[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 onlinelibrary.wiley.com (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6251—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on a criterion of topology preservation, e.g. multidimensional scaling, self-organising maps
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6279—Classification techniques relating to the number of classes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46—Extraction of features or characteristics of the image

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
Sun et al. Sugar: Subgraph neural network with reinforcement pooling and self-supervised mutual information mechanism
Omitaomu et al. Artificial intelligence techniques in smart grid: A survey
Pokuri et al. Interpretable deep learning for guided microstructure-property explorations in photovoltaics
Liu et al. A novel and quick SVM-based multi-class classifier
Tagade et al. Attribute driven inverse materials design using deep learning Bayesian framework
Paul et al. Property prediction of organic donor molecules for photovoltaic applications using extremely randomized trees
Lee Identifying correlation between the open-circuit voltage and the frontier orbital energies of non-fullerene organic solar cells based on interpretable machine-learning approaches
Du et al. Microstructure design using graphs
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
Wang et al. Graph mixture of experts: Learning on large-scale graphs with explicit diversity modeling
Ren et al. Pre-fault dynamic security assessment of power systems for multiple different faults via multi-label learning
Li et al. Performance prediction and optimization of perovskite solar cells based on the Bayesian approach
Gupta et al. Evolution of artificial intelligence for application in contemporary materials science
Qi et al. Incorporating adaptability-related knowledge into support vector machine for case-based design adaptation
Farhadi et al. Machine learning for fast development of advanced energy materials
Zuo et al. Exploring graph capsual network and graphormer for graph classification
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
Zhang et al. Machine Learning for Screening Small Molecules as Passivation Materials for Enhanced Perovskite Solar Cells
Yang et al. A dynamic global backbone updating for communication-efficient personalised federated learning
Armitage et al. Fragment graphical variational autoencoding for screening molecules with small data
CN114298854B (en) Weak supervision user identity linking method combining learning representation and alignment
Liu et al. Symmetry-constrained non-negative matrix factorization approach for highly-accurate community detection