A Comprehensive Review of Machine-Learning Approaches for Crystal Structure/Property Prediction
Problem
Traditional computational crystal structure and property prediction is costly, poorly scalable and struggles with complex energy surfaces.
Why it matters
Faster materials discovery affects development of advanced materials across many industries.
What the AI did
The paper reviews the transition from classical optimization to machine-learning-based methods for crystal structure and property prediction; it is a review, not a new tool.
Results
not stated in source
Impact
The evidence only supports that ML methods are claimed to enhance prediction accuracy and reduce computational expense.
Record
| Venue | Crystals |
|---|---|
| Published | 2025 |
| Work type | article |
| Authors | Mostafa Sadeghian, Arvydas Palevičius, Giedrius Janušas |
| Citations | 12 (OpenAlex, at import time) |
| References | 189 |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3390/cryst15110925 |
| Open access | gold |
| Keywords | crystal structure prediction, crystal property prediction, machine learning, materials discovery, review |
Importance rating
significant — thousands to millions affected
Crystal structure/property prediction accelerates advanced materials discovery across scientific disciplines, a broad but methodological field; abstract gives no quantified scale of impact or beneficiaries.
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