Emerging materials intelligence ecosystems propelled by machine learning

By Rohit Batra, Le Song, Rampi Ramprasad. Nature Reviews Materials, 2020

336 citations recorded by the source corpus.

Subfields: Materials Chemistry

View Paper: https://www.semanticscholar.org/paper/f3542470da73d444f3f63030517ec918553caadc

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In Materials Chemistry

1. Nour Makke
2. Sanjay Chawla

Citations in the corpus

• Interpretable scientific discovery with symbolic regression: a review (2022, methodology): “SR has been applied in fundamental and applied sciences such as astrophysics [34], chemistry [35, 36], materials science [37, 38], semantic similarity measurement [39], climatology [40], medicine [41], among many others.”
• Benchmarking graph neural networks for materials chemistry (2021, background): “To accomplish this, several criteria should be met: (1) the same datasets should be used across the evaluated models, (2) the datasets used should represent diverse problems in materials chemistry, (3) the same input information and representation should be used, (4) the hyperparameters of the model”