Machine learning for perovskite materials design and discovery
Problem
Discovering and rationally designing new perovskite materials is slow; the paper reviews how machine learning can assist that discovery process.
Why it matters
Materials development is a driving force of scientific progress and technological innovation, per the abstract.
What the AI did
It reviews ML workflows applied to predicting properties of inorganic, hybrid organic-inorganic, and double perovskites to guide discovery and design.
Results
not stated in source
Impact
Organizes recent ML applications to perovskite discovery; reports no new material or benchmark.
Record
| Venue | npj Computational Materials |
|---|---|
| Published | 2021 |
| Work type | article |
| Authors | Qiuling Tao, Pengcheng Xu, Minjie Li, Wencong Lu |
| Citations | 536 (OpenAlex, at import time) |
| References | 148 |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1038/s41524-021-00495-8 |
| Open access | gold |
| Keywords | machine learning, perovskites, materials discovery, materials design, review |
Importance rating
significant — thousands to millions affected
Abstract states materials development is a driving force of scientific progress and technological innovation; ML-assisted perovskite discovery targets a foundational but unquantified bottleneck.
More in Engineering & Materials
| # | Paper | Year | Citations | Importance |
|---|---|---|---|---|
| 1 | Scaling deep learning for materials discovery | 2023 | 1,471 |
8
|
| 2 | Application of Machine Learning in Material Synthesis and Property Prediction | 2023 | 165 |
7
|
| 3 | Machine learning-guided discovery of ionic polymer electrolytes for lithium metal batteries | 2023 | 115 |
7
|
| 4 | Machine Learning for Structural Health Monitoring of Aerospace Structures: A Review | 2025 | 44 |
7
|
| 5 | Power System Transient Stability Assessment Based on Machine Learning Algorithms and Grid Topology | 2023 | 42 |
7
|