Abstract
To comply with the Kigali amendment to the Montreal Protocol in 2016, development of new refrigerants with low global warming potential is urgently required in addition to satisfying the conventional requirements of cooling performance, safety, and non-destructiveness to the ozone layer. Because these requirements closely correlated, the proper control of various chemical properties is necessary to fulfill the requirements. However, simultaneous satisfaction of all the requirements is extremely difficult because of the tradeoffs among the chemical properties. Hence, we must correctly recognize how chemical properties behave when the composition of molecules is changed. We performed an in-silico screening that combines quantum chemical calculations, machine learning, and database search, where 10,163 molecules were investigated exhaustively within the properly imposed constraints; subsequently we found a few candidates.
Highlights
To comply with the Kigali amendment to the Montreal Protocol in 2016, development of new refrigerants with low global warming potential is urgently required in addition to satisfying the conventional requirements of cooling performance, safety, and non-destructiveness to the ozone layer
It is noteworthy that the importance of in silico screening for obtaining the optimal compositions of molecules/crystals in material design is increasing with the recent development of computers and computing technologies[2,3,4]
We show the following data in Spreadsheet S1, which is attached as supplementary datasets: Chemical formula of 131 molecules, descriptors for gradient boosting regression (GBR), global warming potential (GWP) tabulated in AR5 by intergovernmental panel on climate change (IPCC), and the predicted
Summary
To comply with the Kigali amendment to the Montreal Protocol in 2016, development of new refrigerants with low global warming potential is urgently required in addition to satisfying the conventional requirements of cooling performance, safety, and non-destructiveness to the ozone layer. Because these requirements closely correlated, the proper control of various chemical properties is necessary to fulfill the requirements. Refrigerants are suitable for such in silico screening because the molecule size is relatively small: they must be a gas at room temperature and atmospheric pressure This enables us to examine the chemical space of the refrigerants by exhaustively changing their compositions within the range of the appropriately determined constraints. When an unsaturated bond does not exist, the GWP must be predicted by machine-learning approaches to select molecules that have a low GWP
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
