Application of Machine Learning in Material Synthesis and Property Prediction
Problem
Traditional experimental and simulation-based discovery of new materials is slow and resource-intensive, motivating faster computational approaches.
Why it matters
Faster materials discovery accelerates technological progress and industrial development.
What the AI did
This is a review article outlining machine learning principles and commonly used algorithms applied to material screening and property prediction across fields like superconductivity and catalysis.
Results
not stated in source
Impact
The evidence only summarizes prior research progress; no new problem is solved here.
Record
| Venue | Materials |
|---|---|
| Published | 2023 |
| Work type | article |
| Authors | Guannan Huang, Yani Guo, Ye Chen, Zhengwei Nie |
| Citations | 165 (OpenAlex, at import time) |
| References | 129 |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3390/ma16175977 |
| Open access | gold |
| Keywords | machine learning, materials discovery, property prediction, review, screening |
Importance rating
significant — thousands to millions affected
Abstract states material innovation is very important to technological progress and industrial development and traditional discovery is slow/resource-intensive; spans superconductivity, photovoltaics, catalysis; unquantified.