Info
- Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.
Artificial Intelligence for Materials Science / / edited by Yuan Cheng, Tian Wang, Gang Zhang
| Artificial Intelligence for Materials Science / / edited by Yuan Cheng, Tian Wang, Gang Zhang |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 |
| Descrizione fisica | 1 online resource (231 pages) |
| Disciplina | 620.110285 |
| Collana | Springer Series in Materials Science |
| Soggetto topico |
Materials science
Machine learning Materials Materials Science Machine Learning Materials Engineering |
| ISBN | 3-030-68310-9 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1. Brief Introduction of the Machine Learning Method -- Chapter 2. Machine learning for high-entropy alloys -- Chapter 3. Two-way TrumpetNets and TubeNets for Identification of Material Parameters -- Chapter 4. Machine learning interatomic force fields for carbon allotropic materials -- Chapter 5. Genetic Algorithms -- Chapter 6. Accelerated Discovery of Thermoelectric Materials using Machine Learning -- Chapter 7. Thermal nanostructure design based on materials informatics. - Chapter 8. Machine Learning Accelerated Insights of Perovskite Materials. |
| Record Nr. | UNINA-9910484237103321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Informatics for materials science and engineering [[electronic resource] ] : data-driven discovery for accelerated experimentation and application / / edited by Krishna Rajan
| Informatics for materials science and engineering [[electronic resource] ] : data-driven discovery for accelerated experimentation and application / / edited by Krishna Rajan |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Oxford, : Butterworth-Heinemann, 2013 |
| Descrizione fisica | 1 online resource (542 p.) |
| Disciplina | 620.110285 |
| Altri autori (Persone) | RajanKrishna |
| Soggetto topico |
Data mining
Materials - Data processing Materials science |
| Soggetto genere / forma | Electronic books. |
| ISBN | 0-12-394614-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front Cover; Informatics for Materials Science and Engineering; Copyright Page; Contents; Preface: A Reading Guide; Acknowledgment; 1. Materials Informatics: An Introduction; 1. The What and Why of Informatics; 2. Learning from Systems Biology: An "Omics" Approach to Mater; 3. Where Do We Get the Information?; 4. Data Mining: Data-Driven Materials Research; References; 2. Data Mining in Materials Science and Engineering; 1. Introduction; 2. Analysis Needs of Science Applications; 3. The Scientific Data-Mining Process; 4. Image Analysis; 5. Dimension Reduction; 5.1 Feature Selection Techniques
Distance Filter Chi-Squared Filter; Stump Filter; Relief; 5.2 Feature Transformation Techniques; Principal Component Analysis (PCA); Isomap; Locally Linear Embedding (LLE); Laplacian Eigenmaps; 5.3 Comparison of Dimension Reduction Methods; 6. Building Predictive and Descriptive Models; 6.1 Classification and Regression; 6.2 Clustering; 7. Further Reading; Acknowledgments; References; 3. Novel Approaches to Statistical Learning in Materials Science; 1. Introduction; 2. The Supervised Binary Classification Learning Problem; 3. Incorporating Side Information; 4. Conformal Prediction 5. Optimal Learning 6. Optimal Uncertainty Quantification; 7. Clustering Including Statistical Physics Approaches; 8. Materials Science Example: The Search for New Piezoelectrics; 9. Conclusion; 10. Further Reading; Acknowledgments; References; 4. Cluster Analysis: Finding Groups in Data; 1. Introduction; 2. Unsupervised Learning; 2.1 Principal Components Analysis; 2.2 Clustering; 3. Different Clustering Algorithms and their Implementations in R; 3.1 Agglomerative Hierarchical; 3.2 K-Means; 3.3 Divisive Hierarchical; 3.4 Partitioning Around Medoids (PAM); 3.5 Fuzzy Analysis (FANNY) 4. Validations of Clustering Results 4.1 Dunn Index; 4.2 Silhouette Width; 4.3 Connectivity; 5. Rank Aggregation of Clustering Results; 6. Further Reading; Acknowledgments; References; 5. Evolutionary Data-Driven Modeling; 1. Preamble; 2. The Concept of Pareto Tradeoff; 3. Evolutionary Neural Net and Pareto Tradeoff; 4. Selecting the Appropriate Model in EvoNN; 5. Conventional Genetic Programming; 6. Bi-Objective Genetic Programming; 6.1 