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Characterization data for the manuscript: "Using genetic algorithms to systematically improve the synthesis conditions of Al-PMOF"

Name: Characterization data for the manuscript: "Using genetic algorithms to systematically improve the synthesis conditions of Al-PMOF"
Keywords: Artificial intelligence, Electronic lab notebook, Genetic algorithm, Metal-organic frameworks, MOF

Research datakeyboard_double_arrow_right Dataset 06 Jun 2022Publisher:Zenodo

Authors: P. Domingues, Nency; Moosavi, Seyed Mohamad; Talirz, Leopold; P. Ireland, Christopher; Ebrahim, Fatmah Mish; Smit, Berend;

doi: 10.5281/zenodo.6620501 , 10.5281/zenodo.6620502 , 10.5281/zenodo.6616402

Characterization data for the manuscript: "Using genetic algorithms to systematically improve the synthesis conditions of Al-PMOF"

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Abstract

This work is part of the PrISMa Project (299659), funded through the ACT Programme (Accelerating CCS Technologies, Horizon 2020 Project 294766). Financial contributions from the Department for Business, Energy & Industrial Strategy (BEIS) together with extra funding from the NERC and EPSRC Research Councils, United Kingdom, the Research Council of Norway (RCN), the Swiss Federal Office of Energy (SFOE), and the U.S. Department of Energy are gratefully acknowledged. Additional financial support from TOTAL and Equinor is also gratefully acknowledged. S.M.M.\ acknowledges support from the Swiss National Science Foundation (SNSF) under Grant P2ELP2_195155.

This entry contains characterization data for the manuscript "Using genetic algorithms to systematically improve the synthesis conditions of Al-PMOF", which we exported from the electronic lab notebook (ELN). To visualize the data in this dataset: open entry

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École Polytechnique Fédérale de Lausanne EPFL
Switzerland

Keywords

Artificial intelligence, Electronic lab notebook, Genetic algorithm, Metal-organic frameworks, MOF

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Characterization data for the manuscript: "Using genetic algorithms to systematically improve the synthesis conditions of Al-PMOF"
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