Price: $109.99
(as of Jan 21,2025 09:11:47 UTC – Details)
Publisher : Springer; 1st ed. 2024 edition (March 17, 2025)
Language : English
Hardcover : 481 pages
ISBN-10 : 9819979919
ISBN-13 : 978-9819979912
Item Weight : 1.74 pounds
Materials informatics is a rapidly evolving field that combines principles of materials science, data science, and machine learning to accelerate the discovery and development of new materials. By leveraging vast amounts of data and advanced computational techniques, materials informatics holds the promise of revolutionizing the way we design and engineer materials for various applications.
At the core of materials informatics is machine learning, a subset of artificial intelligence that focuses on building algorithms and models that can learn from data and make predictions or decisions without being explicitly programmed. In the context of materials science, machine learning algorithms can be trained on large datasets of material properties, structures, and performance metrics to identify patterns, correlations, and relationships that can inform the design of new materials with specific properties.
There are several key elements of machine learning that are essential to understanding materials informatics:
1. Data collection and preprocessing: The first step in any machine learning project is to gather relevant data from various sources, such as experimental measurements, simulations, literature, or databases. This data must then be cleaned, formatted, and annotated to ensure its quality and consistency before it can be used for training machine learning models.
2. Feature selection and engineering: Features are the individual attributes or properties of a material that are used as inputs to machine learning models. Feature selection involves choosing the most relevant and informative features for a given task, while feature engineering involves transforming or combining features to enhance the predictive power of the model.
3. Model selection and training: Machine learning models come in various forms, such as neural networks, decision trees, support vector machines, and more. The choice of model depends on the complexity of the data and the desired outcomes. Models are trained on labeled data through an iterative process of optimization to minimize prediction errors and improve performance.
4. Validation and evaluation: Once a model has been trained, it must be validated and evaluated to assess its accuracy, generalization, and robustness. This involves splitting the data into training and testing sets, cross-validation, and performance metrics such as accuracy, precision, recall, and F1 score.
5. Model interpretation and deployment: Finally, the insights gained from machine learning models can be used to interpret and understand the underlying relationships between material properties and performance. These models can then be deployed in real-world applications, such as materials design, optimization, and discovery.
In conclusion, materials informatics offers a powerful framework for integrating machine learning and materials science to accelerate innovation and discovery in the field of materials research. By harnessing the power of data-driven approaches, researchers and engineers can unlock new opportunities for designing and developing advanced materials with tailored properties and functionalities.
#Introduction #Materials #Informatics #Elements #Machine #Learning,machine learning: an applied mathematics introduction
