Adsorption regime changes the information needed for data-driven metal–organic framework screening
4 affiliations
- 1Lucy Cavendish College
- 2University of Cambridge
- 3School of Chemical Engineering, Faculty of Engineering
- 4University of Tehran
Abstract
How much fitting evidence, and which materials descriptors, are needed to prioritize porous materials? We analyze archived computational uptake labels for 279,010 metal–organic frameworks at four CO2/CH4 conditions (298 K). In retrospective experiments using target-informed example selection, each condition reaches at least 90% of its B = 1000 top-5% recall endpoint by B = 500. Local-chemistry RAC descriptors improve methane top-5% recall by 0.175 at 5.8 bar but only 0.039 at 65 bar, consistent with a pressure-dependent balance between chemical and geometric signals. These values describe predictor-fitting budgets, not total acquisition cost: the random-split setting also uses approximately 44,642 labeled structures for conformal calibration. Missing historical split identities and reference-informed candidate selection constrain transfer and shortlist claims. Thus, adsorption conditions appear to modify representation utility, while prospective label efficiency and physical attribution require independent, leakage-audited validation.
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BibTeX
@misc{papadantonaki2026adsorption,
title = {{Adsorption regime changes the information needed for data-driven metal–organic framework screening}},
author = {Natalia Lydia Papadantonaki and Angelo Di Bella and Shayan Abaei and Mehrdad Asgari},
howpublished = {ChemRxiv},
year = {2026},
month = oct,
doi = {10.26434/chemrxiv.15010304/v1},
url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010304/v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Adsorption regime changes the information needed for data-driven metal–organic framework screening
AU - Natalia Lydia Papadantonaki
AU - Angelo Di Bella
AU - Shayan Abaei
AU - Mehrdad Asgari
PY - 2026
DA - 2026/10/12
PB - ChemRxiv
DO - 10.26434/chemrxiv.15010304/v1
UR - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010304/v1
AB - How much fitting evidence, and which materials descriptors, are needed to prioritize porous materials? We analyze archived computational uptake labels for 279,010 metal–organic frameworks at four CO2/CH4 conditions (298 K). In retrospective experiments using target-informed example selection, each condition reaches at least 90% of its B = 1000 top-5% recall endpoint by B = 500. Local-chemistry RAC descriptors improve methane top-5% recall by 0.175 at 5.8 bar but only 0.039 at 65 bar, consistent with a pressure-dependent balance between chemical and geometric signals. These values describe predictor-fitting budgets, not total acquisition cost: the random-split setting also uses approximately 44,642 labeled structures for conformal calibration. Missing historical split identities and reference-informed candidate selection constrain transfer and shortlist claims. Thus, adsorption conditions appear to modify representation utility, while prospective label efficiency and physical attribution require independent, leakage-audited validation.
ER -