intelligence techniques in smart grid: A survey
Sun et al. Sugar: Subgraph neural network with reinforcement pooling and self-supervised mutual information mechanism
Pokuri et al. Interpretable deep learning for guided microstructure-property explorations in photovoltaics
Majeed et al. Using deep machine learning to understand the physical performance bottlenecks in novel thin‐film solar cells
Parker et al. Selecting appropriate clustering methods for materials science applications of machine learning
Liu et al. A novel and quick SVM-based multi-class classifier
Paul et al. Property prediction of organic donor molecules for photovoltaic applications using extremely randomized trees
Lee A Machine Learning–Based Design Rule for Improved Open‐Circuit Voltage in Ternary Organic Solar Cells
Du et al. Microstructure design using graphs
Goswami et al. Artificial intelligence in material engineering: A review on applications of artificial intelligence in material engineering
Lu et al. Recent progress in the data-driven discovery of novel photovoltaic materials
Zhang et al. Combined machine learning and high-throughput calculations predict heyd–scuseria–ernzerhof band gap of 2D materials and potential MoSi2N4 heterostructures
Wu et al. Parameter identification of single-phase inverter based on improved moth flame optimization algorithm
Gupta et al. Evolution of artificial intelligence for application in contemporary materials science
Obada et al. Explainable machine learning for predicting the band gaps of ABX3 perovskites
Li et al. Performance prediction and optimization of perovskite solar cells based on the Bayesian approach
Qi et al. Incorporating adaptability-related knowledge into support vector machine for case-based design adaptation
Zhang et al. Machine Learning for Screening Small Molecules as Passivation Materials for Enhanced Perovskite Solar Cells
Zuo et al. Exploring graph capsual network and graphormer for graph classification
Zhang et al. Graph self-supervised learning for optoelectronic properties of organic semiconductors
Borvick et al. Process-function data mining for the discovery of solid-state iron-oxide PV
Yu et al. Graph classification based on sparse graph feature selection and extreme learning machine
Yosipof et al. Visualization based data mining for comparison between two solar cell libraries