BioGP Code; 7. Analyzing the Variable Response in EvoNN and BioGP; 8. An Application in the Materials Area; 9. Further Reading; References 6. Data Dimensionality Reduction in Materials Science 1. Introduction; 2. Dimensionality Reduction: Basic Ideas and Taxonomy; 3. Dimensionality Reduction Methods: Algorithms, Advantages, and Disadvantages; 3.1 Principal Component Analysis (PCA); PCA Algorithm; 3.2 Isomap; Isomap Algorithm; 3.3 Locally Linear Embedding; LLE Algorithm; 3.4 Hessian LLE; hLLE Algorithm; 4. Dimensionality Estimators; 5. Software; 5.1 Core Functionality; 5.2 User Interface; 6. Analyzing Two Material Science Data Sets: Apatites and Organic Solar Cells; 6.1 Apatite Data; Dimensionality Estimation 6.2 Unraveling Process-Morphology Pathways of Organic Solar Cells using SETDiR |
| Record Nr. | UNINA-9910452778103321 |
| Oxford, : Butterworth-Heinemann, 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Informatics for materials science and engineering : data-driven discovery for accelerated experimentation and application / / edited by Krishna Rajan
| Informatics for materials science and engineering : data-driven discovery for accelerated experimentation and application / / edited by Krishna Rajan |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Oxford, : Butterworth-Heinemann, 2013 |
| Descrizione fisica | 1 online resource (xv, 525 pages) : illustrations (some color) |
| Disciplina | 620.110285 |
| Collana | Gale eBooks |
| Soggetto topico | Materials science - Data processing |
| ISBN | 0-12-394614-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front Cover; Informatics for Materials Science and Engineering; Copyright Page; Contents; Preface: A Reading Guide; Acknowledgment; 1. Materials Informatics: An Introduction; 1. The What and Why of Informatics; 2. Learning from Systems Biology: An "Omics" Approach to Mater; 3. Where Do We Get the Information?; 4. Data Mining: Data-Driven Materials Research; References; 2. Data Mining in Materials Science and Engineering; 1. Introduction; 2. Analysis Needs of Science Applications; 3. The Scientific Data-Mining Process; 4. Image Analysis; 5. Dimension Reduction; 5.1 Feature Selection Techniques
Distance Filter Chi-Squared Filter; Stump Filter; Relief; 5.2 Feature Transformation Techniques; Principal Component Analysis (PCA); Isomap; Locally Linear Embedding (LLE); Laplacian Eigenmaps; 5.3 Comparison of Dimension Reduction Methods; 6. Building Predictive and Descriptive Models; 6.1 Classification and Regression; 6.2 Clustering; 7. Further Reading; Acknowledgments; References; 3. Novel Approaches to Statistical Learning in Materials Science; 1. Introduction; 2. The Supervised Binary Classification Learning Problem; 3. Incorporating Side Information; 4. Conformal Prediction 5. Optimal Learning 6. Optimal Uncertainty Quantification; 7. Clustering Including Statistical Physics Approaches; 8. Materials Science Example: The Search for New Piezoelectrics; 9. Conclusion; 10. Further Reading; Acknowledgments; References; 4. Cluster Analysis: Finding Groups in Data; 1. Introduction; 2. Unsupervised Learning; 2.1 Principal Components Analysis; 2.2 Clustering; 3. Different Clustering Algorithms and their Implementations in R; 3.1 Agglomerative Hierarchical; 3.2 K-Means; 3.3 Divisive Hierarchical; 3.4 Partitioning Around Medoids (PAM); 3.5 Fuzzy Analysis (FANNY) 4. Validations of Clustering Results 4.1 Dunn Index; 4.2 Silhouette Width; 4.3 Connectivity; 5. Rank Aggregation of Clustering Results; 6. Further Reading; Acknowledgments; References; 5. Evolutionary Data-Driven Modeling; 1. Preamble; 2. The Concept of Pareto Tradeoff; 3. Evolutionary Neural Net and Pareto Tradeoff; 4. Selecting the Appropriate Model in EvoNN; 5. Conventional Genetic Programming; 6. Bi-Objective Genetic Programming; 6.1 BioGP Code; 7. Analyzing the Variable Response in EvoNN and BioGP; 8. An Application in the Materials Area; 9. Further Reading; References 6. Data Dimensionality Reduction in Materials Science 1. Introduction; 2. Dimensionality Reduction: Basic Ideas and Taxonomy; 3. Dimensionality Reduction Methods: Algorithms, Advantages, and Disadvantages; 3.1 Principal Component Analysis (PCA); PCA Algorithm; 3.2 Isomap; Isomap Algorithm; 3.3 Locally Linear Embedding; LLE Algorithm; 3.4 Hessian LLE; hLLE Algorithm; 4. Dimensionality Estimators; 5. Software; 5.1 Core Functionality; 5.2 User Interface; 6. Analyzing Two Material Science Data Sets: Apatites and Organic Solar Cells; 6.1 Apatite Data; Dimensionality Estimation 6.2 Unraveling Process-Morphology Pathways of Organic Solar Cells using SETDiR |
