Cloud-Based, High-Throughput, End-To-End Computational Screening of Solid Sorbent Materials for Carbon Capture

Neumann, Rodrigo F.; Conchuir, Breanndan O; Elengikal, Tonia; Luan, Binquan; Ohta, Ricardo Luís; Oliveira, Felipe Lopes; Mhadeshwar, Ashish B.; Kalyanaraman, Jayashree; Sundaram, Anantha; Falkowski, Joseph M.; Szlachta, Jonathan Rodrigues; Joshi, Yogesh; Steiner, M.

doi:10.2139/ssrn.4275135

Cited by 2 publications

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“…There are many varieties of these materials, both hypothetical and real, amounting to 10 5 -10 6 of them in databases (Moghadam et al, 2017;Boyd et al, 2019) that allow machine learning tools for screening (Wilmer et al, 2012a). The MDLab results reported here use three materials from the database CoRE MOF 2014 (Computation-Ready, Experimental Metal-Organic Frameworks; Chung et al, 2014), and place these data, the screening workflows, and a Jupyter Lab interface in containers on a cloud-based computer cluster (Neumann et al, 2022) to facilitate user interaction and workload management. A more extensive study than what is reported here is in Oliveira et al (2023).…”

Section: Solid Sorbentsmentioning

confidence: 99%

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MDLab: AI frameworks for carbon capture and battery materials

Elmegreen,

Hamann,

Wunsch

et al. 2023

Front. Environ. Sci.

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There is a growing urgency to discover better materials that capture CO2 from air and improve battery performance. An important step is to search large databases of materials properties to find examples that resemble known carbon capture agents or electrolytes and then test them for effectiveness. This paper describes novel computational tools for accelerated discovery of solvents, nano-porous materials, and electrolytes. These tools have produced interesting results so far, such as the identification of a relatively isolated location in amine configuration space for the solvents with known carbon capture use, and the demonstration of an end-to-end simulation and process model for carbon capture in MOFs.

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Section: Solid Sorbentsmentioning

confidence: 99%

“…This review summarizes MDLab. More detailed publications are in McDonagh et al (2022McDonagh et al ( , 2023, Neumann et al (2022), and Sharma et al (2023).…”

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confidence: 99%

MDLab: AI frameworks for carbon capture and battery materials

Elmegreen,

Hamann,

Wunsch

et al. 2023

Front. Environ. Sci.

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“…Recent advancements in computational chemistry, coupled with the increasing processing power of modern computational facilities, have enabled the use of high-throughput computation-based and machine-learning approaches. − These approaches require the analysis of a vast number of chemical and structural variations for numerous compounds, making it possible to select only the most promising candidates for further synthetic efforts. This powerful combination of computational tools and the ability to understand at a molecular level the phenomena related to the applications of COFs helps to streamline the materials discovery process, allowing researchers to more efficiently explore, identify, and improve new useful materials.…”

Section: Introductionmentioning

confidence: 99%

pyCOFBuilder: A Python Package for Automated Creation of Covalent Organic Framework Models Based on the Reticular Approach

Oliveira,

Esteves

2024

J. Chem. Inf. Model.

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Covalent organic frameworks (COFs) have gained significant popularity in recent years due to their unique ability to provide a high surface area and customizable pore geometry and chemistry, making them an ideal choice for a wide range of applications. However, exploring COFs experimentally can be arduous and time-consuming due to their immense number of potential structures. As a result, computational high-throughput studies have become an attractive option. Nevertheless, generating COF structures can also be a challenging and time-consuming task. To address this challenge, here, we introduce the pyCOFBuilder, an open-source Python package designed to facilitate the generation of COF structures for computational studies. The pyCOFBuilder software provides an easy-to-use set of functionalities to generate COF structures following the reticular approach. In this paper, we describe the implementation, main features, and capabilities of the pyCOFBuilder, demonstrating its utility for generating COF structures with varying topologies and chemical properties. pyCOFBuilder is freely available on GitHub at https://github.com/ lipelopesoliveira/pyCOFBuilder.

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Cloud-Based, High-Throughput, End-To-End Computational Screening of Solid Sorbent Materials for Carbon Capture

Cited by 2 publications

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MDLab: AI frameworks for carbon capture and battery materials

MDLab: AI frameworks for carbon capture and battery materials

pyCOFBuilder: A Python Package for Automated Creation of Covalent Organic Framework Models Based on the Reticular Approach

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