Investigating the error imbalance of large-scale machine learning potentials in catalysis-Reference-Cited by-同舟云学术

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Investigating the error imbalance of large-scale machine learning potentials in catalysis

Published:2024 Issue: Volume: Page:
ISSN:2044-4753
Container-title:Catalysis Science & Technology
language:en
Short-container-title:Catal. Sci. Technol.

Author:

Abdelmaqsoud Kareem¹^ORCID,Shuaibi Muhammed²,Kolluru Adeesh¹,Cheula Raffaele¹³,Kitchin John R.¹^ORCID

Affiliation:

1. Department of Chemical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, Pennsylvania 15213, USA

2. Meta Fundamental AI Research, Menlo Park, California 94025, USA

3. Department of Physics and Astronomy, Aarhus University, Nordre Ringgade 1, 8000 Aarhus C, Denmark

Abstract

Removing calculations with surface reconstructions reduces the MAEs of the MLPs.

Funder

HORIZON EUROPE Marie Sklodowska-Curie Actions

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/CY/D4CY00615A

Reference29 articles.

1. Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments

2. D. S.Sholl and J. A.Steckel , Density Functional Theory: A Practical Introduction , John Wiley & Sons , 2022

3. Structure−Property Correlation of CdSe Clusters Using Experimental Results and First-Principles DFT Calculations

4. Adsorption Behavior of Congo Red onto Barium-Doped ZnO Nanoparticles: Correlation between Experimental Results and DFT Calculations

5. Synthesis of 2-Acylindoles via Ag- and Cu-Catalyzed anti-Michael Hydroamination of β-(2-Aminophenyl)-α,β-ynones: Experimental Results and DFT Calculations

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