A Comprehensive Review of Machine-Learning Approaches for Crystal Structure/Property Prediction

Mostafa Sadeghian, Arvydas Palevičius, Giedrius Janušas

Crystals 2025 article cited by 12

importance 6/10 ai assisted Machine Learning in Materials Science materials science stage: research

Traditional computational crystal structure and property prediction is costly, poorly scalable and struggles with complex energy surfaces.

Faster materials discovery affects development of advanced materials across many industries.

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.

not stated in source

The evidence only supports that ML methods are claimed to enhance prediction accuracy and reduce computational expense.

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
6 / 10

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.

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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