Virtual sample generation in machine learning assisted materials design and discovery
Problem
Machine learning-assisted materials design and discovery is limited by small datasets; virtual sample generation techniques are used to expand training data and guide discovery.
Why it matters
Accelerating materials design and discovery speeds development of new materials for industry and technology.
What the AI did
Review summarizing virtual sample generation algorithms (Bootstrap, Monte Carlo, particle swarm optimization, Gaussian mixture models, GANs) and search algorithms applied in ML-assisted materials design.
Results
not stated in source
Impact
Evidence supports that VSG techniques have been applied in ML-assisted materials design; no specific quantified outcome given.
Record
| Venue | Journal of Materials Informatics |
|---|---|
| Published | 2023 |
| Work type | article |
| Authors | Pengcheng Xu, Xiaobo Ji, Minjie Li, Wencong Lu |
| Citations | 20 (OpenAlex, at import time) |
| References | 79 |
| Model or method | GANs, particle swarm optimization, Gaussian mixture models (reviewed techniques) |
| Organization | not stated in source |
| Primary source | https://doi.org/10.20517/jmi.2023.18 |
| Open access | closed |
| Keywords | virtual sample generation, machine learning, materials discovery, GANs, data augmentation |
Importance rating
real but narrower advance
Review of virtual sample generation for ML-assisted materials design; addresses small-dataset limits in a broad field but gives no quantified outcomes or scale.
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