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About Advances of Machine Learning in Materials Science: Ideas and Techniques
1. The document discusses advances in machine learning techniques for materials science. It has enabled high-throughput screening and generation of materials using large datasets and models. 2. Traditionally, materials science involved trial-and-error methods in computational and experimental work. Machine learning has enabled faster screening of materials and generation of new materials based on similar properties. 3. The review covers commonly used machine learning methods like neural networks, decision trees, clustering, and generative models and their applications in areas like nanostructures, alloys, surfaces and interfaces. It also discusses future directions like more integrated use of machine learning and coupling with traditional techniques.
Detailed Information
| Author: | ['danilo'] |
|---|---|
| Publication Year: | 2023 |
| Pages: | 40 |
| Language: | English |
| Format: | |
| Price: | FREE |
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