# Machine Learning in Materials Science

**Type:** Topics  
**Canonical URL:** https://scholariq.org/topics/machine-learning-in-materials-science-2/

## Facts

| Field | Value |
| --- | --- |
| Citations | 1,050,125 |
| Description | 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. |
| Domain | Physical Sciences |
| Field | Materials Science |
| OpenAlex ID | https://openalex.org/T11948 |
| Works | 143,155 |

## Topic researchers

Showing 12 of 20.

- [Yoshua Bengio](https://scholariq.org/researchers/yoshua-bengio/)
- [Geoffrey E. Hinton](https://scholariq.org/researchers/geoffrey-e-hinton/)
- [John P. Perdew](https://scholariq.org/researchers/john-p-perdew/)
- [Georg Kresse](https://scholariq.org/researchers/georg-kresse/)
- [Kieron Burke](https://scholariq.org/researchers/kieron-burke/)
- [Wei Huang](https://scholariq.org/researchers/wei-huang-5/)
- [Yi Cui](https://scholariq.org/researchers/yi-cui/)
- [Jens K. Nørskov](https://scholariq.org/researchers/jens-k-n-rskov/)
- [Omar M. Yaghi](https://scholariq.org/researchers/omar-m-yaghi/)
- [Kenji Watanabe](https://scholariq.org/researchers/kenji-watanabe/)
- [Donald G. Truhlar](https://scholariq.org/researchers/donald-g-truhlar/)
- [Takashi Taniguchi](https://scholariq.org/researchers/takashi-taniguchi/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.