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1篇 您的检索式：作者名="Giovanni Trezza"

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		题名	作者	年代	出处	被引量
	1	Minimal crystallographic descriptors of sorption properties in hypothetical MOFs and role in sequential learning optimization显示文摘We focus on gas sorption within metal-organic frameworks(MOFs)for energy applications and identify the minimal set of crystallographic descriptors underpinning the most important properties of MOFs for CO_(2)and H_(2)O.A comprehensive comparison of several sequential learning algorithms for MOFs properties optimization is performed and the role played by those descriptors is clarified.In energy transformations,thermodynamic limits of important figures of merit crucially depend on equilibrium properties in a wide range of sorbate coverage values,which is often only partially accessible,hence possibly preventing the computation of desired objective functions.We propose a fast procedure for optimizing specific energy in a closed sorption energy storage system with only access to a single water Henry coefficient value and to the specific surface area.We are thus able to identify hypothetical candidate MOFs that are predicted to outperform state-of-the-art water-sorbent pairs for thermal energy storage applications.	Giovanni Trezza Luca Bergamasco Matteo Fasano Eliodoro Chiavazzo	2022	npj Computational Materials2022，，1：	0

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