Machine Learning in 2D Materials Science

Machine Learning in 2D Materials Science

"Data science and machine learning (ML) methods are increasingly being used to transform the way research is being conducted in materials science to enable new discoveries and design new materials. For any materials science researcher or student, it may be daunting to figure out if ML technique...

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Detalles Bibliográficos
Otros Autores: Chundi, Parvathi, author (author)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Boca Raton, FL : CRC Press [2023]
Edición:First edition
Materias:
Materials science > Data processing.
Two-dimensional materials > Computer simulation.
Machine learning.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009869125606719
Tabla de Contenidos:
  • Cover
  • Half Title
  • Title Page
  • Copyright Page
  • Table of Contents
  • Chapter 1 Introduction to Machine Learning for Analyzing Material-Microbe Interactions
  • 1.1 Introduction
  • References
  • Chapter 2 Introduction to 2D Materials
  • 2.1 Classification of 2D Materials
  • 2.2 Synthesis of 2D Materials
  • 2.2.1 Top-Down Methods
  • 2.2.2 Bottom-Up Methods
  • 2.2.3 Layer Transfer Methods
  • 2.3 Functionality of 2D Materials
  • 2.3.1 Mechanical Properties
  • 2.3.2 Electrical Properties
  • 2.3.3 Optical Properties
  • 2.4 Applications of 2D Materials
  • References
  • Chapter 3 An Overview of Machine Learning
  • 3.1 Introduction
  • 3.1.1 The Processing Pipeline of an ML Task
  • 3.1.2 Data Integration
  • 3.1.3 Data Preparation
  • 3.1.4 Model Building
  • 3.1.5 Model Evaluation
  • 3.2 ML Algorithms
  • 3.2.1 Bias and Variance
  • 3.3 Unsupervised Learning
  • 3.3.1 Cluster Analysis
  • 3.3.2 Principal Component Analysis (PCA)
  • 3.4 Supervised Learning
  • 3.4.1 Regression
  • 3.4.2 Classification
  • 3.4.3 Supervised Learning Variants: Self-Supervised Learning
  • 3.5 Deep Learning
  • 3.5.1 Convolutional Neural Networks (CNNs)
  • 3.6 Recurrent Neural Networks (RNN)
  • References
  • Chapter 4 Discovery of 2D Materials with Machine Learning
  • 4.1 Introduction: High-Throughput Screening
  • 4.2 ML Approaches for 2D Materials Research
  • 4.2.1 Three ML Approaches for 2D Materials Research
  • 4.2.2 A Summary of the Use of Machine Learning in 2D Materials Research
  • 4.3 Prediction of 2D Material Properties Using Machine Learning
  • 4.4 Application Machine Learning Approaches to Discover Novel 2D Materials
  • 4.4.1 Predictions of Crystal Structure
  • 4.4.2 Prediction of Components
  • 4.5 Machine Learning for Miscellaneous Functions
  • 4.6 Assessment of Common Challenges and Their Prevention Methods
  • 4.6.1 The Problems with Model Building
  • 4.6.2 Usability.
  • 4.6.3 Learning Efficiency
  • 4.7 Conclusions
  • References
  • Chapter 5 Bacterial Image Segmentation through Deep Learning Approach
  • 5.1 Introduction
  • 5.2 Literature Review and Related Work
  • 5.2.1 Conventional Approaches for Semantic Segmentation
  • 5.2.2 Contour-Based Methods
  • 5.2.3 Ellipse-Fitting Methods
  • 5.2.4 CNN-Based Approaches
  • 5.3 Methodology
  • 5.3.1 Data Collection
  • 5.3.2 Image Preprocessing
  • 5.3.3 ViTransUNet
  • 5.4 Experimental Design and Results
  • 5.4.1 Experimental Setup
  • 5.4.2 Evaluation Metrics
  • 5.4.3 Evaluation Results
  • 5.5 Conclusion and Future Work
  • References
  • Chapter 6 Self-Supervised Learning-Based Classification of Scanning Electron Microscope Images of Biofilms
  • 6.1 Introduction
  • 6.2 Self-Supervised Learning for Image Analyses
  • 6.2.1 Pretext Tasks
  • 6.2.2 Downstream Tasks on Medical Imaging
  • 6.3 Use of Super-Resolution to Address the Heterogeneity and Quality of SEM Biofilm Images
  • 6.3.1 Methodology
  • 6.3.2 Experimental Setup and Results
  • 6.3.3 Summary
  • 6.4 Classification of SEM Biofilms Using SSL
  • 6.4.1 Dataset
  • 6.4.2 Image Pre-Processing
  • 6.4.3 Annotation, Patch Generation, and Object Masking
  • 6.4.4 Self-Supervised Training
  • 6.4.5 Downstream Task
  • 6.4.6 Experiments
