Virtual sample generation in machine learning assisted materials design and discovery

Pengcheng Xu, Xiaobo Ji, Minjie Li, Wencong Lu

Journal of Materials Informatics 2023 article cited by 20

importance 4/10 ai assisted Machine Learning in Materials Science materials science stage: research

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.

Accelerating materials design and discovery speeds development of new materials for industry and technology.

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.

not stated in source

Evidence supports that VSG techniques have been applied in ML-assisted materials design; no specific quantified outcome given.

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
4 / 10

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.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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