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References from Interpretable machine learning reveals geometry-dominated design rules for atmospheric CO2 capture in metal-organic frameworks. Local targets link to admitted publications; unresolved targets remain external evidence.
The Nobel Prize in Chemistry 2025
2025 · External reference
The chemistry and applications of metal–organic frameworks
10.1126/science.1230444 · 2013 · External reference
Metal–organic framework materials with ultrahigh surface areas: is the sky the limit?
10.1021/ja3055639 · 2012 · External reference
Dramatic tuning of carbon dioxide uptake via metal substitution in a coordination polymer with cylindrical pores
10.1021/ja8036096 · 2008 · External reference
Cooperative insertion of CO2 in diamine-appended metal–organic frameworks
10.1038/nature14327 · 2015 · External reference
Made-to-Order metal–organic frameworks for trace carbon dioxide removal and air capture
10.1038/ncomms5228 · 2014 · External reference
Direct air capture of CO2 by physisorbent materials
10.1002/anie.201506952 · 2015 · External reference
A scalable metal–organic framework as a durable physisorbent for carbon dioxide capture
10.1126/science.abi7281 · 2021 · External reference
Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery
10.1016/j.matt.2021.02.015 · 2021 · External reference
Understanding the diversity of the metal–organic framework ecosystem
10.1038/s41467-020-17755-8 · 2020 · External reference
Big-data science in porous materials: materials genomics and machine learning
10.1021/acs.chemrev.0c00004 · 2020 · External reference
Random forests
10.1023/a:1010933404324 · 2001 · External reference
Greedy function approximation: a gradient boosting machine
10.1214/aos/1013203451 · 2001 · External reference
XGBoost: a scalable tree boosting system
2016 · External reference
LightGBM: a highly efficient gradient boosting decision tree
2017 · External reference
A unified approach to interpreting model predictions
2017 · External reference
"Why Should I Trust You?": explaining the predictions of any classifier
2016 · External reference
Matminer: an open source toolkit for materials data mining
10.1016/j.commatsci.2018.05.018 · 2018 · External reference
A general-purpose machine learning framework for predicting properties of inorganic materials
10.1038/npjcompumats.2016.28 · 2016 · External reference
Python materials genomics (pymatgen): a robust, open-source python library for materials analysis
10.1016/j.commatsci.2012.10.028 · 2013 · External reference
Machine learning with force-field-inspired descriptors for materials: fast screening and mapping energy landscape
2018 · External reference
The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design
10.1038/s41524-020-00440-1 · 2020 · External reference
Large-scale screening of hypothetical metal–organic frameworks
10.1038/nchem.1192 · 2011 · External reference
Development of a Cambridge structural database subset: a collection of metal–organic frameworks for past, present, and future
10.1021/acs.chemmater.7b00441 · 2017 · External reference
Advances, updates, and analytics for the computation-ready, experimental metal–organic framework database: CoRE MOF 2019
10.1021/acs.jced.9b00835 · 2019 · External reference
The open DAC 2023 dataset and challenges for sorbent discovery in direct air capture
10.1021/acscentsci.3c01629 · 2024 · External reference
High-throughput screening of the CoRE-MOF-2019 database for CO2 capture from wet flue gas: a multi-scale modeling strategy
10.1021/acsami.3c04079 · 2023 · External reference
Direct capture of CO2 from ambient air
10.1021/acs.chemrev.6b00173 · 2016 · External reference
Intrinsic direct air capture
10.1039/d5sc06099k · 2025 · External reference
Optuna: a next-generation hyperparameter optimization framework
2019 · External reference
Porous materials with optimal adsorption thermodynamics and kinetics for CO2 separation
10.1038/nature11893 · 2013 · External reference
Gradient boosted machine learning model to predict H2, CH4, and CO2 uptake in metal–organic frameworks using experimental data
10.1021/acs.jcim.3c00135 · 2023 · External reference
Interpretable machine learning for materials discovery: predicting CO2 adsorption properties of metal–organic frameworks
10.1063/5.0222154 · 2024 · External reference