  • 6.4.7 Evaluation
  • 6.4.8 Results
  • 6.4.9 Discussion
  • 6.4.10 Summary
  • 6.5 Conclusion
  • Acknowledgements
  • References
  • Chapter 7 Quorum Sensing Mechanisms, Biofilm Growth, and Microbial Corrosion Effects of Bacterial Species
  • Definitions
  • Acronyms
  • 7.1 Introduction
  • 7.2 Quorum Sensing
  • 7.3 Key Quorum Sensing Molecules and Their Signaling Mechanisms
  • 7.4 Quorum Sensing in Relation to Stress Response
  • 7.5 Background on Biofilms with Focus on Its Ecology in Natural Ecosystems
  • 7.6 Quorum Sensing, Biofilm Growth, and Microbiologically Influenced Corrosion.
  • 7.6.1 QS, Biofilm Growth, and MIC
  • 7.6.2 Bioinformatics Analysis
  • 7.7 Adhesion-Induced Emergent Properties in Biofilm
  • 7.8 Methods to Inhibit Quorum Sensing
  • 7.9 Conclusion
  • Acknowledgments
  • References
  • Chapter 8 Data-Driven 2D Material Discovery Using Biofilm Data and Information Discovery System (Biofilm-DIDS)
  • 8.1 Introduction
  • 8.1.1 Microbial Community, Biofilm, and Material-Biofilm Interaction
  • 8.1.2 Complex System Design: SDLC and Agile Methodology Meets Big Data
  • 8.1.3 Big Data Mining and Knowledge Discovery
  • 8.2 Interface between the Living and the Non-Living: a System Thinking Approach
  • 8.2.1 System Understanding of Biointerface
  • 8.2.2 Big Data in Biointerfaces
  • 8.3 Biofilm-DIDS Overview
  • 8.4 Using Biofilm-DIDS to Extract Biocorrosion Gene of Interest from the Literature and Material Dimension Prediction
  • 8.4.1 Expert Informed Relevant Dataset Extraction from User Free Text Question
  • 8.4.2 Downstream Analysis for Material Dimension Prediction
  • 8.5 Conclusions
  • Acknowledgments
  • References
  • Chapter 9 Machine Learning-Guided Optical and Raman Spectroscopy Characterization of 2D Materials
  • 9.1 Introduction
  • 9.2 Established Surface Characterization Techniques
  • 9.3 ML-Guided Optical Detection of 2D Materials
  • 9.4 ML-Guided Raman Spectroscopy Detection of 2D Materials
  • 9.5 Common Challenges to ML in Raman Spectroscopy
  • 9.6 Future Prospects
  • 9.7 Summary
  • References
  • Chapter 10 Atomistic Experiments for Discovery of 2D Coatings: Biological Applications
  • 10.1 Introduction
  • 10.2 Molecular Dynamics (Algorithms and Methods)
  • 10.2.1 Empirical Forcefields
  • 10.2.2 Periodic Boundary Conditions
  • 10.2.3 Binding Energy
  • 10.2.4 Free Energy
  • 10.2.5 Umbrella Sampling
  • 10.2.6 Coarse-Grained Modeling
  • 10.3 Employment of MD on Functional 2D Materials.
  • 10.3.1 Graphene and Its Structural Defects
  • 10.3.2 The Emergence of Bioinformatics: Applications and Methodologies
  • 10.3.3 Current Trends in Biomolecular Simulation and Modeling
  • 10.4 Machine Learning
  • 10.4.1 ML Methods for 2D Materials
  • 10.4.2 ML for Force Field Development and Parameterization
  • 10.4.3 ML for Protein Structure Prediction
  • 10.5 Summary
  • References
  • Chapter 11 Machine Learning for Materials Science: Emerging Research Areas
  • 11.1 Introduction
  • 11.2 Applications of ML in Materials Science
  • 11.2.1 Additive Manufacturing
  • 11.2.2 Combinatorial Synthesis and Machine Learning-Assisted Discovery of Thin Films
  • 11.2.3 Machine Learning-Assisted Properties Prediction of Bulk Alloys
  • 11.2.4 Design of Drug-Releasing Materials with Machine Learning
  • 11.2.5 AI and ML Tools for Search and Discovery of Quantum Materials
  • 11.3 Gaps and Barriers to Implementation
  • References
  • Chapter 12 The Future of Data Science in Materials Science
  • 12.1 Introduction
  • 12.2 Learning with Small Training Datasets
  • 12.2.1 Data Augmentation
  • 12.2.2 Semisupervised Learning
  • 12.2.3 Transfer Learning
  • 12.2.4 Few-Shot Learning
  • 12.3 Physics-Inspired Neural Networks
  • 12.4 Digital Twins
  • 12.5 Data-Centric Artificial Intelligence
  • 12.5.1 Data Collection
  • 12.5.2 Robust and Fair Model Training
  • 12.5.3 Continuous Learning
  • 12.6 GPT Models
  • 12.7 Future Directions in Using ML for 2D Materials
  • References
  • Index.

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