Anuroop Sriram*, Sihoon Choi, Xiaohan Yu, Logan M. Brabson, Abhishek Das, Zachary Ulissi, Matt Uyttendaele, Andrew J. Medford* and David S. Sholl*,
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引用次数: 0
Abstract
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.
Direct air capture (DAC) of CO2 with porous adsorbents such as metal−organic frameworks (MOFs) has the potential to aid large-scale decarbonization. Previous screening of MOFs for DAC relied on empirical force fields and ignored adsorbed H2O and MOF deformation. We performed quantum chemistry calculations overcoming these restrictions for thousands of MOFs. The resulting data enable efficient descriptions using machine learning.
期刊介绍:
ACS Central Science publishes significant primary reports on research in chemistry and allied fields where chemical approaches are pivotal. As the first fully open-access journal by the American Chemical Society, it covers compelling and important contributions to the broad chemistry and scientific community. "Central science," a term popularized nearly 40 years ago, emphasizes chemistry's central role in connecting physical and life sciences, and fundamental sciences with applied disciplines like medicine and engineering. The journal focuses on exceptional quality articles, addressing advances in fundamental chemistry and interdisciplinary research.