Hydrogen storage in MOFs: Machine learning for finding a needle in a haystack.
basic_science · Level V
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- Record sourced from PubMed, PMID 34286309.
- Also identified by DOI 10.1016/j.patter.2021.100305 and PMC identifier 8276009.
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Abstract
In recent years, machine learning (ML) has grown exponentially within the field of structure property predictions in materials science. In this issue of <i>Patterns</i>, Ahmed and Siegel scrutinize several redeveloped ML techniques for systematic investigations of over 900,000 metal-organic framework (MOF) structures, taken from 19 databases, to discover new, potentially record-breaking, hydrogen-storage materials.