Machine Learning in Materials Science
Machine Learning in Materials Science is a research topic within Materials Chemistry. Science Explorer counts 30k research works in it since 1950. 10.0% of them reached the world's top 10% most cited for their field and year.
This cluster of papers focuses on the application of materials informatics, machine learning, and high-throughput computational techniques to accelerate materials innovation. It encompasses topics such as property predictions, crystal structures, molecular dynamics, and data mining in the context of materials science and engineering.
- Materials Informatics
- Machine Learning
- High-Throughput
- Computational Chemistry
- Materials Discovery
- Quantum Mechanics
- Crystal Structures
- Molecular Dynamics
- Property Predictions
- Data Mining
- Research works
- 30k fractional, since 1950
- In the world top 10%
- 3k per year above
- Top-10% rate
- 10.0% share of its works in the world top 10%
- Growth, 2018-21 → 2022-25
- +117% the tick is no change
- Largest producer
China
33% of the 10 largest, 2022-now
Discussion
Ask a question or share a note about Machine Learning in Materials Science. Keep it to the research: no personal attacks, advertising or links. Any reader can report a post, which collapses it for everyone until it is opened.