| Record Nr. | UNINA-9910780000703321 |
| Oxford, : Butterworth-Heinemann, 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Informatics for materials science and engineering : data-driven discovery for accelerated experimentation and application / / edited by Krishna Rajan
| Informatics for materials science and engineering : data-driven discovery for accelerated experimentation and application / / edited by Krishna Rajan |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Oxford, : Butterworth-Heinemann, 2013 |
| Descrizione fisica | 1 online resource (xv, 525 pages) : illustrations (some color) |
| Disciplina | 620.110285 |
| Collana | Gale eBooks |
| Soggetto topico | Materials science - Data processing |
| ISBN | 0-12-394614-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front Cover; Informatics for Materials Science and Engineering; Copyright Page; Contents; Preface: A Reading Guide; Acknowledgment; 1. Materials Informatics: An Introduction; 1. The What and Why of Informatics; 2. Learning from Systems Biology: An "Omics" Approach to Mater; 3. Where Do We Get the Information?; 4. Data Mining: Data-Driven Materials Research; References; 2. Data Mining in Materials Science and Engineering; 1. Introduction; 2. Analysis Needs of Science Applications; 3. The Scientific Data-Mining Process; 4. Image Analysis; 5. Dimension Reduction; 5.1 Feature Selection Techniques
Distance Filter Chi-Squared Filter; Stump Filter; Relief; 5.2 Feature Transformation Techniques; Principal Component Analysis (PCA); Isomap; Locally Linear Embedding (LLE); Laplacian Eigenmaps; 5.3 Comparison of Dimension Reduction Methods; 6. Building Predictive and Descriptive Models; 6.1 Classification and Regression; 6.2 Clustering; 7. Further Reading; Acknowledgments; References; 3. Novel Approaches to Statistical Learning in Materials Science; 1. Introduction; 2. The Supervised Binary Classification Learning Problem; 3. Incorporating Side Information; 4. Conformal Prediction 5. Optimal Learning 6. Optimal Uncertainty Quantification; 7. Clustering Including Statistical Physics Approaches; 8. Materials Science Example: The Search for New Piezoelectrics; 9. Conclusion; 10. Further Reading; Acknowledgments; References; 4. Cluster Analysis: Finding Groups in Data; 1. Introduction; 2. Unsupervised Learning; 2.1 Principal Components Analysis; 2.2 Clustering; 3. Different Clustering Algorithms and their Implementations in R; 3.1 Agglomerative Hierarchical; 3.2 K-Means; 3.3 Divisive Hierarchical; 3.4 Partitioning Around Medoids (PAM); 3.5 Fuzzy Analysis (FANNY) 4. Validations of Clustering Results 4.1 Dunn Index; 4.2 Silhouette Width; 4.3 Connectivity; 5. Rank Aggregation of Clustering Results; 6. Further Reading; Acknowledgments; References; 5. Evolutionary Data-Driven Modeling; 1. Preamble; 2. The Concept of Pareto Tradeoff; 3. Evolutionary Neural Net and Pareto Tradeoff; 4. Selecting the Appropriate Model in EvoNN; 5. Conventional Genetic Programming; 6. Bi-Objective Genetic Programming; 6.1 BioGP Code; 7. Analyzing the Variable Response in EvoNN and BioGP; 8. An Application in the Materials Area; 9. Further Reading; References 6. Data Dimensionality Reduction in Materials Science 1. Introduction; 2. Dimensionality Reduction: Basic Ideas and Taxonomy; 3. Dimensionality Reduction Methods: Algorithms, Advantages, and Disadvantages; 3.1 Principal Component Analysis (PCA); PCA Algorithm; 3.2 Isomap; Isomap Algorithm; 3.3 Locally Linear Embedding; LLE Algorithm; 3.4 Hessian LLE; hLLE Algorithm; 4. Dimensionality Estimators; 5. Software; 5.1 Core Functionality; 5.2 User Interface; 6. Analyzing Two Material Science Data Sets: Apatites and Organic Solar Cells; 6.1 Apatite Data; Dimensionality Estimation 6.2 Unraveling Process-Morphology Pathways of Organic Solar Cells using SETDiR |
| Record Nr. | UNINA-9910825391903321 |
| Oxford, : Butterworth-Heinemann, 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||