High-throughput computational screening of metal-organic frameworks.
By Yamil J. Colón, Randall Q. Snurr. Chemical Society Reviews, 2014
322 citations recorded by the source corpus.
Subfields: Inorganic Chemistry, Materials Chemistry
View Paper: https://www.semanticscholar.org/paper/6ec78bb56a3dc623de248b30c7ccfa1be957b4df
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Citations in the corpus
- Machine-learning-assisted materials discovery using failed experiments (2016, background): “…are: simulation-based predictions of physical properties (for example, charge mobility 11 , photovoltaic properties 12 , gas adsorption capacity 13 or lithium-ion intercalation 14 ) to identify promising target candidates for synthetic efforts 11,15 ; determination of the structure–property…”
- LitMOF: An LLM Multi-Agent for Literature-Validated Metal-Organic Frameworks Database Correction and Expansion (2025, unknown): “…of large curated databases, including the CoRE MOF databases[2–4], the CSD MOF subset[5, 6], QMOF database[7, 8], and MOSAEC-DB[9], which serve as the foundation for high-throughput simulations[10–12], machine learning based property prediction[13–17], and accelerated materials discovery [18–20].”