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The Open DAC 2023 Dataset and Challenges for Sorbent Discovery in Direct Air Capture
by
Sholl, David S.
, Yu, Xiaohan
, Ulissi, Zachary
, Sriram, Anuroop
, Choi, Sihoon
, Uyttendaele, Matt
, Das, Abhishek
, Medford, Andrew J.
, Brabson, Logan M.
in
Adsorption
/ Carbon dioxide
/ chemical structure
/ Climate change
/ Datasets
/ Decarbonization
/ Emissions
/ energy
/ Energy consumption
/ Humidity
/ INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
/ interaction energies
/ Machine learning
/ Metal-organic frameworks
/ Quantum chemistry
/ Sorbents
/ Temperature
2024
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The Open DAC 2023 Dataset and Challenges for Sorbent Discovery in Direct Air Capture
by
Sholl, David S.
, Yu, Xiaohan
, Ulissi, Zachary
, Sriram, Anuroop
, Choi, Sihoon
, Uyttendaele, Matt
, Das, Abhishek
, Medford, Andrew J.
, Brabson, Logan M.
in
Adsorption
/ Carbon dioxide
/ chemical structure
/ Climate change
/ Datasets
/ Decarbonization
/ Emissions
/ energy
/ Energy consumption
/ Humidity
/ INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
/ interaction energies
/ Machine learning
/ Metal-organic frameworks
/ Quantum chemistry
/ Sorbents
/ Temperature
2024
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Do you wish to request the book?
The Open DAC 2023 Dataset and Challenges for Sorbent Discovery in Direct Air Capture
by
Sholl, David S.
, Yu, Xiaohan
, Ulissi, Zachary
, Sriram, Anuroop
, Choi, Sihoon
, Uyttendaele, Matt
, Das, Abhishek
, Medford, Andrew J.
, Brabson, Logan M.
in
Adsorption
/ Carbon dioxide
/ chemical structure
/ Climate change
/ Datasets
/ Decarbonization
/ Emissions
/ energy
/ Energy consumption
/ Humidity
/ INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
/ interaction energies
/ Machine learning
/ Metal-organic frameworks
/ Quantum chemistry
/ Sorbents
/ Temperature
2024
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The Open DAC 2023 Dataset and Challenges for Sorbent Discovery in Direct Air Capture
Journal Article
The Open DAC 2023 Dataset and Challenges for Sorbent Discovery in Direct Air Capture
2024
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Overview
Direct air capture (DAC) is an emerging technology to aid decarbonization. Exploring metal−organic frameworks (MOFs) for DAC needs to encompass vast numbers of materials in the presence of humid CO2. We present a data set with over 38 million quantum chemistry calculations on thousands of MOFs containing CO2 and/or H2O, enabling machine learning models to accelerate development of MOFs for DAC.
Publisher
American Chemical Society,American Chemical Society (ACS)
Subject
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