Abstract
Contact and support
Need help, have a question, or want to contact the ResearchHub team?
© 2026 ResearchHub. Built for responsible scholarly connection.
Dhrubajyoti Nath, Poran Borboruah, Hridoy Jyoti Mahanta, Dipyaman Mohanta
Abstract
Authors
Institutions
No ROR-resolved institution is linked to this work yet.
Provenance
crossref
Confidence 100%
pubmed
Confidence 98%
unpaywall
Confidence 95%
datacite
Confidence 0%
No local reference links have been materialized yet.
No local citing links have been materialized yet.
The Nobel Prize in Chemistry 2025
2025
The chemistry and applications of metal–organic frameworks
10.1126/science.1230444 · 2013
Metal–organic framework materials with ultrahigh surface areas: is the sky the limit?
10.1021/ja3055639 · 2012
Dramatic tuning of carbon dioxide uptake via metal substitution in a coordination polymer with cylindrical pores
10.1021/ja8036096 · 2008
Cooperative insertion of CO2 in diamine-appended metal–organic frameworks
10.1038/nature14327 · 2015
Made-to-Order metal–organic frameworks for trace carbon dioxide removal and air capture
10.1038/ncomms5228 · 2014
Direct air capture of CO2 by physisorbent materials
10.1002/anie.201506952 · 2015
A scalable metal–organic framework as a durable physisorbent for carbon dioxide capture
10.1126/science.abi7281 · 2021
Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery
10.1016/j.matt.2021.02.015 · 2021
Understanding the diversity of the metal–organic framework ecosystem
10.1038/s41467-020-17755-8 · 2020
Big-data science in porous materials: materials genomics and machine learning
10.1021/acs.chemrev.0c00004 · 2020
Random forests
10.1023/a:1010933404324 · 2001
Greedy function approximation: a gradient boosting machine
10.1214/aos/1013203451 · 2001
XGBoost: a scalable tree boosting system
2016
LightGBM: a highly efficient gradient boosting decision tree
2017
A unified approach to interpreting model predictions
2017
"Why Should I Trust You?": explaining the predictions of any classifier
2016
Matminer: an open source toolkit for materials data mining
10.1016/j.commatsci.2018.05.018 · 2018
A general-purpose machine learning framework for predicting properties of inorganic materials
10.1038/npjcompumats.2016.28 · 2016
Python materials genomics (pymatgen): a robust, open-source python library for materials analysis
10.1016/j.commatsci.2012.10.028 · 2013
Machine learning with force-field-inspired descriptors for materials: fast screening and mapping energy landscape
2018
The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design
10.1038/s41524-020-00440-1 · 2020
Large-scale screening of hypothetical metal–organic frameworks
10.1038/nchem.1192 · 2011
Development of a Cambridge structural database subset: a collection of metal–organic frameworks for past, present, and future
10.1021/acs.chemmater.7b00441 · 2017
Advances, updates, and analytics for the computation-ready, experimental metal–organic framework database: CoRE MOF 2019
10.1021/acs.jced.9b00835 · 2019
The open DAC 2023 dataset and challenges for sorbent discovery in direct air capture
10.1021/acscentsci.3c01629 · 2024
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
Direct capture of CO2 from ambient air
10.1021/acs.chemrev.6b00173 · 2016
Intrinsic direct air capture
10.1039/d5sc06099k · 2025
Optuna: a next-generation hyperparameter optimization framework
2019
Porous materials with optimal adsorption thermodynamics and kinetics for CO2 separation
10.1038/nature11893 · 2013
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
Interpretable machine learning for materials discovery: predicting CO2 adsorption properties of metal–organic frameworks
10.1063/5.0222154 · 2024
No additional external references are available.