Accelerating the evaluation of crucial descriptors for catalyst screening <i>via</i> message passing neural network-Reference-Cited by-同舟云学术

Accelerating the evaluation of crucial descriptors for catalyst screening via message passing neural network

Published:2023 Issue:1 Volume:2 Page:59-68
ISSN:2635-098X
Container-title:Digital Discovery
language:en
Short-container-title:Digital Discovery

Author:

Doan Hieu A.¹^ORCID,Li Chenyang¹^ORCID,Ward Logan²^ORCID,Zhou Mingxia¹³,Curtiss Larry A.¹,S. Assary Rajeev¹^ORCID

Affiliation:

1. Materials Science Division, Argonne National Laboratory, Lemont, Illinois 60439, USA

2. Data Science and Learning Division, Argonne National Laboratory, Lemont, Illinois 60439, USA

3. State Key Laboratory of Heavy Oil Processing, China University of Petroleum Beijing, Beijing 102249, China

Abstract

Graph neural networks developed for adsorption energy prediction on molybdenum carbide catalysts provide a significant acceleration over density functional theory calculations.

Funder

Biomass Program

Argonne National Laboratory

U.S. Department of Energy

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2023/DD/D2DD00088A

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2. Experimental and Theoretical Insights into the Hydrogen-Efficient Direct Hydrodeoxygenation Mechanism of Phenol over Ru/TiO2

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4. Carbide and Nitride Overlayers on Early Transition Metal Surfaces: Preparation, Characterization, and Reactivities

5. Bifunctional Catalysts for Upgrading of Biomass-Derived Oxygenates: A Review

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